CRANFIELD UNIVERSITY REZA SALAMAT GAS PATH

CRANFIELD UNIVERSITY REZA SALAMAT GAS PATH
CRANFIELD UNIVERSITY
REZA SALAMAT
GAS PATH DIAGNOSTICS FOR COMPRESSORS
SCHOOL OF ENGINEERING
PhD THESIS
Supervisor: Dr. Yiguang Li
May 2012
CRANFIELD UNIVERSITY
SCHOOL OF ENGINEERING
PhD THESIS
2004-2012
REZA SALAMAT
Gas Path Diagnostics for Compressors
Supervisor: Dr. Yiguang Li
May 2012
© Cranfield University, 2012. All rights reserved. No part of this publication may be
reproduced without the written permission of the copyright holder.
Abstract
The use and application of compressors cannot be overemphasized in the
aeronautical and oil & gas industries. Yet research works in sufficient depth
has not been conducted previously to analyze their actual behaviour under
degraded or even new conditions in operation.
For the purpose of degradation modeling and simulation, a compressor model
was set up using thermodynamic equations and affinity laws representing the
characteristics of a clean compressor. HYSYS was used for degradation
modeling analysis by implanting known linear and nonlinear degradation
trends for an operating point and taking the compressor measurement
changes. It was then assumed the degradation levels are unknown and these
were established by applying the compressor health indices to the new
compressor map. A diagnostic method for compressors was developed where
the prediction in degradation levels were compared for diagnostic purposes.
By applying a unique “successive iteration method” to a real gas site
compressor data at various speeds, a compressor performance adaptation
technique has been developed in this thesis which maps out the actual
performance of the compressor shows the errors in performance prediction
has been reduced from 5-15% to a minimum. This performance adaptation
method allows the compressor performance map to be adapted against field
data of a compressor for a range of speeds. All data were corrected to a
common datum and GPA Indices were utilised for the evaluation of
confidence in the established method.
By observing the centrifugal compressor performance data from 2006 to 2010,
the actual compressor degradation was quantified and modeled by trending
techniques for diagnostic and prognostic purposes so that the operator can
plan ahead for maintenance by knowing an estimate for the actual health of
the compressor at any time.
The major conclusions are that the performance adaptation developed for the
site compressor and the diagnostic technique by data trending has been
successful. And estimation of degradation in health indicators (throughput,
pressure ratio and efficiency drops) by scaling the measurable parameters is
a useful tool for diagnostic purposes.
Acknowledgement
My sincere gratitude and appreciation go to my supervisor Dr. Li who helped and
supported me with his knowledge and vision throughout these valuable years.
I would also extend my thanks to Professor Pilidis, Professor Alexander
(Korakianitis), Ted Gresh, Dr. Ramsden and Dr. Mba for their assistance and support
as well as Dr. A. Zohrabian and Dr. J. Howard for their input.
Finally I salute my family for their patience, encouragement and support right from
the start in these hard but rewarding times.
I dedicate this thesis to my wife, Nayereh.
Table of Content
Abstract
Acknowledgement
List of Figures
List of Tables
Notation
iv
v
vii
BACKGROUND AND OVERVIEW
Chapter 1
Introduction
1
Chapter 2
Literature
5
2.1
2.1.1
2.1.1.1
2.1.1.2
2.2
Compressor operation, thermodynamic principles and efficiency types
Thermodynamic laws principles and turbomachinery efficiency definitions
A Review of Thermodynamics
Turbomachinery efficiency definitions
Compressor and Driver: Interface Losses and Environmental Effects on
Performance
2.3
Causes and Mechanisms of Performance Deterioration in Compressors
2.3.1 Compressor Performance Deterioration
2.3.2 Effect of Seals on Compressor Performance
2.3.3 Compressor Degradation, Degradation Modeling and Gas Path Analysis
2.3.4 Recent Advances in Compressor Design for Optimum Efficiency
2.4
Performance Simulation and Analysis
2.5
Diagnostic Methods for Compressors and Health Monitoring Techniques
2.5.1 Diagnostics by definition
2.5.2 The available diagnostic methods
2.5.2.1 Linear and Non-Linear GPA Model Based Diagnostic Methods
2.6
Continuous on-line monitoring for Health monitoring
2.6.1 Requirements for Compressor Condition Monitoring system
2.6.2 Benefits of online monitoring
2.6.2.1 Effect of OLM on Reliability-Availability-Maintainability (RAM)
5
10
11
17
21
22
22
24
28
38
41
43
43
43
51
57
59
60
60
DEVELOPMENT OF METHODS AND APPLICATION
Chapter 3
3.1
3.2
3.3
3.4
3.5
3.5.1
Compressor Degradation Modeling and Simulation
Introduction
Compressor Health Status Estimation
Data Measurements, Corrections and Uncertainty
A brief description of the simulation programme, HYSYS
Development of Performance Curves and Degradation Modeling
Affinity (Fan) Laws
I
64
64
66
68
71
73
77
Chapter 4
4.1
4.2
4.3
4.4
Use of dimensionless groups in compressor performance analysis
Performance Data Referring and Scaling
Generation of Actual Performance Curves for the site compressor by
Successive Iteration method
Trend analysis in compressor diagnostics
Chapter 5
5.1
5.2
5.3
A Novel Compressor Performance Adaptation
Technique
Developed methodology for Compressor Degradation
Estimation
Compressor Degradation Estimation
Compressor Health Estimation and GPA Index
Sensitivity Analysis
78
78
79
81
87
90
90
94
95
CASE STUDIES AND DATA ANALYSIS
Chapter 6
6.1
6.2
6.3
6.4
Generation of a compressor map for the clean compressor
Generation of a compressor map for the degraded compressor
Degradation Simulation Test Case
Application of Scale Factors for diagnostics
Chapter 7
7.1
7.2
7.3
7.4
7.5
7.6
7.7
Application of compressor degradation modeling
by simulation
Novel Scaling Method and Diagnostics Applied to
Site Compressor
Site Compressor Specifications
Performance Adaptation by Successive Iteration
Application of health estimation methodology
GPA Index Calculations
Establishment of Degradation Indices for the site compressor and
data trending
Health trend generation for the site compressor
Sensitivity Analysis
99
99
106
113
115
125
125
128
135
141
143
145
147
DISCUSSION & CONCLUSION
Chapter 8
8.1
8.2
8.3
8.4
8.5
Discussion
152
Use of HYSYS for Compressor Applications and Limitations
Degradation Modeling by Simulation and Health Estimation by Scaling
Performance Adaptation by Successive Iteration Method and Data Trending
Health Index and Diagnostics
Major Contributions of this thesis
II
152
152
153
154
158
8.6
Scope for Future Work
Chapter 9
158
Conclusions
159
References
163
Appendix A
167
Published Papers by the Author during Research Thesis
Appendix B
168
Compressor and Expander Calculations in HYSYS
Appendix C
187
Site compressor data
(I)
Site base cases for health estimation
(II)
Site compressor untreated data log since 2006
(III)
Typical Performance Curves supplied by OEM
Appendix D
188
Performance Adaptation by Successive Iteration – Calculation Details
III
List of Figures
Figure 1.
Figure 2.
Figure 3A.
Figure 3B.
Figure 3C.
Figure 3D.
Figure 3E.
Figure 3F.
Figure 3G.
Figure 3H.
Figure 3I.
Figure 3J.
Figure 3K.
Figure 3L.
Figure 3M.
Figure 4.
Figure 5.
Figure 6A.
Figure 6B.
Figure 6C.
Figure 7A.
Figure 7B.
Figure 7C.
Figure 7D.
Figure 7E.
Figure 7F.
Figure 7G.
Figure 7H.
Figure 8.
Figure 9.
Figure 10.
Figure 11A.
Figure 11B.
Figure 12.
Figure 13.
Figure 14.
Figure 15.
Figure 16A.
Figure 16B.
Figure 16C.
Figure 17.
Figure 18.
Figure 19.
Figure 20.
Figure 21.
Figure 22.
Figure 23A.
Figure 23B.
Figure 23C.
Figure 24A.
Figure 24B.
The normal profile of a compressor’s health with time
The cycle of rotating equipment on-line performance monitoring
The trend in compressor efficiency over the past 50 years
A typical compressor components used in the process industry
A close up to the internal parts of a centrifugal compressor
A gas turbine (LM2500+) showing the compressor part
Single stage compressor internal details
Multistage centrifugal compressor
The internals and the flow path in a centrifugal compressor
Velocity/pressure development in a centrifugal compressor
Energy Flow and Storage
Energy flows of fluid passing through a control volume
Isentropic flow into a Pitot tube
Applicability of the steady-flow energy equation
Types of turbomachine efficiencies
A compressor wash programme developed for a station
The effect of blade tip clearance on efficiency in compressors
Seal arrangement in centrifugal compressors for leakage
Location of shaft seals in the compressor
Gas seal component in the compressor
Dirt or polymer buildup in the diffuser passage of compressor
Impeller fouling of a centrifugal compressor
Eroded leading edge of a rotor blade of a compressor
Axial compressor blade fouling
Compressor blade corrosion
Damage to the casing coating
Blade damage and coating material wear
Fouling deposition and blade damage
The profile of performance deterioration of a fouled compressor
Centrifugal Compressor component failures
Blade temp. for various operating hours and degradation rates
CFD modeling of compressor’s moving parts
Effect of coating on dirt buildup in diffuser of compressor
The structure of rotating equipment diagnostic techniques
Fundamental concept of gas path diagnostics
Search space for compressor
Effects of number of measurements on diagnostics
Nonlinear Solution for compressor diagnostics
Principle summary and comparison of GP diagnostic methods
Summary of diagnostic methods based on model complexity
Online performance monitoring – Compressor Capacity Index
Contribution of gas turbine components to outages
Relation between MTBF, downtime and availability
Relation between availability and downtime
Plant availability taken at gas turbine availability of 90%
Clean and degraded compressor performance map
Trend analysis fall in efficiency with time against a base line
Plot of performance data by OEM to map the degradation
Plot of performance data by OEM to develop fouling factor curve
The effect of compressor degradation on PR performance
The effect of compressor degradation on Compressor Efficiency
IV
4
4
6
6
7
7
8
9
9
10
11
12
14
15
20
24
26
27
27
28
29
29
30
30
31
31
32
33
33
34
36
40
41
44
50
51
51
53
55
56
57
61
62
63
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67
88
88
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96
97
Figure 25.
Figure 26A.
Figure 26B.
Figure 26C.
Figure 26D.
Figure 26E.
Figure 27.
Figure 28.
Figure 29.
Figure 30A.
Figure 30B.
Figure 31.
Figure 32.
Figure 33.
Figure 34.
Figure 35.
Figure 36.
Figure 37.
Figure 38.
Figure 39.
Figure 40.
Figure 41.
Figure 42.
Figure 43.
Figure 44.
Calculation process for GPA Index
Compressor performance generation, Hp versus q
Compressor performance generation, Hp versus m
Compressor performance generation, PR versus m
Compressor performance generation, T2 versus m mass flow
Compressor performance generation, Gp versus m
Compressor performance at various degradation levels
Performance prediction for degraded compressor and the rerate
Compressor performance rerates for various degradation levels
The variations in Gp due to degradation (high speeds)
The variations in Gp due to degradation (low speeds)
Hp versus Flow for clean compressor in HYSYS
ηp versus Flow for clean compressor in HYSYS
The throughput and efficiency degradation trend over a year
HYSYS input and output for linear and non-linear degradation
Comparison of ‘actual’ measurements for degradation
Performance curve (T2 vertical/Horizontal) shift - linear deg case
Performance curve (T2 vertical/Horizontal) shift – Nonlin deg
Performance curve (Gp vertical/Horizontal) shift - linear deg
Performance curve (Gp vertical/Horizontal) shift - Nonlin deg
Compressor diagnostics based on T2 measurements
Compressor Diagnostics based on power measurements
The gas gathering and compression overview
A snapshot of the DCS (distributed control centre) room
Site compressor simulation model in HYSYS
98
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105
105
106
110
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111
112
112
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114
117
119
120
121
121
122
122
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133
List of Tables, Graphs and Charts
Table 1.
Table 2.
Table 3.
Table 4.
Table 5.
Table 6.
Table7.
Table 8.
Table 9.
Table 10A.
Table 10B.
Table 11.
Table 12.
Table 13.
Table 14.
Table 15.
Table 16.
Table 17.
Table 18.
Table 19.
Table 20.
Table 21.
Instrumentation set for compressor simulation
Maximum measurement noise
Variables requiring input in HYSYS compressor calculations
Available gas properties and basic rated performance data
Generation of speed curves based on Affinity Laws
Calculation of un-degraded compressor variables for all speeds
Degradation modeling of a compressor at 100% speed
Generation of degraded performance curves using affinity laws
Inlet condition in HYSYS for degradation investigation
HYSYS input and output for linear degradation
HYSYS input and output for non-linear degradation
HYSYS output ‘measurements’ six month after initial operation
Predicted (by scaling) and Actual degradations – T2 rise
Predicted (by scaling) and Actual degradations - Gp rise
Generated Pressure Ratio Scale Factors
Pressure Ratios Before and After Adaptation
Generated Polytropic Efficiency Scale Factors
Polytropic Efficiencies Before and After Adaptation
Generated Discharge Temp. Scale Factors
Discharge Temp Before and After Adaptation
GPA Indices for the site compressor
Establishments of degradation indices for the site compressor
V
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72
101
102
103
108
109
118
118
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120
123
123
130
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142
143
Graph 1.
Graph 2.
Graph 3.
Graph 4.
Graph 5.
Graph 6.
Graph 7.
Graph 8.
Graph 9.
Graph 10.
Graph 11.
Graph 12.
Graph 13.
Graph 14.
Graph 15.
Graph 16.
Graph 17.
Graph 18.
Graph 19.
Graph 20.
Graph 21.
Graph 22.
Performance scaling method matching the OEM and Site data
Performance Adaptation by Successive Iteration for PR
Performance Adaptation by Successive Iteration for ηp
PR prediction errors before and after adaptation technique
ηp prediction errors before and after adaptation technique
T2 prediction errors before and after adaptation technique
Referred values from extreme summer and extreme winter
HYSYS performance output of Hp vs flow for site compressor
HYSYS performance output of ηp versus flow for site compressor
Site compressor PR shift due to degradation
Site compressor Efficiency shift due to degradation
Derived PR degradation index versus time - site compressor
Derived ηp degradation index versus time-site compressor
Trend Analysis of the Site Compressor: ηp Degradation
Site compressor efficiency degradation modeling
Compressor m Deterioration Effect on PR
Compressor m Deterioration Effect on Hp
Compressor m Deterioration Effect on T2
Compressor m Deterioration Effect on Gp
Compressor ηp Deterioration Effect on PR
Compressor ηp (efficiency) Deterioration Effect on T2
Compressor ηp (efficiency) Deterioration Effect on Gp
Chart 1.
Flow Chart for establishing the actual site compressor
performance by scaling
A novel flow chart for establishing the actual site compressor
performance maps by successive iteration method
Procedure of Compressor Performance and Health Status
Chart 2.
Chart 3.
VI
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133
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140
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149
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150
150
151
85
86
98
Notation
Subscripts
0
ac
ad
c
clean
Cp
Dc
deg
ex
Fh
gw3
h
in
it
p
P
QL
Qi
ref
s/1
d/2
s
st
t0
th
Tb
ts
∆Tstage
z
Stanation conditions (static plus velocity terms)
Actual
Adiabatic
Compressor
Compressor at un-degraded conditions
Specific Heat Capacity, J/kg/K
Tip Diameter, meters
Compressor at degraded conditions
Exit
Hub to Tip ratio of 1st stage
Compressor external losses
heat transfer
Inlet
Isothermal
Polytropic
Power
Labyrinth seal flow
Impeller flow
Referred value of temperature or pressure
Compressor suction condition
Compressor discharge condition
Isentropic
Static conditions
Initial time
Thermal
Blade temperature
Stagnation-to-static efficiency
Average total temp rise between each compressor stage, K
Vertical direction above a datum
Symbols
a
A
A, B
b
ϒ
C
Cl
Cp
Cv
d
δ
Velocity of sound
Area available for flow
Constants of mass throughput deterioration
Blade height
Ratio of specific heats
Absolute flow velocity
Blade tip /Labyrinth clearance
Specific heat at constant pressure
Specific heat at constant volume
Derivative operator
Dimensionless component inlet pressure
VII
δ
Δ
∑
E
©
θ
g
gc
Gp/PWR
h
h
H
H-1
M
m/w
N
n
n
η/EEP
p
Q
Q
q
q
ρ
R
Rw
rC
s
S
t
T
u
ν
W
Small difference operator (thermodynamics)
Finite difference operator
Summation operator
General energy
Hypothetical specified conditions
Dimensionless component inlet temperature
Gravitational constant
Unit matching constant in Newton’s law
Absorbed power (gas power)
Head
Enthalpy per unit mass
Influence Coefficient Matrix (ICM)
Fault Coefficient Matrix (FCM)
Mach Number
mass flow rate through the compressor
Compressor RPM
Polytropic index
Number of kmoles of gas
Efficiency
Pressure
Volumetric flow rate
Heat transfer rate
Compressor volumetric Inlet Flow rate, actual
Heat per unit mass transferred
Gas Density
Universal Molar Gas Constant (8.314 kN.m/ kmole K)
Gas constant based on mass of gas
Compressor pressure ratio
Entropy per unit mass
Entropy
Time
Temperature
Internal thermal energy per unit mass
Volume per unit mass
Work or power transfer rate
An independent vector in GPA analysis
A dependent (measurable) vector in GPA analysis
z
z
Gas compressibility factor
Height above a datum
Acronyms
AE
CFD
Acoustic Emission
Computational Fluid Dynamics
VIII
CNDF
CNDS
CMT
DE
DOD
Compressor Non-dimensional Flow
ECT
Exit Cone Temperature
EEP
Polytropic Efficiency
Exhaust Gas Temperature
Foreign Object Damage
EGT
FOD
FOH
GPA
ISF
M
MMBtu
MW
N
NDE
OEM
OLM
OPM
P
PH
PR
PTFE
PW
RCM
SFEE
VIGV
VSV
Compressor Non-dimensional Speed
Condition Monitoring Techniques
Drive End
Domestic Object Damage
Total forced outage hours
Gas Path Analysis
Index of Compressor Sensitivity to Fouling
Gas Molecular Weight
Million British Thermal Units
Mega Watts energy
Compressor Speed (rpm)
Non Drive End
Original Equipment Manufacturer
On-line Monitoring
Online Performance Monitoring
Pressure
Period hours
Pressure Ratio
A type of Teflon
Power
Reliability Centred Maintenance
Steady-flow energy equation
Variable Inlet Guide Vane
Variable Stator Vane
IX
1. Introduction
Compressors raise the pressure of a gas stream by the required magnitude
and pass it along to the downstream equipment or facilities. They form a very
critical and expensive part of oil & gas and aerospace industries with
hundreds of billions of dollars invested so far globally in purchases, research
& development and these trends will continue into the foreseeable future.
Performance-based compressor diagnostics is a pertinent part of this
research and development.
Good performance of gas turbine axial compressors and process
compressors are of paramount importance in maintaining revenue for these
companies. Performance deterioration in gas turbine compressors during
operation will result running gas turbine at higher turbine temperatures for the
given power output. This will use the creep life more rapidly resulting in
increased number of overhauls and increased operating costs. Performance
deterioration in process compressors results in increased power demand for a
given compressor duty. Performance deterioration will therefore result in
increased maintenance activity and increased maintenance costs. Suitable
performance monitoring systems can prevent major deterioration and
expensive secondary damage. It would detect the problem and prevent this
damage resulting in saving in increased maintenance cost and lost operating
production. Most performance diagnostic systems available are unable to
determine faults accurately which give rise to these performance deterioration.
A successful diagnostic system must be able to detect the performance short
fall and highlight the components that are failing.
The performance data supplied by the original equipment manufacturer
(OEM) are for a specific set of defined inlet gas properties and environmental
conditions. Furthermore, these data are for a fleet of compressors rather than
the particular compressor supplied to the operator due to manufacturing
tolerances (PTC10- Ref. 45). Therefore seldom the compressor at site
behaves as promised by OEM. Hence once the compressor has moved to
site, the actual performance map of the particular compressor in operation
needs to be established as a base line performance based on real site
parameters and actual gas. Once into the operation, generation of
compressor health indices at selected intervals give snapshots of compressor
performance and compared with the established baseline performance gives
the compressor diagnostics. It is important to recognize that the base line
performance is not a constant expectation and need to be updated with time
to ensure maximum compressor performance availability and reduction of
operational costs.
1
In order to diagnose the compressor correctly, the model set up for the site
compressor must be highly accurate because, for the same environmental
condition, differences between expected and actual measurable parameters
such as pressure ratio are the lump sum of actual degradation and any errors
due to inaccurate modeling and parameter readings. This thesis describes
how measured data reading errors are made diminishingly small at site by
regular calibrations and noise reduction. This principally leaves out any
performance modeling errors that could be mistaken for degradation. When
an accurate performance model is set up for the compressor, the operator can
be certain that any deviation from the expected performance (i.e., base line) is
principally due to compressor degradation.
As operation time progresses, it is usually expected that degradation will also
propagate. In order to investigate the relationship between degradation and
time, the health parameters or degradation indices will be established at
various times during compressor operation. By developing the degradation or
health indices for the compressor at regular intervals it will provide snapshots
of the compressor real performance. Therefore, developing a method for
generating health indices for compressors by on line monitoring is best way
forward for the compressors. It will be noted that when evaluation intervals
are too small then the derived data will virtually overlap thus some suitable
time interval should be considered for performance evaluation. Figure 1
[developed from Ref 33] demonstrates the expected normal profile of a
compressor health and how it degrades with time. It demonstrates the
detection and quantification of degradation (degraded performance) at
present time in relation to a reference point (un-degraded performance) is
diagnostics. It also shows how the maintenance strategies fit in with the health
profile of the compressor. Figure 2 [Ref. 33] shows the overall cycle of
performance data analysis and maintenance advice based on diagnosis
derived from compressor performance simulation as a part of gas turbine.
The actual centrifugal compressor data at site from 2006 onwards are used by
the Author to develop a unique performance adaptation by “successive
iteration technique” that structures the actual performance of a clean
compressor for a range of speeds. For this purpose, the compressor health
parameters defined as pressure ratio, polytropic efficiency and throughput
obtained from 2006 onwards which are considered as degraded health
parameters are compared with the un-degraded or clean health parameters
obtained from site tests. The degradation indices are established at various
times and these parameter trends are recorded on graphs for validation,
analysis and development of diagnostic technique by the Author.
The main objectives of this thesis are to develop:
2
(i)
(ii)
An adaptation technique for a centrifugal compressor by successive
iteration and compressor diagnostics by the generation of
degradation indices on measurable or independent parameters
(pressure ratio, polytropic efficiency and mass throughput). The
proposed adaptation technique will be validated by evaluating errors
in predicted measurements before and after the performance
adaptation. The methodology for diagnostics will be substantiated
by the application Gas Path Analysis (GPA) Indices over several
years of site operational data.
A diagnostic technique to estimate the health of a centrifugal
compressor by taking the measured compressor parameters under
degraded condition and gauging them with those when the
compressor was un-degraded or clean. To accomplish this
objective, representative performance maps for a clean compressor
will be developed from thermodynamic and proportionality principles
and then fed to compressor simulation programme, HYSYS, where
the degraded compressor will be modeled and the performance
measurements analysed and scaled to estimate the degradation in
throughput, efficiency and pressure ratio.
In addition to the above developed techniques using site measurements, the
following are covered in this thesis:
1) A comprehensive literature review covering the qualitative principles of
thermodynamic laws and efficiency types in compressors, the
principles and advances in both centrifugal and axial compressor
degradations, mechanisms of degradation and the state of the art
compressor online monitoring and diagnostic techniques.
2) Introduction of the advanced compressor simulation package HYSYS
with its unique capability of allowing the operator to mimic the near
exact compressor measurable input/output under clean and degraded
conditions.
3) Sensitivity analysis on the effect of degradation on measurable
parameters.
The results are discussed in depth and conclusions made based on the
established results.
The diagnostics design tools developed in this thesis can be used as a
decision making tool to achieve minimum capital and operating costs for new
gas transmission system design or upgrade/expansion of an existing
transmission system, upstream and downstream of oil and gas facilities.
3
Figure 1. The normal profile of compressor’s health with time (developed
from Ref. 33)
Figure 2. The cycle of rotating equipment on-line performance monitoring
[Ref. 33]
4
2. Literature
2.1 Compressor Operation
Centrifugal and axial compressors respectively find extensive use in the oil &
gas and aerospace industries. Optimum design of compressors with highest
possible efficiencies has been the prime goal of compressor designers.
Reference to Figure 3A, thank to the superior design by the application of
CFD, better materials of construction and ever tighter clearances between
moving and stationery parts, the polytropic efficiencies of centrifugal
compressors has increased by nearly 20% over the past 5 decades from 75%
in 1950s to well over 88% in 2000 [Refs 36, 59]. High reliabilities and
availabilities are necessary to ensure compressors remain online to perform
their duty as expected and maintained as necessary. There are different
definitions of efficiency and types in compressor design and operation and
these should be well understood by the asset holder and the designer alike to
avoid unexpected performance or power demand when in operation. These
factors as well the thermodynamic principles are discussed in the following
subsection.
The components of a typical centrifugal process compressor are shown in
Figures 3B and 3C. Figure 3D shows a gas turbine with its axial compressor
component.
Reference to Figures 3B, 3F and 3G on centrifugal compressors, the
stationery part of the compressor is the diaphragm which forms a face of the
upstream diffuser, part of the return bend, all of the return channel and a face
of the downstream diffuser. Each impeller requires a diaphragm. After the gas
passes through the first impeller, it enters the diffuser, which systematically
reduces the velocity of the gas by increasing the flow area radially. With
reference to Figure 3H, the kinetic energy imparted to the gas by the impeller
is converted to the pressure rise as a consequence of this velocity reduction in
the diffuser. After leaving the diffuser, the gas enters the return bend which
guides the gas into the return channel and finally into the next impeller. The
return channel includes guide vanes which eliminate swirl in the gas caused
by rotation of the preceding impeller. After the gas passes through the last
diffuser, it enters the discharge volute which directs the gas to the discharge
nozzle and into the piping system.
5
Figure 3A. The increasing trend in compressor efficiency over the past 50
years [Ref. 59]
Figure 3B. A typical compressor components used in the process industry
[Ref. 27]
6
Figure 3C. A close up to the internal parts of a centrifugal (barrel type)
compressor [Ref. 60]
Figure 3D. A gas turbine (LM2500+) showing the compressor part at the
intake on the left [Ref. 61]
7
Figure 3E. Single stage compressor internal details: (1) discharge volute, (2)
casing, (3) diffuser, (4) impeller, (5) shaft, (6) seal, (7) radial bearings, (8)
trust or axial bearing, (9) coupling, (10) wear rings (protects the impeller), (11)
inlet guide vanes (IGVs) [Ref. 60]
8
Figure 3F. Multistage centrifugal compressor : (a) the inlet nozzle, (b) inlet
guide vanes, (c) impeller, (d) radial diffuser, (e) return channel, (f) collector
volute, and (g) discharge nozzle [Ref. 28]
Figure 3G. The internals and the flow path in a centrifugal compressor [Ref.
28]
9
Figure 3H. Velocity/pressure development in a centrifugal compressor [Ref.
28]
2.1.1 Thermodynamic laws principles and turbomachinery efficiency
definitions
Compressor design with high efficiency is a major source of fluid mechanics
challenge in turbomachinery. Prior to 1900s, the compressor efficiency was
less than 50%. A major pioneering work was by Auguste Rateau in 1902
when he designed a turbocompressor giving a pressure ratio of 1.5 at 12000
rpm giving an efficiency of 56% and in later years he continued to improve the
efficiency. In certain industries such as aircraft, each fraction of improvement
in efficiency translates into appreciable savings. In order to achieve these
improvements, a detailed analytical and experimental methods need to be
implemented or adopted on the analysis of flow and stress as well as the
vibration predictions.
A turbomachinery produces a change in enthalpy in a stream of fluid passing
through it and transfers work through a rotating shaft. Compressors absorb
shaft work from a “driver” defined as power turbine.
The content of this section is not exhaustive as it forms the foundation
knowledge for further readings and analysis [Ref. 67 provides detailed
description of thermodynamics and efficiencies upon which this section is
based]. The principles of thermodynamic laws defining the energy-enthalpy
relations are defined and various forms and types of machinery efficiencies
are clearly discussed to help the researcher choose an appropriate definition
of efficiency depending on the objective and type of machinery being
10
designed or used in order to avoid possible implications later on during
operation including a higher power demand than expected.
2.1.1.1 A Review of Thermodynamics
The first and second laws of thermodynamics are applied to the flow systems
of turbomachinery to derive the energy-enthalpy relations. However these
equations must be used with caution, for example, an isentropic (i.e.,
conditions under which the entropy remains essentially constant under
enthalpy change) flow function should not be employed to relate upstream to
downstream conditions in a frictional flow as in a frictional flow some of the
useful work is dissipated as unwanted heat and entropy can not be
considered constant. A review of first and second laws of thermodynamics are
covered in the following subsections.
First Law
This is the law of the conversation of energy. This can be stated as “The
energy passing into a given mass of material in a given time is equal to the
energy passing out of the material plus the energy stored within it”. The
referred material could be any form of molecule collection such as solid, liquid
or gas. In symbolic terms this could be referred as the following equation in a
positive displacement machinery where boundaries are well defined:
δq = d EA+ δw
δqin
d EA
Eq. 2.1
δwex
Figure 3I. Energy Flow and Storage [Ref. 67]
Where,
δq (=δqin) is the heat transferred per unit mass in the process (as a
convention, this is positive for heat transfer into the material);
d EA is the increase of energy level of the material inside the system “A”,
termed as “internal” energy; and,
δw (=δwex) is the work transferred per unit mass out of the material to the
surroundings (as a convention, this is positive for work done by the material
on the surroundings).
In turbomachinery application, however, Figure 3I is less applicable because
the material (working fluid) crosses control volume boundaries. Here the first
11
law of thermodynamics is adapted to the material-crossing-boundary known
as “flow” system. A space or “control volume” in which the flow process takes
place can be modeled and the fluid is analysed for mass and energy as it
moves in and out of the fixed boundaries of the control volume in a time
increment of δt.
With reference to Figure 3J, the control volume has a single inlet (Station 1)
and single outlet flow (station 2). The rate of heat transfer entering and exiting
the control volume are (δqin/dt) and (δqex/dt) respectively. Likewise, power
transfer entering and exiting the control volume are (δwin/dt) (δwout/dt). The
average fluid velocity is C perpendicular to area A.
Figure 3J. Energy flows for a steady stream of fluid passing through a control
volume [Ref. 67]
For flow-process control volumes it is just not the heat and work transfers
through the surfaces of control volume but the various categories of energy
that the material takes with it as it passes through the inlet and outlet of the
control volume. These principle categories include kinetic (the effect of high
velocity as material or fluid passes through the control volume). Another
category accounts for the displacement of mass through the port at pressure
p. Another category accounts for potential energy changes between ports due
to changes of elevation z with respect to an arbitrary datum level.
When the energy balances are applied to turbomachinery, the subscript st
(static) denotes property values that do not include the effects of kinetic or
potential energy terms. The subscript 0 (stagnation) denotes property values
that include effects of kinetic energy but not the effects of potential energy and
subscript T (total) denotes the property values that include the effects of
kinetic energy as well as the potential energy.
12
Perfect or semi-perfect gases have different static and stagnation
temperatures, pressures and densities (Tst, T0, pst, p0, ρst and ρ0) and the
mass flow rate is given by m= ρstAC.
From the principle of conservation of energy, at steady state the energy flows
in and out of the control volume in Figure 3J may be represented as:
Wex + Qex + m2 (ust,2 + pst,2 νst,2 + C22/2gc + gz2/gc)
2
= Win + Qin + m1 (ust,1 + pst,1 νst,1 + C1
/2gc + gz1/gc)
Eq. 2.2
Where,
W is work transferred
Q is heat transferred
C is the velocity of material
gc is unit matching constant in Newton’s law
ν is specific volume at static conditions (i.e., measured at the speed of the
flowing materials) and it is the reciprocal of ρst
u is the internal thermal energy per unit mass
pst is the static pressure
z is height above datum
Subscripts “in” and “ex” denote the inlet and exit to and from the control
volume, and,
1, 2 denote stations 1 and 2 on Figure 3J
The above equation is known as the “steady-flow energy equation”, (SFEE)
and it may be applied widely for the analysis of turbomachinery and
associated steady flow processes as the characteristic response time of flow
in turbomachinery are orders of magnitude shorter than the characteristic
response time of the overall transient encounter meaning that most flow
analysis cases may be completed by examining a series of steady-state
conditions in which there is no storage of mass or energy inside the control
volume.
The combined static properties of (ust + pstνst) can be given the name as
“static enthalpy” or hst and this may be defined as:
hst Ξ ust + pst νst
Eq. 2.3
In this equation, hst can be measured only from a defined datum state as the
internal thermal energy can only be measured from a defined datum whilst p
and ν are measurable quantities.
13
It may be noted that enthalpy in the above equation has the subscript “st” or
static (i.e., stream). This is because these are the conditions measured with
instruments that are “static” or stationery with respect to the fluid and this is
difficult task at high speed flows. The stagnation properties are measured by
probes that are shielded to reduce heat transfer by radiation and conduction
(Figure 3K). Thus reference to Figure 3J, for a horizontal stream (z2=z1) and
assuming that the flow velocity within the probe (C 2) is vanishingly small, then
at any flow station:
H0 Ξ hst + C2/2gc Eq. 2.4
Figure 3K. Isentropic flow into a Pitot tube
Thus difference between static and stagnation is established and appropriate
types of properties and should be used in flow analysis. When motion is slow,
for instance, the static properties equal the stagnation properties. In
thermodynamics relationships, stagnation conditions should be used
throughout.
In air and gas turbomachinery, changes of height have a negligible effect on
enthalpy change and the general form of steady-flow energy equation
(Equation 2.2) can be written as
(Qin + Win - Qex – Wex) / m = ∆12(h0) = h0,2 - h0,1
Eq. 2.5
Where,
∆ is finite difference operator and it is the property difference between outlet
station 2 and inlet station 1.
The steady-flow energy equation (SFEE) is conveniently simplified for
component of turbomachinery. For instance, in compressors where there is a
good thermal isolation, the heat transfer is negligible and the process
undergoes an adiabatic change (Qin = Qex = 0). In other turbomachinery
components there is considerable heat transfer but no work transfer (i.e.,
W=0), such as in combustor chambers and heat exchangers. In other
turbomachinery components such as nozzles and diffusers there is neither
heat nor work transfers. Figure 3L summarizes how Equation 2.2 is used in
components with gas, vapour or liquid and how the restricted SFEE of
14
Equation 2.5 is in used in components with gas, vapour or liquid in horizontal
flow.
Figure 3L. Applicability of the steady-flow energy equation (SFEE) [Ref. 67].
* denotes case to case basis: a compressor may incorporate intercooler or
turbine blade may incorporate cooling circuit.
Second Law of Thermodynamics
The second law of thermodynamics is stated in various forms. One common
form is “Heat cannot pass from cooler to a warmer body without the
expenditure of work”. This gives rise to the entity of “entropy” or degree of
disorder within the fluid structure which in general has the tendency to
increase. The general equation for entropy known as the Gibbs equation for a
simple substance in the absence of energy storage due to motion, gravity,
electricity, magnetism and capillary is:
T ds = du + p dv
Eq. 2.6
The Gibbs equation apply to any ideal or non ideal changes. For a simple
reversible process,T ds = δq and p dv = δw in the first law of thermodynamics
(Equation 2.1) and hence the equations are identical under simple reversible
conditions.
A perfect gas may be defined as the one which has constant values of C v
(specific heat capacity at constant volume) and Cp (specific heat capacity at
constant pressure). A semi-perfect gas may be defined as the one which C v
and Cp are functions of temperature (T) only. Both perfect and semi-perfect
gases obey the equation of state as follows:
pV = nRT = m Rw T
Eq. 2.7
15
Where, n is the number of kmoles of gas, R is the universal molar gas
constant (8.313 kJ/kmol/K), m is the mass flow rate of gas and R w is the gas
constant based on unit mass of the gas under analysis (0.287 kJ/kg/K for Air).
The above equation of state dictates that the specific internal energy and
specific enthalpy of perfect and semi-perfect gases also obey the following
relations:
dh = Cp dT
du = Cv dT
Eq. 2.8
Eq. 2.9
Where the ratio of specific heat capacities, ϒ, is defined as
ϒΞ Cp/Cv = Cp/(Cp-R)
Eq. 2.10
By the application of 2nd law of thermodynamics (Eq. 2.6) and equation of
state (Eq. 2.7) one may obtain the velocity of small pressure wave
propagation as:
Eq. 2.11
Where,
ast is the actual velocity of sound (static) and T st is appropriately the static
temperature
For a perfect gas the entropy change between any two states 1 and 2 may be
given by
Eq. 2.12
For a semi-perfect gas the entropy change can be calculated by above with a
suitably averaged value of Cp over the temperature range between T 2 and T1.
For an isentropic process where entropy stays the same, S 2 – S1 = 0, and the
above equation becomes:
Eq. 2.13
Many flow issues in the design of turbomachinery involve the specification of
the stagnation pressure and temperature, p0 and T0 and either the absolute
16
velocity C or the mass flowrate m and characteristic area A. If the Mach
number can be found, all the actual and static properties are immediately
calculable. Applying Eq. 2.4 to an adiabatic system for a perfect gas the
following ratio is obtained:
Eq. 2.14
And, substituting for ast in equation Eq. 2.11, and defining Mach number M
as M= C/ast , will yield the following equation which is the fundamental
equation for one-dimensional compressible flow based on first law and
adiabatic (no heat transfer) process:
Eq. 2.15
Combining equation 2.15 with equation 2.13 will yield the following equations
for isentropic flows in terms of stagnation to static ratios:
Eq. 2.16
Eq. 2.17
2.1.1.2 Turbomachinery efficiency definitions
The overall energy efficiency of turbomachines compares the actual work
transfer with that which would occur in an ideal process. If the machine is
using energy such as the compressor, the work transfer in an ideal process is
in the numerator. If the machine is energy producing, then the ideal process
work transfer is in the denominator.
17
Efficiencies must be defined with sufficient precision and seldom this is the
case. Hence here these are well defined.
If the efficiency of a compressor is defined as follows: [power transfer in ideal
process from an inlet stagnation pressure and temperature to a defined outlet
stagnation pressure] / [the actual compressor power], or,
ηc = W in, ie/W in, ac
Eq. 2.18
Then, (i) The ideal process must be identified where the principle choices are
isentropic, polytropic and isothermal, (ii) The inlet and specifically the outlet
plane must be identified, (iii) The outlet pressure should be stated whether it is
actual or static, (iv) The actual work transfer include or exclude the losses in
seal friction, bearings and disk.
Each of the above definitions is described below.
Ideal Processes
The two principle ideal processes used are isentropic for adiabatic processes
and the isothermal for gas compression when intercooling is employed.
The isentropic efficiency for a compressor (η s, c) is best defined as:
Eq. 2.19
Where,
= Enthalpy after an isentropic process from h0, in and T0, in to
rc = Compression ratio
It is important to note that the term “R/Cp” is used rather than “ϒ” because the
latter leads the user to find a value of C p appropriate for temperature but to
take ϒ as constant with temperature which leads to errors and inconsistencies
in precise calculations. The equation 2.19 above in terms of temperature
differences and rc are exact for perfect gases and approximation for semiperfect gases.
The isothermal power required for perfect and semi-perfect gases is obtained
by Equation 2.20 below:
18
Eq. 2.20
The actual work transfer in the process is given by the steady-flow energy
equation (SFEE) as:
Eq. 2.21
The above equation is a general and exact expression for the actual work
transfer where no significant change in fluid height is experienced meaning
the work transfer between the fluid and the machine’s rotor(s), stator(s) and
ducts. In many evaluations of energy efficiencies this equation (Eq. 2.21) is
used and it gives the measure of the quality of the aerodynamic and
thermodynamic design. The “external” energy losses resulting from friction in
bearings and labyrinths must be added or subtracted from the process
energy.
Turbomachines are generally adiabatic (Q in = Qex = 0). In certain cases where
intercooling is involved, heat transfer is significant.
Figure 3M shows the general types of turbomachine efficiencies. W g3 indicate
“group 3” or external losses (i.e., shaft power losses through friction, the
energy from which is dissipated away from the working fluid such as friction
losses in bearings and seals) and these power losses appear as increases in
compressor power requirements.
19
Figure 3M. Types of turbomachine efficiencies
Polytropic Efficiency
The isentropic efficiency is a function of pressure ratio and losses. Isentropic
efficiency has a serious disadvantage if it is used as a measure of the quality
of the aerodynamic design, or as a measure of losses, in an adiabatic
machine. To avoid the influence of pressure ratio on isentropic efficiency, the
limiting value of the isentropic efficiency for a given polytropic process can be
used as the pressure ratio approaches unity. This is known as the “polytropic”
efficiency and for compressors this is represented as:
ηp Ξ (ηs)r
1.0
= (R/Cp)/((n-1)/n))
Eq. 2.22
Where n=polytropic index (a unique number advised by the manufacturer
based on fluid composition and properties). For perfect gases Eq. 2.22 leads
to the useful equation of the following:
Eq. 2.23
Equation 2.23 enables the adiabatic work to be obtained directly from
pressure ratio. However, which value of p ®0, ex to use is a subject of debate
and depends on the objective(s). When p ®0, 2 is defined as static pressure, the
efficiency obtained is termed a “stagnation-to-static” efficiency, η0s. When p®0,2
20
is a stagnation pressure, the efficiency obtained is “stagnation to stagnation”,
η00. Thus there is no unique “one” definition of efficiency but whichever is
chosen should be well identified and defined [Ref. 67]. The type of efficiency
used is application dependent. For instance, the appropriate definition of p®0,2
for the efficiency of a compressor in a turbojet engine is the static pressure at
the end of compressor diffuser at the boundary between the compressor and
the combustor because the dynamic pressure at the point cannot be used by
the combustor. If stagnation pressure is used, undesirable consequences
would result since actual losses would be increased if the diffuser were
eliminated. Whereas, in the case of the turbine of a turbojet engine, the
appropriate p®0,2 to use in the turbine-efficiency definition is the stagnation
pressure upstream of the propulsion nozzle that produces the jet. It is to be
noted that the actual work is unaffected by the choice of useful outlet
pressure, p®0,2 and only the ideal work is affected since from the SFEE the
actual (internal) work is the difference in stagnation enthalpy from inlet to
outlet and this work remains constant at (h 0, ex – h0, in) regardless of how the
outlet pressure is defined.
In removing the effect of pressure ratio, the polytropic efficiency is useful in
the sense it enables machines of different pressure ratios to be reliably
compared, regardless of number of stages.
The relationship between polytropic and isentropic efficiencies for perfect
gases may be defined as in Eq. 2,24 below and therefore knowing one type of
efficiency, the other may be calculated:
Eq. 2.24
2.2 Compressor and Driver: Interface Losses and Environmental Effects
on Performance
On oil and gas projects, the process discipline group is usually the first to
commence plant design and often the process is designed by the process
engineer without sufficient consideration for compression equipment. At a
later stage, when mechanical engineers come onboard, they frequently find
that they have to search the market for a compression unit that satisfies the
process requirements as at this stage it is virtually impossible to change the
process conditions. It is of paramount importance, therefore, that the
21
engineers across the disciplines know what factors affect the compressor
power demand and the profile of power requirement during the life cycle of the
compressor such that suitable gas turbine drivers are supplied from the
beginning to keep the availability and reliability at high levels.
The conditions of environment, process and fluid properties entering the
compressor vary with time over the life cycle of a project and as such, it must
be ensured that at the very early stage all these variations and their effects on
compressor performance as well as the gas turbine driving the compressor
are accounted for, so that critical conditions such as power supply shortfall
from the gas turbine(s) driving the compressor(s) do not happen at any time
during the entire expected life time of the compressor(s) thus keeping the
compressor online in operation at the highest availability.
The power demand profile from the compressor is evaluated taking into
account all the environmental losses and process changes as well as
identification of sources and quantification for supply power losses, then
finding a suitable driver. Once the power demand profile over the entire life
expectancy is determined, suitable cascade of choices for the gas turbine
driver shall be the next step. Determination of a suitable gas turbine driver is
an important task which should be determined by the compressor operator
considering all the losses throughout the life cycle of the project.
2.3 Causes and Mechanisms of Performance Deterioration in
Compressors
2.3.1 Compressor performance deterioration
In an axial compressor as a part of the gas turbine, the performance
deterioration of the compressor results from the deterioration of one or more
of the engine sub-components. The deterioration of these sub-components
results in changes in their characteristics. The interaction of these deteriorated
component characteristics lead to a change in compressor shaft power
demand, efficiency and engine measurable parameters such as pressure,
temperatures speeds and flows. Likewise, in the centrifugal compressors used
in the process industries, the performance deteriorates due degradation
resulting in changes in its performance maps.
Many factors affect the compressor performance and include the following:





Compressor Fouling
Variable Inlet Guide Vane (VIGV) and Variable Stator Vane (VSV)
problems for axial type
Blade Tip Rubs
Vibrations in the shaft, indicator of rotor problems
Tip seal and labyrinth seal Wear & Damage
22



Foreign Object Damage (FOD) & Domestic Object Damage (DOD)
Erosion
Corrosion
The effect of rubs is discussed separately in the following section. Fouling is
the most common cause of performance deterioration. Even with the best
filtration system, dirt, salt (offshore), sand etc. will get through the filtration
system and deposit on the compressor blading. Compressor fouling will
increase surface roughness as well as reducing the flow area (capacity) and
efficiency. The first effect will be lowering the performance curves and the
second will shift the curves to the left. Vibration is usually an indication of
compressor deterioration rather than the cause. However, vibration can also
be due to operating in region of choke of the compressor, where vibrations
may increase as the compressor becomes fouled. Axial displacement is also
another key indication of performance deterioration of the compressor thrust
piston condition.
There are two kinds of turbo machinery performance deterioration:
recoverable and non recoverable. The performance degradation associated
with widening clearances between the moving and stationery parts of the
compressor are “non-recoverable”. The only remedy for non-recoverable
degradation is an overhaul.
Compressor fouling is a “recoverable”
degradation in that it can be alleviated by periodic on-line and/or off-line
compressor washing. Off-line washing may fully recover the performance loss
of compressor. It is essential to develop maintenance schedules based on the
characteristics of the compressor (see Figures 1 and 4) or its operating
environment and/or cycle in order to balance the maintenance costs with lost
revenues resulting from the loss of production. Compressor washing mitigates
compressor fouling and the shift in these running lines may be used to
determine compressor washing frequency. Optimizing compressor washes is
more complex and has to take into account many factors such as downtime
for washing, costs and revenue [Ref 41], see Figure 4 developed for a
demonstration of optimized wash periods based on running hours for the
compressor of a gas turbine. Arebi [Ref 37] has discussed the compressor
washing in detail and Jordal [Ref 25] and Hovland [Ref 46] have arrived at
formulas for optimum compressor washing intervals.
23
Figure 4. A compressor wash programme developed for a station [Ref 37]
In the proceeding subsections, the significant causes and forms of
compressor performance deterioration are discussed and these are tip seal
damage, labyrinth seal wear and/or damage, fouling due to adherence of dirt
particles on the surface of materials, erosion which is the abrasive removal of
compressor coatings and materials and corrosion which is the removal of
compressor materials due to chemical reaction between the gas medium and
compressor components.
2.3.2 Effect of Seals on Compressor Performance
Due to the pressure rise across successive compression stages seals are
required at the impeller eye and shaft to prevent back flow from discharge to
the inlet end of the casing. Therefore the conditions of these seals directly
affect the compressor performance. These seals are normally labyrinth seals
and if they are clogged with dirt and worn with increased clearances allow
larger leaks (see Figures 6A and 7A). This will affect operation and also the
compressor efficiency. Calculations and field performance data have shown
that wiped interstage seal reduce efficiency as much as 7% or more.
In axial compressors the labyrinth seal reduces internal leakage between the
discharge and suction side of the compressors and turbines. Damage to these
seals increases the internal leakage and result in reduced compressor and
turbine performance. Labyrinth seal damage reduces component efficiency
rather than flow capacity.
24
The fall in efficiency due to rob and wear is not a recoverable process. The
rub and wear causes a direct drop in efficiency and pressure ratio curves; it
does not shift the curves from left to right like fouling. Having said that,
however, if the tip clearances open up in a open face impeller, more flow will
migrate causing an increase in viscous losses but it will also impact the
loading on the impeller blades. This could be seen as reduction in capacity of
the stage as well as reduction in the range of operation (stall point moves). It
should also be noted that a high degree of varying flow angle coming out of
the impeller will negatively impact diffuser vane performance.
Figures 6A-6C show the position of seals within the compressor.
The effect of blade tip clearance in centrifugal as well as axial compressors is
shown on Figure 5. For an open centrifugal impeller the efficiency loss is
about one-third of a point for each percent of tip clearance ratio at the impeller
outer diameter.
For axial compressor the clearance between the compressor rotor and the
casing must be kept small in order to reduce secondary loss. Increase in
compressor clearance reduces compressor flow capacity and efficiency.
Increase in clearance normally occurs when running an engine with high
vibration. Erosion also increases the rotor tip clearance. Significant increase in
axial compressor clearance can result in the surging of the compressor. The
compressor efficiency is reduced by 2 percentage points for each percent of
tip clearance ratio [Ref. 28].
For a closed centrifugal impeller with labyrinth seals at the eye, the efficiency
loss is about one percentage point for each percentage increase in impeller
flow as a result of the leak (see Figure 5).
25
Figure 5. The effect of blade tip clearance on efficiency in centrifugal as well
as axial compressors [Refs 50, 51]
Seals also avoid process gas inside the casing leak outboard. A typical seal
arrangement to accomplish this objective is shown on Figure 6A. The location
of shaft seals is clearly shown on Figures 6B and 6C.
26
Figure 6A. Typical seal arrangement in centrifugal compressors for leakage
avoidance [Ref.62]
Figure 6B. Location of shaft seals in the compressor [Ref.62]
27
Figure 6C.Gas seal component in the compressor [Ref.62]
2.3.3 Compressor degradation, degradation modeling and Gas Path
Analysis
With reference to Figures 7A-7H, a compressor performance falls with time or
degrades via fouling (adherence of particles) and erosion (abrasive removal of
compressor materials and hence the increase of surface roughness)
mechanisms. The removal of compressor materials by chemical reaction(s)
between it and the transported gas will result in corrosion in which the material
surface also ends up in increased roughness and ultimately material failure.
In compressors the degradation causes an increase in tip clearance, change
in airfoil geometry and quality. The first two are irrecoverable (part
replacement is necessary) and the 3 rd type is partially recoverable through
compressor washing. The effect of on-line/offline washing on the recovery of
compressor performance is shown in Figure 8 [Ref. 57]. It is seen here that
effective washing brings back a noticeable part of the lost performance due to
degradation.
28
Figure 7A. Dirt or polymer buildup in the diffuser passage of the centrifugal
compressor [Ref 28]
Figure 7B. Impeller fouling of a centrifugal compressor [Ref.60]
29
Figure 7C. Eroded leading edge of a rotor blade of a first stage of a
compressor. The picture shows the mid-span of a first stage compressor
(axial) rotor [Ref 16]
Figure 7D. Axial compressor blade fouling [Ref 37]
30
Figure 7E. Compressor blade corrosion [Ref 37]
Figure 7F. Damage to the casing coating [Ref. 60]
31
Figure 7G. Blade damage and coating material wear note tip of the blades[Ref. 60]
Figure 7H. Fouling deposition and blade damage [Ref. 60]
32
Figure 8. The profile of performance deterioration of a fouled compressor with
and without treatment [Ref. 57]
The effect of material degradation on rotating equipment availability is
phenomenal.
Around 70% of turbomachinery equipment failure is due to surface
degradation out of which about 40% is due to wear and the balance is due to
corrosion. For the axial compressors of gas turbines, solid particle erosion
(SPE) and therefore the degradation of moving blades is the most commonly
occurred fault [Refs 20, 21] due to air intake composition and the fall in the
performance of air filters with time. For process gas centrifugal compressors
the cause of degradation is erosion primarily due to presence of foreign
particles such as burnt lube oil, seals oil leaks, mechanical impurities from
water evaporation cooling systems (heat exchangers), salt and heavy
hydrocarbons within the process gas. FOD and DOD result in the change in
the flow paths through compressors and turbines. They also alter the surface
finish. These effects can result in a reduction in both flow capacity and
efficiency. The component failures within the process gas centrifugal
compressors (based on a survey of 500 olefin plants [Ref 27]) is shown in
Figure 9 where impeller problems tops the chart with 32% of the reported
problems followed by fouling (28%), case leaks (14%) and seals (10%).
33
Figure 9. Centrifugal Compressor component failures (based on industry
survey of 500 olefin plants between 1994 and 1999) [Ref 27]
A case study found that after 24000 hours of operation, 2-6% performance
degradation would occur assuming no degraded parts are replaced and if
replaced the expected performance degradation would be 1-1.5% [Ref 23].
Case studies carried out by others [Ref 24] have found fouling resulted in 5%
reduction in throughput along with 2.5% reduction in efficiency and a 10% rise
in shaft power demand. The effect of roughness (due to erosion) on
measurable or dependent parameters is not uniform. Erosion effect on
efficiency reduction is more pronounced than on pressure ratio; 2% versus
0.5% based on a case history [Ref 18] and on another axial type compressor
engine it was found that 6 stages fouling caused 4.5% reduction in mass
throughput, 4% reduction in pressure ratio a 2% reduction in efficiency [Ref
24].
It is due to the above significant effects of fouling on performance and
therefore economics that since 1970s and 80s many researchers have
attempted to model compressor fouling and its effects on performance.
In the late 80s, Aker and Saravanamuttoo [Ref 15] demonstrated that a linear
or stage stacking fouling model gives an accurate representation of the fouling
process up to approximately half way through the compressor. It is likely that
34
fouling is not a linear process for the entire compressor stages; it is most
sever right after compressor washing, and thereafter it slows down and
eventually it stabilizes [Ref 25]. For the linear part, the compressor fouling can
be modeled as a “Linear Progressive Model” [Refs 15, 24, 26]. This means
the 1st interval corresponds to the fouling of the 1 st stage and the decrease in
head and efficiency equals ∆h and ∆η; the 2 nd stage causes further decrease
in head and efficiency of the 1st stage by 2∆h and 2∆η and so on up to the 6th
stage.
In mid 90s, based on degradation modeling aided by field data, Tarabrin et al
[Ref 24] developed an index of compressor sensitivity due to fouling (ISF) that
show strong dependency on tip diameter as follows:
ISF=10-6. m. Cp. ∆Tstage /((1-F2h). D3c))
Where,
ISF= Index of Compressor Sensitivity to Fouling
m= Compressor mass throughput, kg/sec
Cp=Specific Heat Capacity, J/kg. K
∆Tstage =Average total temperature rise between each compressor stage, K
Fh =Hub to Tip ratio of 1st stage
Dc=Tip Diameter, meters
The above equation shows that a smaller axial type compressor engine is
more susceptible to fouling than the larger one. High head stages of the
compressor are more sensitive to fouling than the low head.
In 2000, Kurz and Brun [Ref 16] published their works on the changes in
clearances and seal geometries due to degradation and changes in blade
surfaces and aerodynamics due to erosion or fouling and have included a
methodology to simulate the effects of gas turbine engine and driven
equipment degradation and have proposed ‘linear deviation factors’ by
comparing the test data to rotating equipment maps at various stages of
degradation.
Based on analytical works in 2002, Jordal and Asadi [Ref 25] demonstrated
how blade materials and compressor fouling rate affect the blade life time and
based on thermodynamic approach they arrived at determining an optimum
time for compressor washing intervals. It is stated that compressor fouling is
most sever after compressor washing, thereafter it slows down and eventually
stabilizes and present the degradation in terms of mass throughput as follows:
(mclean - m deg)/ m clean = A [1-exp(Bt)]
Where,
35
m=mass throughput and suffices “clean” and “deg” refer to clean and
degraded conditions respectively
A= The mass flow rate deterioration at which fouling stabilizes (typically set at
5% or 0.05)
B= A constant and it determines the rate of fouling. Three different rates of
fouling were investigated. The results for a 4% reduction in mass throughput
gives a value for B as follows:
B=3.22 x 10-3 at 500 hours of operation
B=1.61 x 10-3 at 1000 hours of operation
B=1.07 x 10-3 at 1500 hours of operation
The representation of degradation in the equation above is similar to the
works carried out by Tarabrin [Ref 24]. The reduction in polytropic efficiency
was taken to be proportional to the inlet mass throughput. Jordal [Ref 25] also
studied the effect of blade material and working temperature on the life of the
blade and this is shown on Figure 10 below showing a higher temperature rise
for the blade of a compressor that has a higher degradation rate.
Figure 10. Increase in axial compressor engine model uniform blade
temperature Tb as a function of operating hours for various degradation rates.
No fouling at to=0 [Ref 25]
In same year as Jordal’s works above in 2002, Kubiak et al [Ref 21] modeled
the effect of increased clearances, due to wear degradation, on power losses
and it was found to be significant. An enlargement of compressor clearances
36
by 50% (measurements made during overhaul), diminished the power by
about 5%.
More recently in 2009, Li [Ref 14] has developed a health status estimation
method by defining compressor degradation as indices pertinent to flow
capacity, pressure ratio and efficiency. Recognizing degradation causes a
shift of the whole performance maps, the index of each the aforementioned
independent parameter is the ratio of degraded value of the parameter to that
of clean performance. Thus the current health of a compressor can be
estimated at any time.
Also in recent times in 2010, Morini et al [Ref 35] has studied the effect of
blade deterioration on performance maps, applying stage-by-stage
simulations as well as applying scale factors that represented shift of
compressor behaviour in deteriorated conditions. It was found that the flow
rate decrease due to compressor fouling is proportional to, and varies linearly,
with the number of stages affected reaffirming the result of earlier works on
the degradation mechanism by Saravanamuttoo [Refs 15, 26] and Tarabrin
[Ref 24] described earlier in this section. It was also found that scaled
mapping gives a very accurate estimation with a RMS error less than 1% at
areas close to design or test points.
Rotor blade erosion and geometry deterioration are strongly dependent on
particle concentration and duration of ingestion. The patterns of erosion
effects on blades depend on quantity of throughput relative to its design rate.
Gheaiet has developed formulas giving approximate life time of blades [Ref
22] in the axial compressor due to sand ingestion which is a common problem
due to filter inefficiency at the air intake.
Mechanical degradation of compressors parts has different symptoms. Seal’s
wearing out (clearance) is associated with sever rotor vibrations. The
symptoms of blade degradation are not same as seals wearing out.
Degradation causes visible or measurable changes in performance such as
reduction in pressure ratio, compressor efficiency and mass throughput. In all
these cases, the initial effect is that the compressor discharge temperature
goes up and it has to work harder to meet the set discharge pressure and
mass throughput. This in turn will lead to increase in fuel or electric demand
driving the compressor. To get an appreciation on the scale of economics
involved here, for a single 40 MW gas turbine machine at a rated heat rate of
10,000 Btu/kW.hr at an average load level of 80% operating throughout the
year, an increase of 1% in fuel at a cost of $8/MMBtu will lead to an extra cost
of $200,000 per year [Ref. 19]. The ultimate effect of compressor degradation
is significant deviation from acceptable performance specification and
component failure. It is ironic at the same time, however, that compressor
37
failures have reduced in the past decade due to better technology and online
health monitoring but the frequency of failures is still significant.
The degradation cannot be measured. However, the degraded performance
produces deviations in measurable parameters such as discharge pressure
and temperature, mass throughput etc. Gas Path Analysis (GPA) is a
technique for analyzing deterioration effects of a compressor. GPA provides
the means for assessing the independent (non-measurable) compressor
parameter deviations by establishing a relationship between them and the
dependent (measurable) parameters. Based on the changes from a new and
clean status (base line) a compressor gas path diagnosis is issued. In the
proceeding sections compressor diagnostics and techniques are discussed in
detail.
As it will be seen in the later sections, compressor degradation causes the
performance curves move downward and to the left due to polymer build up,
dirt, corrosion, increased seal wear and greater restriction to process flow in
general due to fouling. The efficiency is reduced because of increased
frictional losses and/or increased internal recirculation (wear, rubbings,
clearances, etc). As a result of compressor degradation, surge margins are
reduced meaning the compressor will go into surge mode at higher flow rates
than the rated OEM values for a clean compressor causing potentially
extensive damage to the compressor. Thus lower surge margins are
indicators of compressor fouling. References 8, 9, 15, 18, 34-38, 50 and 51
provide further in-depth theory and survey on compressor fouling, tip
clearance and compressor performance deterioration and modeling.
In axial compressors performance deterioration results in increase in
operating temperature which in turn may result in an increase in operating
speed and higher firing temperature which results in accelerated creep life
usage.
2.3.4 Recent advances in compressor design for optimum efficiency
In the preceding section it was realized that up to 70% of turbomachinery
equipment failure is due to surface degradation and in case of centrifugal
compressors in the process industry impeller problems due to rubs and
erosion tops the chart of reported problems with well over 30% of reported
failures followed by compressor fouling. Also in the preceding section, the
effect of fouling and erosion/corrosion was emphasized on the performance
by numbers that for a case, fouling had resulted in 5% reduction in throughput
along with 2.5% reduction in efficiency and a 10% rise in shaft power demand.
It is for the above reasons that much research and development has been
concentrated on minimizing rubs and minimizing possibility of foreign particle
sedimentation on the compressor surfaces by the application of special
38
coatings and materials that have specific favourable properties such as low
coefficient of expansion.
Certain and some finite controlled leakages must exist in a process gas
centrifugal compressor (see Figure 6A) such that the compressed gas, which
if released will be harmful to operators and environment, is in isolation and do
not travel outside the casing into the ambient. Furthermore materials generally
expand outwardly due to temperature increase or centrifugal forces. This
means that some clearances must exist between the moving and stationery
parts to allow for the mentioned physical changes. Too wide a gap, though,
increases leakages and pressure losses leading to reduced compressor
efficiency and lower throughput capacity.
Recent advances for improvements include use of superior materials and
coatings in the various parts of the compressor that are:
1) Resistant to erosion/corrosion,
2) Able to withstand higher stresses without physical damage or deformation,
3) Limited coefficient of expansion of the casing and impellers
Other areas of advances include superior and abradable seals in the impeller
eye and shaft seal areas.
The limited expansion of the casing is accomplished by a superior material so
that their expansion is very finite due to temperature increase and therefore
do not rub against the moving parts leading to tighter design operating
clearances and minimal efficiency effects after a seal rub.
The application wear rings shroud the exposed surface of the shaft to the
corrosive gas hence disallowing continuous contact between the process gas
and the shaft in corrosive environments and fluids resulting in the shaft being
far less prone to corrosion and instead the wear rings are easily replaced
when required and these rings are special corrosion/erosion resistant
materials.
Recent developments to further improve compressor efficiency include the full
application of computational fluid dynamics (CFD) to ensure full, gentle and
radiused gas flow paths control minimizing gas velocity changes and flow
separation for the compressor stage as well as for the auxiliary flow paths
such as the inlet and discharge nozzles and volute design which are all part of
compressor rotor dynamics and aerodynamics design and analysis. Figure
11A gives an example of CFD modeling where the problem areas are
identified in the original design (red spots on the impeller) and the new design
where the flow is smooth and stresses are reduced.
Figure 7A shows the long term effect of coated and non-coated surfaces on
dirt/polymer buildup in diffuser passage of a centrifugal compressor. The type
39
of material and coatings used are being updated regularly with superior
properties and the clearances are reduced to absolute minimal to minimize
losses and increase the compressor efficiency. Figure 11B shows the effect
on compressor efficiency due to the coating used. A material composition
applied by a specific manufacturer [Ref. 55] consists of three different layers
and these are from inside to outside (the latter is in contact with the process
gas):



Chromate/phosphate coating with aluminum
Polymeric coating
Coating layer containing PTFE
Together these layers achieve good bonding, provide corrosion protection and
achieve a very smooth surface of the rotor and prevent fouling from sticking to
the rotor. Coating can be applied on rotor as well as on stationary parts.
Maximum continuous operating temperature for the coating is approx. 250°C
[Ref. 55].
The application of advanced tools for health monitoring, effective shift of
maintenance strategy to predictive or conditioned based maintenance,
innovative noise reduction for the health and accuracy of measuring devices,
the application of “integrally geared” centrifugal compressors featuring multishaft arrangements with different speeds, new and extended software
applications in aerodynamic, structural, rotordynamic and manufacturing are
among the other recent advances in compressor design for optimum
performance and power consumption.
40
Figure 11A. Computational Fluid Dynamic (CFD) modeling of compressor’s
moving parts showing how stresses are reduced during design phase [Ref
54].
Figure 11B. Effect of coated and non-coated surfaces on dirt/polymer buildup
in diffuser passage of a centrifugal compressor [Ref 28]
2.4 Performance Simulation and Analysis
The performance of a compressor is best modeled by simulation. In order to
come up with the correct diagnostic results, the prediction of performance by
the simulated model must be highly accurate so that any simulation errors are
not comingled or confused with performance deterioration. Once an accurate
model of the compressor is built then the model could be impregnated
(stimuli) with a known fault and the resultant changes or the responses (fault
signature) are analysed. These patterns compared with the real compressor
but unknown fault will lead to fault finding or diagnostics. It is better to apply
two different approaches for diagnostic determination.
Advanced simulation programmes are available for performance modeling
purposes and the one chosen for this thesis is the application of HYSYS.
In HYSYS, the feed composition, pressure and temperature as well as two of
the following four variables:




Flow rate
Duty
Efficiency
Outlet Pressure
41
Once the necessary information is provided, the appropriate speed is
determined and the other two variables are calculated. If speed curves are
available then the polytropic or isentropic heads and efficiencies for a range of
flows and speeds are input from the user into HYSYS which, for a given
speed and flowrate, calculates the outlet pressure. If the inlet and outlet
measurable parameters such as flow, pressure and temperature are entered,
then HYSYS calculates the actual compressor polytropic efficiency and
speed. The input/output to and from this software and the thermodynamic
equations used for the compressor model building and performance
evaluation are laid out in Appendix B.
42
2.5 Diagnostic Methods for Compressors
2.5.1 Diagnostics by definition
The compressor diagnostics are the application of available methods to detect
and quantify faults within the compressor. The compressor may be the axial
type as an integral part of a gas turbine or a centrifugal type compressing gas
in a process plant. The diagnostic technologies have shifted the maintenance
from preventative type to reliability centred maintenance (RCM) based and in
so doing they have increased the compressor availability. For example,
reference to Figure 1, the detection and quantification of fault of a compressor
(i.e., compressor health) at current time is diagnostics and prediction of
behaviour in future and therefore the imminent maintenance/spare parts
requirement is prognostics. The time at which maintenance action is taken
depends on company tolerances and policies. In order to carry out the
diagnostics, correctly selected performance data coming from the compressor
must be monitored and analyzed by various available tools and methods
including accurate simulation of the compressor under investigation by an
advanced simulation module leading to true diagnostics and advice on the
required maintenance. This cycle for an axial compressor in a gas turbine is
shown on Figure 2 [Ref.33].
2.5.2
The available diagnostic methods
In order to keep high availability and reliability effective maintenance is
essential meaning the asset holder must shift from preventative maintenance
philosophy to RCM and this is essentially accomplished by compressor health
monitoring and fault diagnostics.
The most common cause of degradation for both centrifugal and axial
compressors is fouling (see section 2.3.3), tip clearance due to wear and
erosion and labyrinth seal damage, body/shaft erosion and corrosion. These
faults result in changes in thermodynamic performance evaluated
fundamentally by efficiency and flow capacities which in turn produce changes
in the measured or observable dependent parameters such as discharge
pressure and temperature and rotational speed. The respective degree of
change reflects degradation which can be used to detect and isolate the
unhealthy subcomponent.
The relation between the performance deterioration and the resultant changes
in the measured parameters, fault detection and isolation was first realized by
Urban in the mid 1960s and since then up to now many sophisticated
methods of diagnostic techniques has been developed and some techniques
such as transient measurement techniques and expert systems are still under
development.
43
The purpose of condition monitoring is to draw conclusions on the condition of
the rotating equipment from the measured data in a cost effective manner.
The diagnostic techniques broadly fall into two groups of Model based and
Non-model based methods as shown on Figure 12 which are broadly
applicable to compressors as well. There are many condition monitoring
techniques (CMT) available but there is no single technique that can satisfy
the requirement of fault detection for all the conditions and sub components of
the compressor.
Figure 12. The structure of rotating equipment diagnostic techniques [Ref 37]
The following are the commonly used techniques for compressor diagnostics:
1) Performance analysis based diagnostic techniques:The GPA approach is mostly used in compressors. This is one of the most
useful approaches which allows the Engineer to identify current operation
points and position on performance map, if the compressor or compression
train is a cause of bottleneck in the system, performance losses, power
balance calculations, causes of vibration, limited production (hence
bottleneck), temperature limitations and recycle operations. Understanding
these points can help to determine an operational strategy or actions for
maintenance to maximize production, reduce the number of machinery trips,
extend life of machinery and plant.
44
Performance based diagnostics are discussed further after highlighting other
available techniques.
2) Oil system monitoring technique:This technique is useful to identify temperatures which can show increased
bearing load or worn components. Also sampling of oil is useful when there
are known failures. These all are only typically manually monitored, with
some temperature alarms usually in place.
3) Vibration monitoring techniques:Very useful coupled with performance analysis. DE and NDE radial vibration
probes with high alarm and trip set-points. Axial displacement vibration
probes to indicate worn components badly performing balance piston or even
operating performance of the compressor. Probes are set with =/- alarm and
trip set-points. Trends are typically manually monitored or analysed by
performance and vibration analysis contracts. Usually clients contract out the
vibration analysis (typically given so called “performance monitoring” title to
the contracts, even though only the vibration is monitored), however, the
complexities of performance analysis and understanding the link between
performance and vibration is only possible by specialist companies.
4) Engine usage monitoring techniques:Many Oil and gas clients do not use sophisticated monitoring systems. The
main observation is on the running hours to determine when to wash the
engine and maintenance period, also monitored are; firing temperature (EGT
or ECT), and filter inlet DP in case of axial compressors in gas turbines. Most
operators do not understand the importance of monitoring engine compressor
discharge temperature and pressures, or what these parameters tell them
about the performance of the engine. Therefore typically these are not even
instrumented.
5) Visual condition monitoring techniques:This is borescope inspection (although typically very limited) or physical
examination of components when compressor bundle is pulled.
6) Engine exit spread monitoring:Useful for power balance checks between engine and compressor to
determine the average EGT if not recorded. Useful mainly to engine
performance assessment and component lifting/ maintenance schedules.
45
7) Limited transient monitoring:This is not carried out for performance monitoring, although some specialist
companies use data (with some caution due to transient data and heat
soakage effects) to look into start-up issues. Used for offline analysis and
anti-surge and plant control assurance studies and training simulators.
8) Acoustic monitoring:Acoustic emission monitoring is carried out on critical parts that are prone to
failure in certain types of compressors; for instance the reciprocating
compressor valves. Here technology exists that records an acoustic emission
(AE) and temperature fingerprint for the operation of each valve (baseline).
Data is analyzed against the baselines to determine whether anomalies with
the operation of the compressor are present. The system is able to detect
changes in the timing and nature of valve opening & closing events and detect
the presence of leakage when valves should be closed.
A source of statistics reported by Li [Ref 39] for breakdown of faults reveal
28% of detected faults are due to oil leakage, 24% is attributed to oil
lubricated component problems, 14% due to engine performance problems
and 10% due to component vibration problems. Goetz [Ref 40] provides an
excellent statistical database for various types and components of
turbomachinery.
The diagnostics based on performance analysis is one of the most effective
tools where the analysis of gas path parameters provides information on the
severity of degradation. Many of these techniques utilize state of the art
technology. In this respect some of the available techniques are:







Gas Path Analysis (GPA- linear/non-linear, Kalman Filter)
Health Indices
Artificial Neural Networks (ANN)
Genetic Algorithms (GA)
Pattern Matching
Fuzzy Logic (part of Expert Systems)
Transient Measurements
The methods listed above apply to both compressors and gas turbines. In the
current research works, Health Indices will be concentrated upon, developed
and applied to the site compressor in the later sections.
Gas Path Analysis or GPA was first introduced in 1967 by Urban [Ref 29] as a
linear model based method. In 1990, Estamatis accounted for the non-linearity
of gas turbine engine behaviour in his modeling using conventional
optimization method. But this method may at stop at local minimum. This
46
limitation was overcome by the application of Genetic Algorithm first by Zedda
and Singh in the late 90s. Further development of the non-linear method was
done at Cranfield by Escher in 1995 utilizing Newton-Raphson technique and
a computer code “PYTHIA” [Refs 49, 32]. Neural Networks (NN) were first
introduced by Denny in 1965 and it has been widely used since mid 1980s.
NN has the advantage that only engine experimental knowledge for the
training of neural nets and once the NNs are trained computational time for
diagnosis is very short. Application of “Expert Systems” for engine diagnostics
was first developed in 1980s and this group of techniques is one of the best
method and still under development which also applicable to compressors.
More recent advances in rule-based fuzzy expert system were introduced
after mid 90s such as those introduced by Fuster & Sui et al in 1997. The
computational speed for expert systems is fast but, it is however, a
complicated modeling approach.
A neural network is artificial intelligence based diagnostic method. It is a
massively parallel distributed processor made up of simple processing units,
which has a natural propensity for storing experimental knowledge and
making it available for use. The most popularly used artificial neural network in
gas turbine diagnostics is the Feed-Forward Back-Propagation Networks, a
supervised network, where sensed information is propagated forward from
input to output layers while calculated errors are propagated backward and
used to adjust synaptic weights of neurons for better performance.
Artificial neural networks and diagnostics is based on similarities of the way
neurons and brain work and the system allows apply the “experience” to
isolate and quantify the fault. Artificial neural networks (ANNs) are a group of
algorithms originated within the field of artificial intelligence. They are not
programmed; they learn from experience. It can handle multidimensional
nonlinear systems. The ANN must first be trained before becoming useful; Li
and Arriagada give more details [Refs 39 and 42].
In Genetic Algorithm diagnostics, the idea of a fault diagnosis with genetic
algorithm is similar to a recycle feed controller in a control loop. With an initial
guess of engine component parameter vector, the engine model provides a
predicted performance measurement vector. An optimization approach is
applied to minimize an objective function meaning the predicted value by the
model becomes very close to the actual measurement with a minimum set
difference and once the set difference is reached, iteration is stopped. More
details are given by Li [Ref 39].
Li and Singh [Refs 29, 31] provide further sources of information and further
reading for the current available diagnostic techniques listed at the start of this
section.
47
Broadly speaking all diagnostic techniques listed above and others rely on
GPA [Ref 31]. The fundamental concept of gas path diagnostics is shown in
Figure 13 and it illustrates the nature of the gas path diagnostic problem. The
common features of all Gas Path Diagnostic Techniques are:





Objective Function – This is a representation of the problem
(minimization of objective function), easy to solve and take into account
measurement noise and sensor bias. Figure 14 shows a high pressure
compressor search space and the model. It is obtained by varying the
deterioration or degradation in mass flow from –3.5% to +3.5% and the
deterioration in efficiency from 0 to 3.5%. The values obtained are then
compared with data generated by introducing a 2.75% efficiency
deterioration in the high-pressure compressor. Each point on the
surface plot is a potential solution and the best solution is the one
having the lowest objective function.
Number of Operating Points – A technique using a single operating
point can result in a high degree of accuracy if the number of
measurements is higher than the performance parameters. For the
case of number of measurements being lower than the number of
performance parameters, a more judicious choice of diagnostic
technique is needed such as multiple operating point analysis
technique (MOPA). Here the accuracy of diagnostics depends on the
choice and number of data points [Ref 31].
Number of Measurements - The choice of number and type of
sensors is a critical issue which can greatly affect the final results as
well as the computational burden of the diagnostic technique. A high
number of sensors invariably makes the search space smoother and
therefore reduces the computational burden. Refer to the two plots
shown on Figure 15. The graph on the left shows the search space
generated with 9 instruments and the graph on the right is for the same
engine but with 16 instruments. The flatness of the plot around the
minimum on the left graph implies that optimum would be difficult to be
identified. Whereas for the same engine using 16 instruments referring
to the graph on the right of Figure 15, it shows a distinct convergence
to a global minimum which is easier to locate.
Quality of Measurements – This is another important issue with
diagnostics techniques related to the quality of the measurements or
observability. Not all choices of measurements will allow the
identification or observability of all the faults.
Search Algorithm – The complexity of the search space characterized
by several rather than one local minima favours the utilization of search
methods other than classic optimization techniques. This include
“random search” whereby controlling a set of parameters the random
search is guided progressively towards a solution. However, Genetic
48
Algorithm (GA) often outperforms the referred method in many
optimization problems.
The factors that give may result in erroneous diagnostics is measurement
noise causing data scattering around the true value and in this instances data
filtering and averaging are applied to reduce the errors and the diagnostic
accuracy. Li [Ref 29] has identified good sources of referencing for
measurement noise filtering via the application of auto associated neural
network (AANN).
In GPA there are two methods available: linear and nonlinear and these are
described later in more details. Since the behaviour of a compressor’s
component is generally non-linear, the application of linear method for
diagnostics is limited although it has its own advantageous including providing
a quick solution and fault isolation, quantification and multiple fault
diagnostics. Both of the linear and non linear GPA apply optimization methods
to deduce a fault and nonlinear GPA is combined with a conventional
optimization method. But conventional optimization stop at a local minimum
and genetic algorithm has overcome this disadvantage. Kalman filters and
optimal estimation are applied to GPA in order to overcome its setbacks [Ref
31].
In the current compressor performance adaptation and diagnostic research at
hand, linear and non-linear model based diagnostic is of particular interest
which is described more fully in the next section.
49
Figure 13. Fundamental concept of gas path diagnostics [Ref 31]
50
Figure 14. Search space for compressor
[Ref 31]
Figure 15. Effects of number of measurements on diagnostics . [Ref 31]
2.5.2.1 Linear and Non-Linear GPA Model Based Diagnostic Method
A linear and non-linear GPA approach is an effective tool for performance
diagnostics, the principle of which has been utilized for the subject research.
The principle base description and rules for linear and non-linear GPA are
described below.
Reference to Figure 16, if at a certain operating point of the compressor a
linear relationship is taken between the dependent or measurable parameter
such as gas path pressures and temperatures, mass throughput etc., and the
independent parameter such as pressure ratio and efficiency the following
relation is valid:
= H.
51
Degradation can be recognized as the deviation in performance from that
when the engine was new. At a given operating point and at certain time
during operation a linear relationship between gas path measurement
deviation vector ∆ and engine component health parameter deviation vector
∆ can be obtained from the engine performance model
tailor series expansion [Ref 30]:
using a
∆ = H. ∆
Where H = Influence Coefficient Matrix (ICM)
Therefore the engine performance degradation represented with ∆ can be
obtained with the following equation if the number of measurements is at least
equal the number of health parameters:
∆ = H-1. ∆
Where,
= An independent variable such as pressure ratio, flow capacity or efficiency
= A dependent variable. These are the measurable variables such as
pressure, temperature or mass flow rate, and,
∆ denotes a change (deterioration or degradation) in vector parameter
H-1 = Fault Coefficient Matrix (FCM) or Diagnostic Matrix.
The above equation shows that by multiplying the measured changes in the
dependent parameters by the FCM, the changes in the independent
parameters can be found. This allows the cause of the deterioration to be
determined. Linear GPA is suitable for small changes or degradations as
changes at or near the operating point are linear.
The above is the linear GPA. Since performance normally deviates nonlinearly with degradation, solution by linear method may lead to significant
errors and a non-linear GPA is developed where the linear GPA is used
iteratively in the following manner until a converged solution is obtained using
Newton-Raphson method; see Figure 16A.
With an initial guessed parameter vector
, the compressor or engine model
provides a predicted performance measurement vector
. An optimization
approach is then applied to minimize an Objective Function (see Figures 14,
15 and 16A) minimizing the error by an iterative process. The iteration is
carried out until the best predicted component parameter vector for real is
obtained. The error is defined as the difference between the real
52
measurement of vector and the predicted measurement of vector
normally a very small value (0.001-0.002).
and it is
The non linear method described above utilizes a computer code, PYTHIA,
which was developed by Cranfield University [Ref 33] and it has the axial
compressor module as a part of the gas turbine. Others have also produced
optimization programmes for engine simulation adaptive modeling applying for
instance Generalized Minimum Residual (GMR) method or Maximum
Likelihood Estimate (MLE) method [Ref 29], which are also adaptable to
compressors.
In order to make all differential GPA methods valid, the number of measured
performance variables must be greater than or at least equal to the number of
diagnostic parameter to be estimated.
The success of identifying a given fault set depends greatly on the set of
monitored parameters. In other words, if the dependent variables do not relate
to the sought-after fault, then either the diagnostic or the fault set is in error.
Figure 16A. Nonlinear Solution (by repetitive linear iterations) for compressor
diagnostics [Ref. 49]
53
Taking into account the influence of ambient and operating conditions, sensor
failure and compressor component degradation, an “integrated” GPA
diagnostic system should be described. By applying Theta (θ) and Delta (δ)
factors to the data (see later in Chapter 4), performance data comparison of
clean and degraded compressor on the same environmental and inlet
conditions are enabled so that environmental changes do not interfere with
the degradation results and differences in actual and expected performance
would be due to degradation and instrument errors only. Reference to the site
compressor under research, all critical instruments are regularly calibrated,
maintained and failed sensors are automatically reported to the control room.
Only instrument noise is inevitable which due to the design and operational
constraints and it is not considered to have significant effect on the diagnostic
results as the instruments are regularly calibrated and have self diagnosing
and reporting capabilities. Li [Ref 32] has provided an insight into
measurement errors, in which, sources for further referencing has also been
identified.
GPA provides the means for assessing the non-measurable or independent
compressor parameter deviations by establishing a relationship between them
and the measurable or dependent parameters. Based on the changes from a
new and clean status which is the base line, a compressor gas path diagnosis
is issued.
Based on the works by Marinai [Ref. 66], Kammunge [Ref. 63] has
summarised the diagnostic methods as follows:
1) Linear GPA with ICM inversion are based on the relative changes in the
health parameters are relatively small. The inadequacy of this assumption has
led to the development of nonlinear methods
2) Multiple Fault Isolation (MFI) are suited for the analysis of slow
deteriorating components, whereas Single Fault Isolation (SFI) implies a rapid
trend shift, perhaps due to a single or multiple entity going skewed.
3) Estimation techniques require prior information and the solution can be
dramatically affected by this choice.
4) Expert systems, ANNs and fuzzy logic systems are referred to as modelfree systems. This feature has the advantage of data fusion capability but the
limitation is that no model-based proof of robustness is possible.
Figure 16B shows the summary of diagnostic methods [Res. 66, 63] and
Figure 16C is an illustrative summary of the diagnostic methods based on
model complexity and computational speed [Refs 41, 58].
54
Figure 16B. Principle summary and comparison of gas path diagnostic methods [Refs. 66, 63]
55
Figure 16C. Summary of diagnostic methods based on model complexity and
computation speed [Refs 41, 58]
56
2.6 Continuous on-line monitoring for compressor health monitoring
As with most mechanical devices, the performance of a running compressor
slowly deteriorates from its normal healthy condition. This degradation can be
quantified by comparing the real runtime process parameters to those
provided by compressor manufacturer (OEM data), the latter is a (decreasing)
variable and a function of time before a major overhaul is carried out (see
Figure 1 and Ref. 57).
Diagnostics of a running compressor is necessary to optimize the
performance as well as optimizing the maintenance strategy saving in periodic
shutdowns leading to less operational costs. Continuous performance
monitoring of the compressor is the best available mean to provide real time
performance data tracking degradation and ensuring remedial action is taken
as forecasted in advance of the failure. Figure 17 shows how the online
information can help decide the washing time for the compressor. The
highpoints are the peak performances which depreciate with time. Notation “0”
is an established base line. The negative troughs are the lowest performance
that the operator will allow before washing is performed after which the
performance is picked up again.
Figure 17. Online performance monitoring – thermodynamic analysis of
compressor capacity index [Ref 11]
A program that incorporates the condition of a critical component (condition
monitoring) rather than its operating time (time based or pre-scheduled
maintenance) offers benefits to the economics of the plant because the
equipment may still be healthy if the replacement is time based. The on-line
performance monitoring (OPM) helps turbomachinery operators to know
57
instantly when something goes wrong or starting to go wrong. By definition
rotating equipment condition monitoring is a management technique that uses
the regular evaluation of actual operating conditions of the machinery to
optimize the total machinery operation.
OPM benefits the plant operators by confirming if the equipment is performing
as promised by the manufacturer. Maintenance scheduling, including cleaning
and major overhaul such as a crack that may appear in the diffuser of the
compressor, can be based on equipment condition. Current and historical
data are invaluable when rerates are considered.
By informing the operator where the compressor is operating on its curve,
online monitoring can help prevent mechanical problems such as avoiding
impeller failures. Start ups and other transients such as trip will remain
unknown unless OPM is implemented.
The main difference between gas turbine OPM and that of compressor is the
medium going through the engine in the entire path: in gas turbine the
medium is air and fuel or its products whereas in compressors the medium is
normally a process gas whose properties such as density and specific heat
ratio vary from plant to another [Refs. 43, 44, 45]. Therefore the gas path and
the thermodynamic properties analysis in a compressor is a distinguished
difference with gas turbine. As the correct diagnosis depends on the accuracy
of the predicted or simulation model, then the properties or equations used to
generate or predict the gas behaviour under an envelope of operating
scenario must be accurate as well. In OPM systems compressor health is
measured by comparing the current performance with manufacturer’s data.
Raw operating data such as discharge pressure or pressure ratio are not
sufficient indicators of compressor performance as these parameters can
change with inlet temperature and pressure. But head and efficiency are
accurate indicators of compressor performance as they are not affected by
process conditions. The BRW (Benedict, Webb & Rubin) EOS (Equation of
State) is used to calculate the head and this EOS is a comprehensive
equation that defines gas parameters accurately [Ref 47]. In HYSYS
simulation program this option is available. Sandberg [Ref 48] provides an
excellent insight into use of various EOS and the relative accuracies under
various scenarios. Calculating compressor gas power (i.e., the energy
transferred from the impeller blades to the gas) helps to confirm the input data
are accurate. Any degradation in the interstage seals, corrosion or fouling will
show up as a loss in efficiency and head but the work input stays the same.
Real-time software model that can be adapted to the site compressor is a tool
to provide constant health monitoring system. This model may be built from
OEM performance data, which is for the case at hand by the application of
HYSYS advanced simulation software capable of precise mapping of the site
58
compressor online as it is performing. When OEM data is not available, the
data necessary to build a normalized mathematical model through field-testing
can be performed. This baseline performance data is then continuously
compared to the actual compressor performance using real-time field data.
Online performance monitoring can be regarded as one of the most powerful
tools to continuously monitor the health of the compressor in real time and
warn the operator in advance if the deviation of a performance indicator is
more than expected so that the fault can be inspected and resolved.
As it will be described in the next section continuous on-line monitoring allows
specific trends to be developed in real time for the compressor and these
trends can be compared with a established base line where the operator can
decide in advance the need for, and the nature of, the maintenance thus
shifting the pre-scheduled maintenance policy to condition based
maintenance. Further references on OPM can be found elsewhere [Refs. 1, 2,
4, 10-13, 19-20, 47 and 57].
The techniques and applications of online monitoring have been discussed in
this chapter
and the next chapter focuses on compressor performance
adaptation.
2.6.1 Requirements for rotating equipment condition monitoring system
The following must be satisfied in order that a condition monitoring system
can be used in compressor (or gas turbine) applications:












Reliability
Accuracy
Robustness
Computational Efficiency
Hybrid Schemes
Data Fusion
Data Uncertainty
Online Application
Sensor Faults
Multiple Component Fault/Sensor Fault
Prognostics
Maintenance Advice
Prognostics and the resultant maintenance advice are the “fruits” of online
monitoring once the other referred factors are established for the rotating
equipment.
59
2.6.2 Benefits of online monitoring
Real time performance data must be integrated with mechanical parameters
including vibration data, bearing temperatures and oil condition so that
operators can better select operating regimes and maintenance schedules. By
going through the literatures, it is established that the key benefits of online
monitoring system include:





Faster troubleshooting minimizing downtime and loss of production
The trending of machinery performance helping to isolate the operating
points that may have caused machine’s current condition
Immediate evaluation of the effects of process changes
Extended scheduled maintenance as knowing the machine
performance along with vibration, thrust bearing temperature and oil
condition enables this strategy. This effectively enhances the
availability which is described in details in Section 2.6.2.1.
Minimizing insurance premiums
For the current research at hand on compressor model adaptation and
diagnostics, all the OEM and actual (site) performance curves are available
(Appendix C). HYSYS is also available to simulate the real compressor for
ANY inlet/outlet conditions or gas properties. Drawing from above, all the
tools are available for compressor monitoring and diagnostics works to be
carried out.
2.6.2.1 Effect of OLM on Reliability-Availability-Maintainability (RAM)
Online monitoring effectively increases the rotating equipment or plant’s
Reliability-Availability- Maintainability (RAM) due to advance warnings to the
operator on the behaviour of the rotating equipment and the shift of
maintenance philosophy from preventative mode to reliability centred
maintenance.
It must be emphasized that on-line monitoring cannot pick up all types of
faults of the centrifugal compressor or axial compressor in a gas turbine.
There are essentially three types of failures to all failures: The “instantaneous
failures” such as fatigue failure of compressor blades give no warning. No
amount of monitoring will detect the onset of these types of failures. The”
delayed time-dependant failures” are those for which there is no detectable
change until at some point of its life deterioration occurs. The ability to detect
early the start of deterioration is a challenge and hence requires a
sophisticated system of Equipment Health Monitoring to identify it. The “pure
time-dependant type of failure” is also a candidate for engine online health
monitoring.
60
Since online monitoring of the rotating equipment enhances reliability and
availability, it is prudent to highlight the definitions and importance of reliability
and availability.
Reliability is defined as [Ref 39]:
Reliability = (1-(FOH/PH))*100%.
FOH= Total forced outage hours and PH=Period hours.
Reliability is a design feature and the rotating equipment can at best be
restored to its design level. Reliability could be increased by stress margins
used for critical speeds, blade and vane vibration frequencies and
environmental conditions including fuel, air and oil specifications. Higher
reliability causes higher costs but has an optimum point in the “costs versus
reliability graph” and good maintenance techniques are required to keep it
which increases with increase in reliability. Note reliability falls with time
unless there is maintenance programme so there is a requirement to have
knowledge of the engine condition at the operating time.
The largest contributor to forced outage rates are often the support systems
such as control and fuel systems. The associated down-time can be managed
to acceptable levels by design redundancies and spares. Refer to Figure 18
below for a comparison of contributions to outages by gas turbine components
which include the axial compressor section.
Figure 18. Contribution of gas turbine components to outages [7]
The major gas path components such as compressors and turbines have a
high reliability. However, downtime could be large if outage is caused by these
61
components. Statistics show that gas path components contribute a lot to
down time even though they exhibit high reliability.
Outage rate is related to reliability and total downtime is related to availability.
Availability may be defined as:
Availability= MTBF/ (MTBF + Downtime), MTBF depends on reliability
For the same downtime the availability increases with increasing MTBF and
this is shown in Figure 19, i.e., the availability is inversely proportional to
downtime. MTBF is influenced by reliability whereas downtime is influenced
by factors such as equipment design is modular, instrumentation and
diagnostic capability are adequate and access for inspection is good.
With reference to Figure 20, if down time associated with maintenance action
is large, relatively poor availability will result. For example, if MTBM is 20,000
hrs and procurement of a part is 3 months, availability achieved is 90%. But if
due to diagnostic techniques the parts could have been identified in advance
and reduced the downtime to 7 days, then availability would be 99%. This
offers a significant improvement in economics. Figure 21 shows the trend of
how the overall availability is improved by having spares available [Ref. 39].
An economical analysis should be carried out to see the balance between
extra expenditures in fixed costs and the gains in terms of higher availabilities.
Figure 19. Relation between mean time between failure, downtime and
availability [Ref. 39]
62
Figure 20. Relation between availability and downtime [Ref. 39]
Figure 21. Plant availability taken at gas turbine availability of 90% [Ref. 39]
63
3. Compressor Degradation Modeling and Simulation
3.1. Introduction
As compressors continue to operate, there are deviations to gas path
measurements such as rotational speed, discharge pressure and temperature
and gas throughput. These variations may be due to varying ambient
conditions and/or process gas properties and the degradation of compressor
associated with prolonged operation time. The performance of the compressor
may be represented by pressure ratio and efficiency and its health may be
represented by the magnitude of deviation from the clean state in throughput
and efficiency. As compressors degrade the whole of performance curves
shift to a new position which is downward and generally to the left of the
original performance map. The amount of shift is indication of severity of the
degradation and thus based on this information the operator can decide the
operation or maintenance period of the compressor.
During the compressor operation, the gas path measurements are obtained
on regular basis and these are used to model the degraded performance of
the compressor. With the effects of ambient conditions and gas property
variations taken out, the fall in performance may be due to degradation alone
taken that the measurement errors are within an acceptable band. Thus in the
following works it is taken that all measurements are taken at same
environmental condition and the gas properties do not change during the test
period. It is also assumed that gas path sensors are in good health thus there
is no measurement bias. Since some measurement noise is inevitable in the
measurements which will impact the diagnostics they are discussed
separately. However, the errors due to noise and bias are reducing with
advancing technologies and state of the art compressor and layout designs for
measurements.
The compressor characteristic maps are developed from the basic data. This
model generates polytropic heads for a range of flows and speeds which are
then fed into HYSYS advanced simulation programme to model the
performance of the compressor in its initial or clean state. Degradation is then
modeled and simulated by step reductions in mass throughput and efficiency
and the effects on the measured parameters are then deduced. These
measured parameters include speed, discharge temperature and power.
Similar to the high quality of model built by HYSYS for clean or un-degraded
compressors, representative models are also built for degraded compressors
as the simulation programme is highly adaptable. There is no library of
performance curves stored in HYSYS and therefore curves specific to the
compressor of interest need to be generated using actual or simulated data.
For an accurate and complete build up of the compressor model, in addition to
thermodynamic principles, computational fluid dynamics (CFD) should be
64
considered taking into account the geometry and build the compressor model
stage by stage.
The above works are divided into three sections:
1) Compressor model generation: Development of a representative
model generation for a clean or un-degraded compressor utilizing
thermodynamics principles. This has been done under the current
Chapter. The effects of degradation on the whole characteristic maps
have been calculated and demonstrated on the same maps to show
the degradation effect on the entire performance map rather than at
one point of operation. The un-degraded or clean compressor model is
transferred to HYSYS for further analysis on degradation by
simulation. The transfer of clean model into HYSYS and the works
below have been described in Chapter 6.
2) Compressor degradation simulation: A progressive degradation
with time has been assumed for an operating point and performance
has been monitored by HYSYS simulations for a period of 1 year since
the start of the clean compressor. As an operational constraint, the
pressure ratio has been held constant throughout the test period. Two
modes of degradation patterns have been assumed over the control
year: linear and non-linear falls in mass throughput (0 – 5%) and
efficiency (0 – 3%) simultaneously throughout the same period of 1
year. The simulated measurements have been taken as the ‘actual’
readings in compressor discharge temperature, gas power demand
and speed. The estimation of degradation in throughput and efficiency
has been estimated using scaling method applied over the measured
parameters.
3) Compressor
diagnostics:
The
fault
signatures
(relative
measurement deviation compared with clean measurements) have
been presented and the degradation have been estimated for both the
linear and non-linear degradation path. Results are then analysed and
discussed with conclusions.
65
3.2. Compressor health status estimation
The operating point on a degraded compressor map can be compared with
the same point but on the un-degraded (clean) compressor map leading to
estimation of degradation. This comparison necessitates the assumption that
conditions of gas entry and environmental remain the same. In case these
change, they need to be accounted for in the comparison.
The degradation of the compressor is represented by the shift of the
characteristic curves on the maps and such shifts are represented by
Degradation Indices. The solid lines on Figure 22 represent the un-degraded
or clean compressor map and doted lines represent the degraded compressor
map. Reference to the same figure, the degradation of a compressor may be
described by the deviation of 3 degradation indices: pressure ratio, flow
capacity and efficiency. The degree of shift of characteristic speed lines from
when the compressor was new, may describe the health state of the
compressor. Thus 3 independent degradation indices (scaling factors) can be
devised to describe the health state of the compressor as shown below:
SFF = FCdeg/FCcl
SFPR=PRdeg/PRdeg
SFEff= ηdeg/ηcl
Eq. 3-1
Eq. 3-2
Eq. 3-3
On the basis of above, the closer the scaling factor is to unity (1), the healthier
(clean) is the compressor. At the initial day of operation (t=0), compressor
may be taken as un-degraded (clean) and all the scaling factors are 1 and
degradation is 0. Later on in the time scale, the difference between these
scaling factors and unity denotes the degradation.
In the proceeding works, the health of the simulated compressor at regular
intervals after the initial operation are estimated and measurable and
measurable output are deduced by simulation and the results are presented
and discussed.
66
Figure 22. Clean and degraded compressor performance map
67
3.3. Data measurements, corrections and uncertainty
The selected instrumentation set for the analysis of the compressor model is
described in Table 1.
No. Symbol
Description
1
2
3
4
Throughput on mass basis
Compressor Temperature
Compressor Pressure
Shaft Rotational Speed
m (kg/hr)
T1/T2(K)
P1/P2(bara)
N (rpm)
Note: Subscripts 1 & 2 denotes compressor
inlet (suction) and outlet (discharge)
Table 1. Instrumentation set for compressor simulation
For the purpose of the proceeding works, it is assumed that all gas path
sensors are in good health (no measurement bias). The compressor inlet gas
properties and the environmental changes over the testing and sampling
period are ignored and hence both clean and degraded compressor
performance are measured at the same external conditions to make the data
comparable otherwise corrections are necessary to adjust for gas property
and environmental changes.
The deviation of the compressor gas path parameters indicates degraded
compressor performance. The simulated samples of those parameters are
collected and regarded as actual gas path measurements. Statistically, the
larger the number of samples, the greater the certainty on the readings. In the
gas fields the traditional practice has been to obtain compressor readings by
daily monitoring for the purpose of performance evaluation and the trend is
becoming more intense as operators become more aware of benefits of online
monitoring. Nowadays rotating equipment purchased automatically has an
associated online monitoring system.
Some measurement noise is inevitable in compressor measurements and has
a negative impact on diagnostic results if the noise effect is not quantified.
Therefore the intention should always be to introduce measurement errors in
the gas path simulation works.
Error is the difference between true value and measured value. An associated
interval that include the true measured value should be included in the
analysis. The errors may be classified as two types of fixed and random. The
random error between repeated measurements is called precision error and a
standard deviation may be used as a tool to measure the precision error as
follows:
68
Eq. 3-4
Where,
S=Precision Error Index
xi=Observed value of the sample items
x= Mean value of the observations
N= Total no. of samples
Bias is the constant or systematic error. To obtain the bias, the “true” value is
defined together with an associated limit.
Uncertainty may be centred about the measurement and defined as:
U=+/- (B+t95S)
Eq. 3-5
Where,
B=Bias limit
S=Precision Error Index (obtained by the previous equation)
t95=95th percentile point of the two –tailed student’s ‘t’ distribution and t=2 if
sample size is greater than 30.
The maximum measurement noise for different gas path measurable
parameters is based on the information provided by Dyson and Doel [Ref. 64],
shown in the Table 2 below.
Table 2. Maximum measurement noise [Ref. 64]
To reduce the negative impact of measurement noise on performance and
diagnostic analysis, multiple gas path measurement samples are obtained in
the simulation and a “rolling averaging” may applied to get an averaged
measurement sample prior to processing of the measurements. Rolling
averaging reduces noise for a measurement by taking a rolling average (RA),
as expressed in the equation below, of its values in certain last period [Ref.
58]:
69
Eq. 3-6
Where,
zk is the value of a measurement at time k, RA is the rolling average of the
measurement, I is the total number of measurement values that involved in
the rolling average calculation process.
Alternatively, in an “exponential averaging” (EA) as expressed in equation (37) of a measurement is calculated by its current value and the average of the
last ten values, where the current value is associated with a weight with 15%
and the average with a weight with 85% [Ref. 58]:
Eq. 3-7
As a result data averaging the scatter of sample data over the original data
are appreciably reduced and the averaged sample values approach the true
values.
70
3.4. A brief description of the simulation programme, HYSYS
The simulation programme HYSYS is industry proven and widely used
throughout the oil & gas and energy industries for simulation of major
equipment and compressor is one of them.
HYSYS does not have any pre-stored compressor performance maps. This
programme utilizes the rigorous equations of state for property generation of
hydrocarbon gas feeding the compressor as well as the thermodynamic and
iterative laws and proportionality laws to give result and data necessary for
compressor design and operation. HYSYS is a suitable programme for the
generation of performance curves both for new and degraded compressors
alike based on, and generated by, the input data of the user. Sufficient
number of runs without the performance curves using site measured data can
lead to building a complete map of actual compressor performance curves.
The input to the programme with and without the performance curves is given
below in Table 3.
HYSYS is considered a simulation tool to simulate new and degraded
compressors as well building the actual site compressor model for data
analysis and operational optimization and as such it is used for the current
works under research.
71
Without Curves
With Curves
1. Flow rate and inlet pressure
are known. Then:
Flow rate and inlet pressure
are known. Then:
A) Specify outlet pressure.
And,
B) Specify either Adiabatic
or Polytropic efficiency.
A) Specify operating speed.
B)HYSYS uses curves to
determine efficiency and
head.
HYSYS calculates the
required energy, outlet
temperature, and other
efficiency.
HYSYS calculates outlet
pressure, temperature, and
applied duty.
2. Flow rate and inlet pressure
are known. Then:
A) Specify efficiency and
duty.
HYSYS calculates outlet
pressure, temperature, and
other efficiency.
Flow rate, inlet pressure, and
efficiency are known. Then:
A)HYSYS interpolates curves
to determine operating
speed and head.
HYSYS calculates outlet
pressure, temperature, and
applied duty.
Table 3. Variables requiring input in HYSYS compressor calculations
72
3.5. Development of performance curves and performance degradation
modeling
Reference is made to the compressors curves supplied by the vendors. These
curves are obtained for the rated conditions based on the physical properties
for the hydrocarbon gas. However, the curves supplied by OEM are not
always available and the performance curves need to be developed by
applying thermodynamic equations.
Spreadsheet is developed to calculate Pressure ratio, Discharge Pressure,
Discharge Temperature and the Power requirement for the given volumetric
flow rate through the compressor.
Following sets of Equations are used for determining discharge conditions for
a given Compressor and inlet volumetric flow rate for any gas composition
taken into consideration.
Isentropic Exponent K, is given by
Eq. 3-8:
k 
Cp
Cv
Where,
k = Isentropic exponent
Cp = Specific Heat at Constant pressure (kJ/kg K)
Cv = Specific Heat at Constant volume (kJ/kg K)
It is known that
Eq. 3-9:
MC p  MCv  R
Where,
R = Universal Gas Constant 8.314 kJ/k-mole K
MCp = Molar Specific Heat at constant pressure kJ/k-mole K
MCv = Molar Specific Heat at constant volume kJ/k-mole K
Hence Rearranging Eq. 3-9 and substituting for Cv in Eq. 3-8, obtain:
Eq. 3-10:
k
MC p
MC p  8.314
73
The Discharge Temperature of the Gas leaving the Compressor is calculated
using following.
Eq. 3-11:

P
Td  Ts  273 d

 Ps




n 1
n

 273


Where,
Td = Discharge Temperature in degC
Ts = Suction Temperature (or T1) in degC
Pd = Discharge Pressure (or T2) in bar
Ps = Suction Pressure in bar
n = polytropic exponent
Polytropic Exponent is defined as
Eq. 3-12:
n
 k 

p
n  1  k  1
Where,
k = Isentropic Exponent
ηp = Polytropic Efficiency
Polytropic Head is calculated using following
Eq. 3-13:
Eq. 6:

8314  Z avgTs  Pd

Hp 
 n  1   Ps
MW 
 
 n 



n 1
n

 1


Where,
Hp = Polytropic Head Nm/kg
Zavg = Average Compressibility Factor
Pd = Discharge Pressure bara
Ps = Suction Pressure bara
MW = Molecular Weight of the Gas
74
Ts = Suction Temperature K
A generalized equation to determine the discharge conditions for a given
compressor and inlet volumetric flow rate that results from a variation of all
inlet condition at rated conditions developed for any given (constant) rotational
speed is given below [Lapina: 1982]. Reference may be made to Table 6-1
where the basic data for both rated and site (new) conditions are available.
Eq. 3-14A:
Where subscripts N and R denotes New (site) and Rated compressor
respectively.
If the test gas is principally same as OEM, rearranging for pressure ratio
calculation is found in
Eq. 3-14B:
PR = [(Hp/a) +1] (n/(n+1))
Where PR is the ratio of discharge to inlet Pressure
And a =
8314  Z avgTs
 n 1
MW 

 n 
The Gas Power is calculated using following
Eq.3-15:
GP 
w H p
 p  3.6 106
Where,
GP = Gas Power kW
m = Gas Flow rate kg/h
ηp = Polytropic Efficiency
Hp = Polytropic Head Nm/kg
75
Inlet Volumetric Gas Flow through the Compressor is calculated using
following
Eq. 3-16:
Q
w

Where,
Q = Volumetric Inlet Flowrate, m3/h
m = Mass Flow Rate, kg/h
ρ = Gas Density at suction conditions, kg/m3
And Gas Density is given by following
Eq. 3-17:

PM
zRT
Where,
ρ = Gas Density kg/m
P = Suction pressure bara
z = Compressibility Factor
R = Universal Gas Constant N m/mole K
T = Suction Temperature K
M = Gas Molecular weight
76
3.5.1. Affinity (Fan) Laws
The compressor maps including off design can be generated using the affinity
laws (Fan laws or laws of proportionality). These relationships are accurate for
ideal gas and medium pressures (as in the research case) and that these laws
do not take into account the volume ratio effects. Natural gas follow closely
follow the ideal gas laws unless the gas is very sour. For very high pressures,
Fan laws should be used with caution.
Once the performance curve (polytropic head or H p versus throughput) is
developed for 100% speed applying the thermodynamic equations in the
previous Section, affinity (proportionality or Fan) laws can be applied to
estimate the Hp versus throughput for other speeds.
The basic Fan laws are [Ref 28]:
Eq. 3-18:
(a)
Q
N
(b)
Hp N
(c)
PWR
(d)
Delta T
(e)
ℓn (
2
3
N
2
N
) N2
Based on the above, the following is deduced.
Eq. 3-19:
Q/N=Constant1=K1 and Hp/N2=Constant2=K2,
And,
Eq.3-20:
Ndeg =
x Nclean
Or
Eq. 3-21:
Nrerate =
x Noriginal
77
4. A Novel Compressor Performance Adaptation Technique
4.1 Use of dimensionless groups in compressor performance analysis
[Ref. 56]
Dimensional analysis is very important in compressor performance as it is
only necessary to plot 2 sets of curves in order to define the compressor
performance completely. In this respect the importance of Dimensionless,
Quasi-dimensionless, Referred and Scaling Parameter Groups in all aspects
of rotating equipment performance can not be over emphasized.
“Dimensionless” groups are the groups that contain all variables affecting
component performance including linear scaling and fluid properties. This
form is of interest if different working fluids are to be considered. “Quasidimensionless” groups are those having the specific gas constant, gamma
and the physical diameter omitted. This suits the situation of a component
design of linear scale, using a fluid of fixed properties such as molecular
weight; i.e., only operational condition shall be considered. “Scaling
parameters” groups are the dimensionless groups with only the working fluid
properties omitted. This will allow assessment on performance effects of linear
scaling an existing compressor or matching differentially scaled existing
compressors. Referred (or corrected) groups are the groups that are directly
proportional to Quasi-dimensionless groups. The difference is the substitution
of theta (θ) for component inlet temperature and delta (δ) for component inlet
pressure.
The compressor performance maps supplied by OEM such as pressure ratio
or discharge pressure versus throughput are normally for a specific sets of
compressor inlet temperature, pressure and gas physical properties such as
gamma and molecular weight. An attempt to carry out full range of tests and
make the full presentation of variations of these quantities would be an
impossible task because of so many variables required to describe
numerically compressor performance throughout the operational envelope. A
large part of this complication is eased with applying the dimensional grouping
through which the dimensionally related variables are combined so that the
performance analysis becomes manageable. Thus the complete
characteristics of any compressor can be defined by two sets of curves only.
When compressing a specific gas for a machine of fixed size, there will be:
f (P2/P1, T2/T1, m√(T1)/P1, N/ √T1) = 0
Suffices 1 and 2 denote compressor entry and exit conditions. Each of terms
within the function is either dimensionless or quasi-dimensionless. Plotting
one group against the other for various fixed values of the third will
characterise the performance of a compressor allowing ‘on the spot’ tracking
of the compressor performance changes with variations in environmental
78
condition or scaling. The useful plots are pressure ratio P 2/P1 or temperature
ratio T2/T1 versus non-dimensional mass flow m√(T1)/P1 for a range of nondimensional rotational speed N/ √T 1.
In the proceeding works where site data has been applied to building the
performance characteristics of the compressor, the concept of (θ) for
component inlet temperature and delta (δ) for component inlet pressure have
been applied as a tool to simplify the analytical aspect of this research without
compromising the conclusions reached.
4.2 Performance Data Referring and Scaling
The OEM performance data for a compressor is for a specific set of
predetermined ambient and gas inlet conditions and properties. At site,
however, very often the compressor runs or it is tested at a set of gas inlet,
speed and ambient conditions different from those of OEM and therefore the
compressor performance can not be diagnosed based directly on OEM data.
Hence to model the performance of the real compressor and see how it is
performing based on OEM data, it is needed to:
1. Convert the OEM basis and performance curves to a referred site
conditions and generate new performance curves. This is described in
this section below.
2. Establish site compressor’s referred performance at specific site or test
flow rates and speeds. Speed curve interpolation for data between the
curves and extrapolation for establishing the curves outside the tested
range may be necessary, depending on the values of tested speeds
and the corresponding flow rates. Scale factors are then applied by
comparing the referred OEM curve and the site performance to shift the
OEM speed curves and generate actual site compressor performance
curves for a range of compressor speeds by successive iteration. The
performance adaptation by successive iteration is novel and the
technique has been detailed out in the Chapter 7 and Appendix D.
Performance predictions based on scaling are very accurate close to
the test or local point (say at design point or highest rotational speed)
but the accuracy decreases as the operating point move away further
and further from the locality. The latter is true if the scaling factor
remains the same under all speeds of rotation. But how about if the
scaling factor is given an opportunity to update itself (performance
adaptation) as the operating point move from high speed to lower
speed curves so that curve shifts are adjusted. Starting from the highest
down to the lowest available test speeds, in each iteration one curve is
fixed in position until all test points are completed and then there will be
a family of performance curves on the map with the test points falling
79
exactly on the curves and this graph will be actual performance map of
the compressor at a particular time.
The proceeding works in this section and Chapter 7 describe step-by-step the
procedures for the accomplishment of the above objectives. All the
measurement data are validated by regularly calibrated and self diagnosing
instrument sets such that errors in measurements are diminishingly small and
have no determinant effect on the purpose of performance mapping. The full
details of application and sample calculation are in Appendix D.
All the OEM and site data must be converted to “referred data” of performance
parameters such as Pressure Ratio (PR) and Efficiency versus mass flow so
that the same environmental and gas inlet conditions applies and
comparisons between the OEM and site data is made possible. By applying
interpolation technique between referred OEM speed curves, a performance
curve is generated for the speed that match the site referred speed and the
scale factor is calculated which is the ratio of referred OEM performance and
referred site performance at the particular site referred mass flow rate. The
scale factor is then applied to the entire OEM referred speed curve such that
the whole speed curve is shifted. The site performance curve is thus
established for a particular referred speed and the procedure is repeated for
other available lower referred speeds at site. In each iteration, the position of
speed curves are updated and revised with one speed curve (the highest
speed) getting fixed in position. This is repeated until all data points at various
speeds are entered and all these points fall exactly on the characteristic
maps. It is required to apply the scale factor in the manner described because
unlike the OEM performance data being available for a wide range of flows,
site performance data for a particular referred speed is normally available only
for a particular mass flow or a “point” on the curve.
Plotting the performance curves on dimensionless or quasi-dimensionless
axes is very useful as it is only necessary to plot 2 sets of curves in order to
define the compressor performance completely. Scaling parameters Theta (θ)
and Delta (δ) are used to refer the OEM and site data to a common datum,
where,
Theta (θ) = T1/ Tref (Dimensionless)
(1)
Delta (δ) = P1/ Pref (Dimensionless)
(2)
The subscript “1” refers to compressor inlet conditions. T ref and Pref are
arbitrary chosen values as a base temperature and base pressure upon which
all the operating variables either from OEM or site are compared. It is normal
to set reference values same as the most common site compressor inlet
80
conditions for temperature, pressure and the molecular weight controlling the
mass flow.
The flowchart for the methodology of the performance scaling is shown in
Chart 1.
The following are the equations used to refer the OEM and test data:
Treferred = Tn / θ
Preferred = Pn / δ
mreferred = W * √ (θ) / δ
PW referred = PW/ ( δ * √ (θ) )
Nreferred = N/√ (θ)
EEPreferred = EEP / 1
(3)
(4)
(5)
(6)
(7)
(8)
Where, n=1, 2
In this work, subscript “1” refers to inlet of compressor and “2” refers to the
compressor outlet. For an “n” set of site cases, there will be an “n” set of
Thetas (θ) and Deltas (δ).
The OEM data is normally supplied on the basis of actual inlet volumetric flow
rates. If the OEM/Site performance data are not available on mass basis, then
volumetric flow need to be converted to mass flow using the following
equation:
W=q.ρ=q.(P.M)/(z.R.T)
(9)
ρ , q, P, z and T are at compressor suction conditions. It is to be noted that
referring the mass flow to a datum temperature and pressure (equations 3 &
4) assumes the molecular weight do not change. If the molecular weight
change over years of operation, then correction must also be applied to
molecular weight change. The simulation programme HYSYS adjusts the
performance curves automatically with varying molecular weights by a push of
a radio button by the user.
4.3 Generation of actual performance curves for the site compressor by
successive iteration method
It was referred in the previous section that performance curves supplied by
OEM applies to a fleet of compressors rather than the particular compressor
supplied at site and therefore does not constitute the exact performance of the
supplied compressor. Furthermore, the curves have limited application as they
are for a specific set of inlet gas and environmental conditions. Hence it is
needed to apply the proceedings as soon as the compressor is supplied and
positioned at site for operation. The shift of performance due to degradation
81
applies to the whole family of curves from the highest RPM to the lowest RPM
but the magnitude of this shift is not the same for all speeds. A way of
minimizing error in shifting, is to carry out the testing for as wide a speed
range as practically possible and shifting the curves by iteration. This novel
method is fully described below and the accuracy of performance increase
with the number of iteration. These steps are described below:
1. Determine and apply δ and θ to both OEM and site data as described
in Chapter 4.2 and performed in appendix D so that performance is
referred on the same inlet conditions for all cases. This enables
performance comparison between OEM and site.
2. The referred speeds of OEM and Site must be the same. For this
purpose, produce several speed lines between the referred OEM
speed curves. Linear interpolation is valid for this purpose and
confirmed as being practiced by industry [Ref 52].
3. For each of n site test data points and N speeds (all referred) , carry
out the following starting from the highest test speed:
3.1) Start by placing a base site case on the map shown as a star
on PRn speed line in Graph 1. Compare the site Pressure
Ratio to OEM Pressure Ratio and deduce the scale factor
(SF). Shift the original OEM curve “Nn” by applying the scale
factor so that the site point falls exactly on the modified OEM
curve. The parameter variable on y axis may not be only PR
but also be any other parameter that map out the compressor
characteristics depending on whether a suitable range of
information has been supplied by OEM and there are
readings taken at site or accurate simulation tools or
mathematical tools to deduce the equivalent at site.
3.2) Apply the same SF obtained in 3.1 above to all the Lower
OEM referred speed curves (N n-1, N n-2, …) and shift the
curves accordingly, as shown in Graph 1. The new modified
OEM speed curve matching the site data is now fixed in
position and will not move in the subsequent lower speed
curves fitting.
4. Finally there will be a performance map that has a set of test data
points falling exactly on the modified OEM speed curves. There are no
limitations on the number of curves.
82
For the range of speed curves that no test data are available, the last set of
shifted OEM speed curves shall be applied for performance evaluation.
Chart 2 defines the procedure for the generation of actual performance curves
for the site compressor by successive iteration method.
Defining the above procedure mathematically, for an “n” number of test data,
there will be an “n” number of speed curve shift for each iteration “i”. The
higher the “n”, the more the shifting iteration and the more accurate is the
prediction of performance, even outside of the tested speed ranges although
the proposed method gives very accurate performance values for the tested
speed range.
(I) For each measurable parameter and for the 1st shifting iteration (i=1) at the
highest site speed:
Nref,n=1
SF(n=1) = [XSite / XOEM ]n=1
(10)
Where
SF(n=1) = Scale Factor for the 1st test point
X= variable (pressure ratio, dimensionless mass flow rate, polytropic
efficiency, etc. at speed Nref,n=1)
(II) Shift all the speed curves of Nref and lower OEM speed curves by SF 1.
(III) The test point for Nref,n=1 will fall exactly on the OEM modified speed
curve. This point will remain FIXED in the following iterations.
(IV) Repeat (II) and (III) for lower speed successively.
(V) Finally:
SF(n=i) = XSite, n / XOEM, n
(11)
Equation (11) shows that number of successive shifting iterations is equal to
the number of test points at different speeds. For example, for three test data
points at different speeds there will be 3 scale factors.
The developed method above shall be applied for the site compressor
described in Chapter 6 and full sample calculation is laid out in Appendix D.
83
Referred PR
Original OEM
urve
Modified OEM Curve
A Site Data Point
Nn
N n-1
PR 1
N n-2
1
PR n
PR n-1
PR n-2
Referred Flow rate
Graph 1. Performance scaling method matching the OEM and Site data
84
Obtain sets of Site performance data
as base cases: flow rate, molecular
weight, inlet P/T, outlet P/T, rotational
speeds, shaft power, etc.
Choose a suitable datum of inlet P&T
and calculate θ and δ for each site base
cases
Apply θ and δ for each available test.
The referred INLET P and T and
molecular weight will be the same for
all site base cases enabling the
comparison of test results for outlet
measurable parameters
Obtain OEM performance data curves (pressure
ratio, discharge temperature, shaft power,
efficiency, ploytropic head, etc) for a defined set of
inlet conditions that are the same or close to site
inlet conditions
Choose a suitable set of OEM data and calculate
OEM θ and δ
Apply OEM θ and δ to the performance curves and
thus the OEM performance curves become referred
data, i.e. independent of inlet conditions.
Superimpose the referred site data on the referred
OEM curves. If the referred rotational speeds of
OEM do not match the site referred rotational
speeds, interpolate between the closest OEM curves
and draw new curves on OEM performance maps
Dimensionless and actual performance maps are
established for the site compressor in the range of the
tested rotational speeds
Chart 1. Flow Chart for establishing the actual site compressor performance
by scaling
85
Obtain set of referred OEM & site
test data ( Chart 1)
For each available test, n, starting at
highest referred test speed, N,
nn
NN
Compare the referred actual (site)
and expected (OEM) performance,
and shift the referred OEM curve to
overlap with the test point and
deduce a Scale Factor. Fix the test
speed point in position and Shift all
the lower curves by the same scale.
n n+1
NN-1
The speed curve on the previous run
stays in position. Move to next test
data point at the lower speed, get a
new scale factor and shift the
remaining speed lines. In each new
iteration, the highest speed curve is
fixed in position and only the lower
speed curves are shifted
Repeat the above procedure until all
test data are exhausted. Thus a new
set of performance maps are
established which are actual by the
application of site data successive
iteration. The map covers the speeds
outside the tested range
Chart 2. A novel flow chart for establishing the actual site compressor
performance maps by successive iteration method
86
4.4 Trend analysis in compressor diagnostics
Accurate trend analysis on compressors can be very difficult as the operating
point and even the gas analysis change although at a very slow paste. So
how can the trend be evaluated. One method that has been used with good
success is plotting the percentage change of a parameter (independent
parameter in our case such as PR or Efficiency or mass throughput) to a
known base line. It is important to establish the base line and in general
predicted performance curve is used and this adjusted in accordance with the
field data such as inlet conditions, speed and so on. For compressor efficiency
the changes are plotted against timer as shown on Figure 23A [Ref. 28].
As referred in the earlier sections compressor degradation causes the
performance curves move downward and to the left due to polymer build up,
dirt, corrosion, increased seal wear and greater restriction to process flow in
general due to fouling. The efficiency is reduced because of increased
frictional losses and/or increased internal recirculation (wear, rubbings,
clearances, etc).
The best trending data is when the data curves are densely populated with
relevant changes in parameters. However, these data may not be available all
of the time or continuously due to process restrictions or upsets. A way of
overcoming this issue, is to request the manufacturer (OEM) upfront to
analytically predict the relative effect of fouling. By plotting several degrees of
fouling on a speed-compensated plot, it is easy to distinguish the trend of
fouling. The degree of fouling is plotted versus time with reasonable
confidence and prediction of timing for maintenance is simplified [Ref 28].
Figures 23A and 23B demonstrate the required plots for the expected trends
in performance degradation by the OEM [Ref 28].
Trending the key performance parameters as continuously as possible with
time has the advantage of observing the performance in real time. Given a
time laps of sufficient period, the actual trends in performance against an
expected trend appear and the operator can decide in advance the required
actions such as inspection, cleaning, repair, part replacement or overhaul.
87
% ∆ Eff
Trend Analysis of a compressor performance
Change in efficinecy Versus Time
Base Line
0
-5
-10
Time
Figure 23A. Trend analysis of compressor performance: fall in efficiency with
time against a base line [Ref. 28]
Trend Analysis of a compressor performance
Effect of fouling on compressor - to be issued by OEM
DO
Head/N2
O
C
B
O
O
E
O
O
A
1
F
2
3
4
Flow/N
Figure 23B. Plot of performance data by OEM to map the expected
degradation [Ref. 28]
88
Trend Analysis of a compressor performance
Compressor Fouling Severity with Time - to be issued by OEM
O
O
Fouling Factor
2
O
O
1
O
O
A
B
C
D
E
F
Time
(A, B, C,... denote time schedules from OEM - see Figure 23A)
Figure 23C. Plot of performance data by OEM to develop fouling factor curve
[Ref. 28]
89
5. Developed Methodology for Compressor Degradation
Estimation
5.1 Compressor Degradation Estimation
As compressor degrade, the whole performance maps shift. Thus
degradation is the shift of performance maps. The health parameters of a
compressor can be described by establishing degradation scale factors.
These health parameters are Pressure Ratio (PR), Polytropic Efficiency (η)
and Mass Throughput (m) where these parameters are independent from
each other and thus can fully define the characteristics of the compressor.
Degradation scale factor or health parameter is the ratio of each of the
referred independent variables (PR, η, m) under degraded condition relative to
the performance at un-degraded or clean conditions of the same compressor.
The shift of the performance maps normally follows a diagonal shift to the
bottom and to the left (i.e., south west), i.e., degradation is expected to result
in not just a drop in PR (primarily as a result of wear in seals and increase in
clearances between moving and stationery parts) or in mass throughput
(mainly as a result of fouling and erosion/corrosion), but more realistically a
combination of both. However, in this research in order to explore the extreme
conditions, in addition to the realistic diagonal shift resulting from degradation,
it is proposed to have an entirely vertical drop as well as having an entirely
horizontal shift to the left as a separate case and gauge the differences in
degradation prediction.



For a vertical performance drop on the compressor performance map,
degradation in PR is maximum and in m is zero,
For a purely horizontal shift to the left on the compressor performance
map, degradation in PR is zero and in m is maximum,
The real drop is often diagonal. This results in a degradation estimate
which is between the two extremes above. For an accurate prediction
of diagonal drop it is proposed to use Fan laws as will be explained
later as Fan laws are proven to be accurate especially for small
changes measured over small periods of time. For determination of
vertical drop and/or horizontal shift, it is proposed to directly measure
and record the drop or shift on the map.
As operation time progresses, it is expected that degradation will also be
propagating. In order to investigate the relationship between degradation and
time, the health parameters or degradation indices at various times during
compressor operation will be established. The performance curves generated
by the novel method of successive iteration method described in Chapter 7
above to the site compressor will be utilized. The building of the actual
90
performance curves for the “clean” compressor ought to have been done at
the start of its operation in 2004, but it was not carried out. Hence the
operational data post 2004 that are available will be utilized to build the actual
performance maps of the “clean” compressor (the “clean” performance is the
first generated performance curves or base line performance) complemented
by the advanced simulation programme HYSYS (see Appendix B). Once the
“clean” or un-degraded compressor performance maps have been built, the
available site performance data from mid 2006 onwards (Appendix C(I) and
C(II)) shall be applied to obtain the compressor’s degraded health parameters
(pressure ratio, polytropic efficiency and throughput). These
health
parameters will then be compared with the clean health parameters obtained
as described earlier and establish the degradation scale factors at various
time and record the trends on graphs.
The GPA Index for the above will be established as defined in Chapter 7, to
gauge the accuracy of predicted values by simulation compared with actual
site data. The closer the GPA Index is to 1, the more accurate the predictions.
This will followed by a series of sensitivity analysis using the advanced
simulation programme HYSYS to gauge how degradation affects the
measurable parameters.
The followings give the details of the approach:
The shift in performance due to performance degradation may be represented
as in Figures 24 and 25. Degradation causes a fall in performance and an
erosion of surge margins, i.e., the shift of performance curves shall be
downwards and to the left. Increase in clearance spaces is a natural
phenomena and will cause the compressor performance curves move
downward. Degradation due to fouling creates changes in its performance and
later on it may change the blade shape if fouling is allowed to become severe.
The latter statement is especially true for axial compressors. For centrifugal
compressors fouling is mainly due to the build up of materials on the blades.
These will cause the shift of performance curves to the left. On Figures 24 and
25, the solid line represents the performance of clean compressor and the
dashed line may represent the performance of the degraded version of the
same compressor.
By comparing the operating point on its characteristic map when the
compressor is degraded (Point A on Figure 24A and AI on 25) with the
operating point on the same map for a clean (un-degraded) compressor (Point
B on Figure 24A and BI on Figure 24B) leads to the estimation of degradation.
The assumption here is that the degraded performance map keeps almost the
same shape as their original maps (i.e., linear approach) and this may be
justified by the fact that often the geometries are not changed significantly
91
after they are degraded [Ref. 14]. It will be attempted to improve this
assumption by the application of Fan laws as described in the proceeding
works. Fan laws are the laws of relation between old and new measured
parameters and are accurate, especially for small changes or frequent
readings. Thus compressor degradation is represented by the shift of the
characteristic curves on the respective maps and such shifts are represented
by degradation or health indices. For a compressor the primary performance
indicators are pressure ratios (PR) and efficiencies while the compressor
health status represented by PR, efficiency and flow capacity indices [Ref 14].
These will determine the measure of performance shift and can describe the
compressor degradation. Based on these definitions, the following relationship
exists as shown earlier in Chapter 3:
SFfl = m deg / m clean
(1)
SFPR=PRdeg / PRclean
(2)
SFeff = ηdeg / ηclean
(3)
Where,
SF=Scale Factor
m=mass throughput
PR=Pressure Ratio
η=Polytropic Efficiency
And subscripts fl, deg and c represent flow, degraded and clean compressor
respectively.
Reference to Figure 24A, Point A is the starting position and refers to the
conditions of an un-degraded or clean compressor performance at m clean on
the X-Axis and PRclean on the Y-Axis and B refers to the referred
corresponding points on X and Y axis but at degraded conditions, i.e.,
mdeg
and PRdeg respectively. Point A may be obtained from OEM when the
operator request the OEM to perform a full set of performance tests for a
wide range of RPMs once the compressor arrives at site thus the actual
performance curves specific to the supplied compressor are available. In case
these are not done or data not available, at the earliest opportunity the
operator needs to establish the actual performance curves of the compressor
by performing performance tests for a wide range of RPMs on his own accord
in the manner described under Chapter 4. These data then become the
performance data for the “clean” compressor and the subsequent tests will be
for the degraded compressor. All performance data are referred to the same
datum by the application of Theta (θ) and Delta (δ) to eliminate the influence
of environmental changes and gas inlet conditions on the compressor
performance and thus the fall in measurement will be solely due to
92
degradation and not a combination of degradation and environmental
changes. It is to be borne in mind that speed cannot be scaled thus point A
and point B refer to the same referred speed.
Figure 24B is similarly explained as Figure 24A, Point AI is the starting
position and refers to the conditions of an un-degraded or clean compressor
performance at m clean on the X-Axis and ηclean on the Y-Axis and BI refers to
the referred corresponding points on X and Y axis but at degraded conditions,
i.e., mdeg and ηdeg respectively.
Degradation in compressor performance is normally associated with small
changes in key performance indicators as the operator is not prepared to
observe the fall in performance and profitability without taking proactive
action. Thus Fan laws apply whenever necessary and are especially accurate
for the small changes in the designated compressor under observation for
degradation. These laws may be used to confidently estimate the degradation
in mass throughput, as mass throughput (m) is related to q which is in turn
proportional to compressor speed and pressure ratio as shown below:
q α N, Hp α N2, ℓn PR α N2, ∆T α N2, PWR α N3
(4) [Ref 28]
Alternatively, the clean performance curve (point A on Figure 24A) can be
manually shifted until it hits the point on the degraded performance curve
(point B on Figure 24A) since both the degraded mass throughput and PR are
known. In this manner the degradation in throughput is indirectly evaluated.
The degradation indices described by equation 1 to 3 above are the ratios
between the values of degraded (dotted lines) and original performance
curves (solid lines). These indices are independent from each other.
Reference to Figure 24A, the performance of a clean compressor at a
particular throughput, speed and pressure ratio/efficiency is represented by
point “A” on the map and after some time the corresponding degraded
performance is shown by point “B”. It is to be noted that rotational speed can
not be scaled in the same manner described by equations 1 to 3 in this
section for a clean and degraded compressor. In other words, the analysis
between clean and degraded compressor should based and compared for the
same speed.
For the compressor under observation there are available measured
compressor speeds, flowrates, pressure ratios, efficiencies and a host of other
measureable parameters such as inlet/outlet temperatures and power
consumption reported electronically to DCS (distributed control system room)
as well as hand written from field instruments once every 4 hours. All the
instruments are regularly calibrated with online self diagnosing (fault finding)
capabilities. In order that the gas path measurements between clean and
93
degraded conditions are comparable the measurements shall be “referred” to
a common datum by the application of theta (θ) and delta (δ) to all the
parameters. In this manner the environmental and gas inlet variations on
compressor performance degradation are taken out of the equation and errors
due to instrumentation are minimized so that, overall, the changes in
performance values are significantly due to compressor degradation only
rather than true compressor degradation lumped with environmental changes
and instrument noise and bias.
As it was stated earlier in this section the result of degradation is the shift of
performance curves downward (due to increase in clearances as a result of
rubbings between stationary and moving parts) and to the left (due to fouling).
Reference to Figures 24 and 25, the shift angle A°D (a new terminology
introduced here) is not the same for all compressors and the magnitude
depends on the thermodynamic properties of the fluid going through the
compressor as well as the compressor loading (partial or full) and the
physical geometry of the compressor [Refs 16, 17].
The method of health estimation or measure of degradation presented above
takes a “snap shot” of the compressor health situation, i.e., analyzing the data
as and when deemed required.
The proposed procedure for an accurate estimation of compressor
performance and health status is shown in Chart (3) and this has been carried
out for the site compressor whose data are included in Appendix C.
Referring to Table C2A in Appendix C (I), base case 5 was chosen for the
proposed health estimation because this base case happens to have the
same referred speed as the “clean” compressor. This procedure is
recommended for all future works of similar nature. In case the referred speed
at test point for the degraded compressor is not the same as that of the same
compressor at clean conditions, it is perfectly acceptable to interpolate
between two referred speeds at clean conditions for speed matching. The
validity of interpolation method is confirmed [Refs 52, 53].
5.2 Compressor health estimation and GPA Index
In order to express the confidence level in the prediction of degradation, a
GPA Index shall be set up defined as:
GPA index = 1/(1+ε)
(5)
Where ε is a measure of the difference between the measured and predicted
deviations of compressor gas path measurements and it is mathematically
expressed as:
94
(6)
∆Zi, measured / Zi, measured and ∆Zi, predicted / Zi, predicted are the measured and
predicted deviations of measurement Z i, respectively.
The GPA predicts the degradation of a predefined compressor fault case and
this information shall be sent to the model compressor to produce a predicted
measurement deviation. The comparison between the real measurement
deviation and the predicted is ε and then GPA Index shall be calculated. If the
pre-defined case is correct then GPA Index will be close to 1. Thus a GPA
Index approaching 1 indicates a very accurate degradation prediction while a
GPA Index close to 0 means the other way around. Figure 25 shows the
calculation process for GPA Index.
5.3 Sensitivity Analysis
Compressor degradation does not have equal effect on measurable
parameters variables. It is important to recognize which variables are sensitive
and which ones are insensitive to degradation so that maintenance items and
schedules are classified accordingly. Furthermore the nature of relation (linear
or non linear) between degradation and compressor dependent variables is
important to be established so that the future behaviour can be estimated by
extrapolation. GPA Indexing shall be carried out for the diagnostic method
developed above and a full range of sensitivity analysis shall be carried out for
the site compressor. These sensitivity analyses for the site compressor are
shown in Chapter 7, Section 7.
95
Clean compressor
performance at rpm “n”
Degradation Shift Angle A°D
Degraded compressor
performance at the
same rpm “n”
Pressure Ratio
Clean Performance
Degraded Performance
PR clean
PR deg
B
.A
.
N clean
mdeg mclean
Mass throughput
Figure 24A. The effect of compressor degradation on PR performance
96
Clean compressor
performance at rpm “n”
Degradation Shift Angle A°D
Polytropic Efficiency
Degraded compressor
performance at the
same rpm “n”
η clean
η deg
.
.
AI
BI
N clean
Clean Performance
Degraded Performance
Mass throughput
mdeg mclean
Figure 24B. The effect of compressor degradation on Compressor Efficiency
97
Obtain the thermodynamic model
of the clean compressor from
OEM as “expected” performance
or obtain the curves from the last
field tests used as clean
reference. In the absence of
these, create model by the
application
of
advanced
simulation tools
Record the degraded measurable
parameters from the field to
obtain the current or “actual”
performance
to
estimate
accurately
the
degraded
compressor performance. All
performance data
must be
referred to a common datum.
Compressor
Measurements
Gas
Path
Estimate compressor degradation
by comparing clean and degraded
performance to establish the
current degradation indices of
governing
indicators
of
compressor health
Chart 3. Procedure of Compressor Performance and Health Status
Figure 25. Calculation process for GPA Index
98
6. Application of compressor degradation modeling by
simulation
To begin the work, a range of flows versus polytropic heads for 100% speed
should be available. The sources of availability are OEM data for the same
compressor and process gas properties. If the gas properties and the
compressor under research are similar to a rated case from OEM at a given
speed, equation 3-14A is used to calculate the pressure ratios. Alternatively, a
range of polytropic heads (Hp) from equation 6 or a range of pressure ratios
(PR) from equation 3-14B, at a given speed, are calculated which will
subsequently be used to construct the compressor performance maps.
6.1 Generation of a compressor map for the clean (un-degraded)
compressor
For the purpose of simulations, to generate a compressor map, two options
are available:
1) Use any compressor map to start the works which is the current
practice at the department of Engineering of Cranfield, or,
2) Generate a representative site compressor map.
It is obvious that the latter is superior as it enables the user to generate
specific and representative performance curves tailor suited for any
compressor and therefore the method has a much wider and useful
application. In order to generate a representative site compressor map one of
the following may be carried out:
A) Search for a single rated (OEM) supplied speed curve that
resembles the site compressor gas and inlet conditions. This is
shown in Table 4. Apply Equation 3-14A in Chapter 3 to generate
modified or new pressure ratios for the given (100%) speed. Fan
Laws as described under Chapter 3, Section 3.5.1 are then used to
obtain the polytropic heads and throughputs for other speeds and
this is followed by applying the rest of the equations in Chapter 3 to
obtain the other performance parameters such as H p, discharge
temperature and power.
B) For a defined inlet conditions and series of assumed throughputs
and pressure ratios (PR), use Eq. 3-13 to calculate the polytropic
heads. Alternatively assume Polytropic head for each flow and use
equation 3-14B to calculate pressure ratios. Apply affinity (Fan)
Laws in Chapter 3, Section 3.5.1 to obtain the polytropic heads and
throughputs for other speeds. This is followed by applying the rest
99
of the equations shown in Chapter 3 to obtain the measurable
parameters such as discharge temperature and power.
In this work, the polytropic head for a range of flows at 100% speed is
calculated using the equation (3-14A) modifying the OEM (rated) compressor
and gas data to site (new) gas data. Reference to Table 4, it will be noted that
polytropic efficiency is taken as a constant value. In real terms polytropic
efficiency varies with flow rate. However, with superior design capabilities of
the OEM, compressors hold a fairly constant and high value for a wide range
of flowrates and deviate from the peak only at extremely low or extremely high
flowrates which under the normal circumstances, operator does not operate in
these regions.
3) The volumetric flowrate & polytropic head values are transferred into
the spreadsheet specifically developed for generation of performance
curves for other speeds based on proportionality (Fan) laws as
described in Chapter 3, Section 3.5.1 and thus the flows and heads for
other speeds are deduced. It will be noted that for each lower speeds,
the performance curve is shifted to the left of the curve and the flow
range is reduced. This is shown in Table 5.
4) For each set of Hp versus volumetric throughputs at defined speeds
obtained above, equations (3-11), (3-14A), (3-15) and (3-16) are used
to calculate discharge temperature, pressure ratio, power and mass
throughput and these are shown in Table 6. These are graphically
shown on Figures 26A –26E for the clean or un-degraded compressor.
100
Gas
Site
Properties
Conditions
Site
Hydrocarbon
24.60
10.7
315.3
0.95
1.20
0.85
5.10
Rated
Conditions
(OEM)
A
Hydrocarbon
24.88
10.06
303.85
0.96
1.236
0.85
4.45
6.00
5.24
Mol. Weight
Suction Pressure
Intake Temperature K
Compressibility
Cp/Cv
Poly. Eff.(at rated flow)
n/(n-1)
k/(k-1)
Volumetric
Flow Rate
Pressure
Ratio (Rated)
m3/h
10000
11000
12000
13000
14000
15000
15500
16000
16500
17000
Pd/Ps
3.83
3.79
3.72
3.62
3.49
3.31
3.18
3.04
2.87
2.70
Table 4. Available gas properties and basic rated performance data for a
compressor
101
Table 5. Generation of speed curves based on Affinity (proportionality or Fan)
Laws
102
Table 6. Calculation of un-degraded compressor variables for all speeds
103
Performance Curve Generation (Undegraded)
Data for 9500 rpm supplied. Fan Laws applied for other speeds
180
160
Polytropic Head - Hp [kJ/kg]
140
120
100
80
60
Surge Line
6500
7000
8000
9000
9500
Stone Wall Line
40
20
0
5000
7000
9000
11000
13000
Flow [Act_m3/hr]
15000
17000
Figure 26A. Compressor performance generation, polytropic head versus
volume flow
Compressor Performance Curve (Undegraded)
Polytropic head versus Mass Throughput
180
160
Polytropic Head - Hp [kJ/kg]
140
120
100
80
6500
60
40
7000
8000
9000
20
0
40000
9500
60000
80000
100000 120000 140000 160000 180000 200000
Mass Flow [kg/hr]
Figure 26B. Compressor performance generation, Polytropic head versus
mass flow
104
Compressor Performance Curve (Undegraded)
Pressure ratio versus Mass Throughput
4.00
Pressure Ratio- PR [-]
3.50
3.00
2.50
6500
2.00
7000
8000
1.50
1.00
40000
9000
9500
60000
80000
100000 120000 140000 160000 180000 200000
Mass Flow [kg/hr]
Figure 26C. Compressor performance generation, Pressure ratio versus
mass flow
Compressor Performance Curve (Undegraded)
Discharge temperature versus Mass Throughput
150
Discharge Temperature - T d [ °C ]
140
130
120
110
100
90
80
70
60
50
40000
6500
7000
8000
9000
9500
60000
80000
100000 120000 140000 160000 180000 200000
Mass Flow [kg/hr]
Figure 26D. Compressor performance generation, Discharge temp versus
mass flow
105
Compressor Performance Curve (Undegraded)
Gas power versus Mass Throughput
8000
7000
Gas Power - PWR [ kW ]
6000
5000
4000
3000
6500
7000
2000
8000
9000
1000
0
40000
9500
60000
80000
100000 120000 140000 160000 180000 200000
Mass Flow [kg/hr]
Figure 26E. Compressor performance generation, Gas power versus mass
flow
6.2 Generation of a compressor map for the degraded compressor
When a compressor degrades, the performance curves shift down and
generally to the left. These shifts translate into a reduction in dependent
parameters including pressure ratio (for a fixed inlet pressure), efficiency and
throughput. Thus if the clean un-degraded compressor is giving a pressure
ratio of 3.75 at a flow of 10,000 m3/hr and 9500 rpm (Table 6), under
degraded conditions it will give a PR smaller than 3.75 at the same rpm. Thus
in order to keep the PR the same as un-degraded, the compressor has to
increase the speed beyond 9500 rpm (rerate) demanding more power from
the driving turbine. The increase in rpm may be beyond the design margin of
the compressor (normally capped at 5% above maximum continuous speed)
or that the availability of excess power supply may be the limiting factor. An apriori data analysis is required so that allowable ranges or tolerable regions of
compressor degradation are mapped out in advance of the project
implementation by the operators and thus the frequency of maintenance is
predicted.
The performance curves for the clean compressor were developed in the
previous section. In this section, the curves for degraded compressor shall be
developed and the rerates shall be determined.
The criteria chosen for evaluation of degradation are:
106
A)
B)
C)
D)
5.0% Deg in PR, No Deg in η, No Deg in m
5.0% Deg in PR, 2.5% Deg in η, No Deg in m
5.0% Deg in PR, 2.5% Deg in η, 5.0% Deg in m
10.0% Deg in PR, 5.0% Deg in η, 10.0% Deg in m
Table 7 shows the performance parameter values for all the above cases and
the rerates required at 100% speed. Affinity laws (Equations 3-18 to 3-21) are
used to calculate the polytropic head & rotational speeds under degraded
conditions as well as for the rerating. Table 8 shows the calculated polytropic
heads for other speeds. Referring to Table 7, for a 10% degradation in PR,
the compressor curve at 100% speed falls from 9500 rpm (clean or undegraded compressor) to an equivalent of 9290 rpm. Thus for this degraded
compressor to give the original discharge pressure, it has to run at the speed
of 9715 rpm under degraded condition to give the equivalent of 9500 rpm of a
clean or un-degraded compressor and as a result the power demand by
compressor increases and as a result if this surge in power demand is
combined with high ambient temperatures and/or efficiency degradation, the
operator may face operational limitations or even face the inability to deliver
the gas as per the specification. It shall be noted from the same Table 7 that
for this compressor the design limit in terms of maximum allowable continuous
speed is reached well before 10% degradation in PR. The power requirement
is also a sensitive function of state of the compressor which if left unattended,
the demand increase in supplied power may be the limiting factor for allowed
level of compressor fouling.
Figures 27, 28 and 29 show the remarkable effect of degradation on
compressor performance. The effect of degradation on gas power demand
has also a very profound effect even for small degradation levels and this is
shown on Figures 30A and 30B.
107
Table7. Degradation modeling of a compressor at 100% speed (Table 8 for other
speeds)
108
Table 8. Generation of degraded performance curves using affinity laws
109
Figure 27. Compressor performance at various degradation levels (values from
Table 7)
Figure 28. Performance prediction for degraded compressor and the required
rerate
110
Figure 29. Compressor performance rerates for various degradation levels
(Table 7 for values)
111
Figure 30A. The variations in gas power demand due to degradation (high
speeds)
Figure 30B. The variations in gas power demand due to degradation (low
speeds)
112
6.3 Degradation Simulation Test Case
The clean or un-degraded compressor map generated in the previous Section
was fed into HYSYS in terms of polytropic heads and volumetric flows (Figure
26A). For the purpose of this work a single operating point at 100% speed is
used at a predetermined (assumed) rate of degradation in flowrate and
efficiency respectively with time. The head curves for the clean compressor as
entered into HYSYS are shown in Figure 31. The single operating point is
shown on the same Figure.
For the analysis of degradation at various speeds, HYSYS also requires the
corresponding polytropic efficiencies if multiple efficiency points are to be
used in the degradation analysis. By observing many compressor
performance curves using advanced design tools, it is noted that the efficiency
remains more or less constant, regardless of speed, except for extremely low
or extremely high flowrates, in the range of which the operator is unlikely to
operate to order to avoid surge or stonewall. The efficiency curves are thus
generated unless actual efficiency curves are determined under compressor
testing conditions. The efficiency curves for the clean compressor as entered
into HYSYS are shown in Figure 32.
Figure 31. Polytropic Head versus Flow for clean compressor in HYSYS
113
Figure 32. Polytropic Efficiency versus Flow for clean compressor in HYSYS
Once the clean compressor commences operation, the performance start to
degrade and the degradation manifests itself in terms of measurable output
following a characteristic trend. The rate of degradation with time may be
linear or non linear. In order to investigate the effect of degradation
mechanism on measurable compressor parameters, both linear and non
linear degradation patterns were assumed over time.
1) A linear degradation pattern is assumed over a period of one year from
a known compressor operation. Total no. of samples is 12, i.e., one
taken per month. It is taken that flow capacity degrades (reduces) 5%
and efficiency falls 3% over the year. This is graphically and
numerically shown below in Figure 33 and Table 10A.
2) A non-linear pattern is assumed with time over the same period of 1
year from a known compressor operation. Total no. of samples is 12,
i.e., one taken per month. It is taken that flow capacity degrades
(reduces) 5% and efficiency falls 3% over the year. This is graphically
and numerically shown below in Figure 33 and Table 10B.
The compressor inlet conditions of the throughput gas for which the HYSYS
simulations were carried out is described in Table 9. Keeping the Pressure
Ratio constant as an operational constraint, HYSYS was run at each
degraded point as shown on Figure 33 to obtain and record the degraded
discharge temperature (T2), power (Gp) and rotation speed (N). As the
compressor inlet pressure remains unaltered, constant pressure ratio implies
the discharge pressure is constant.
The numerical results are tabulated in Tables 10A and 10B for linear and nonlinear degradation respectively. From statistical point of view a larger number
of samples would have been advantageous so that the robustness of analysis
is increased using data averaging shown under Chapter 3, Section 3. From
114
practical point of view, however, the frequency of compressor measurements
is only once in while, although there is a growing trend to increase the
performance checking periods to access the compressor performance in real
time. Hence 12 samples have been identified over the 1 year test period and
all are used for HYSYS analysis and a larger number of samples would not
have changed the trend, observations or results.
The graphical results are shown on Figure 34. Reference to this Figure, it is
seen that at time 0 the compressor is taken as un-degraded or clean and at 6
month after initial operation, there is remarkable difference between the
performances at this time and these differences are tabulated on Table 11.
The dotted lines on Figure 34 shows the health of the compressor after 6
months of operation based on degradation indices (SF FC and SFEff) discussed
in Chapter 3, Section 2 earlier. It is seen from Figure 34 that at 6 months
period, a 0.9% variation in power demand, corresponds to 1.5% degradation
in efficiency for a linear degradation pattern. In general, it can be seen that
linear degradation pattern produce nearly linear changes in measurable
compressor parameters. Likewise for non-linear degradation pattern, there will
be a non-linear pattern in measurable compressor parameters. Describing the
current health condition by degradation indices is clearly a powerful tool which
should be used for future applications as well.
Figure 35 shows the differences in measurable and non measurable
compressor parameters after 6 months of operation for both linear and non
linear degradation patterns taken from HYSYS. Although the final mass and
efficiency degradation at the end of 1 year period are taken as the same for
both linear and non-linear, it is evident that for in-between periods, each
pattern of degradation induces different effects on measurable parameters.
In real field life, most degradation mechanisms are likely to be non linear and
therefore more frequent operational data gathering and analysis for predictive
maintenance is necessary.
6.4 Application of Scale Factors for diagnostics
Upon performance degradation, the performance curves shift to a new
location on the performance maps. If an operating point on the clean
performance curve can be considered, upon degradation, in the extreme
sense it can be shifted in two distinctive ways until it lies on the degraded
performance curve: pure vertical or pure horizontal shifting. For the map of
power versus mass throughput, the horizontal shift considers no degradation
(increase) in power demand and assumes the total degradation is due to the
mass throughput component only. Like wise, for a pure vertical shift, until the
operating point on the clean and original curve hits the degraded curve, the
entire mass degradation component is taken out and it is taken that the
115
degradation is due to efficiency drop only. This practice is useful because it
gives an estimate of the maximum shift in compressor health indicators such
as mass and efficiency indices. The operator can simply take compressor
measurements and estimate the current health of the compressor relative to
when it was new by indirectly evaluating the drop in mass throughput and
efficiency as the health indicators.
In this study, the measurements are the discharge temperature, power and
speed and the compressor health indicators are mass throughput and
efficiency health indices. Reference to Figure 34, the operator may take the
direct measurements of discharge temperature, power and shaft speed and
apply them to estimate the drop in throughput, pressure ratio and efficiency
relative to when it was new which are the health indicators of the compressor.
Also in this study it is taken that:
(1) The rise in discharge temperature is primarily due to drop in polytropic
efficiency, i.e., there is a relationship between the dependent
(measured) parameter (temperature) rise and the independent
parameter (polytropic efficiency).Or,
(2) The rise in compressor power demand is primarily due to drop in
polytropic efficiency, i.e., there is a relationship between the dependent
(measured) parameter (power demand) rise and the independent
parameter (polytropic efficiency).
Both the above points are manifested in the pure vertical shift of the
performance curve, i.e., the horizontal component (mass throughput) taken
completely out. For the case of power, reference to Eq. 3-15 in Chapter 3, the
power demand is directly proportional to mass and inversely proportional to
polytropic efficiency. For a vertical shift, mass change due to degradation is
zero and thus for constant polytropic head, the power demand rise will be due
to drop in polytropic efficiency only. From Chapter 3, Section 2 earlier it was
stated that:
SFF = FCdeg/FCcl
SFPR=PRdeg/PRdeg
SFEff= ηdeg/ηcl
In this study PR is taken as constant as an operational constraint. This leaves
scaling flow and efficiency as health indicators of the compressor. Scaling the
flow is straight forward since it explicitly appears on the X axis of performance
curves. The drop in polytropic efficiency may be taken as a scale of discharge
temperatures of degraded compressor after six months of operation over the
new or clean compressor at time zero. This is the vertical component on
Figures 36 and 37 and the numerical and graphical results are shown on
Table 12 and Figure 40.
116
The drop in polytropic efficiency may also be taken as a scale of power
demand of degraded compressor after six months of operation over the new
or clean compressor at time zero. This is the vertical component on Figures
38 and 39 and the numerical and graphical results are shown on Table 13
Figure 41.
On comparing Figure 41 with Figure 40, it can be clearly seen that error in
prediction of mass and efficiency drops are greatly reduced when power
measurements are take as the basis for health indication rather than
temperature increase.
Another important measurement which may lead to estimation of mass and
efficiency drops as health indicators is the measurement of rotation speed
variation. However, as far as speed is concerned, reference to Equations 3-18
(a) to (e), increase in speed requirement is a lump function of several
variables which will need further investigations.
Figure 33. The throughput and efficiency degradation trend assumed over a
year
117
Gas
Properties
Mol. Weight
Suction Pressure (Bara)
Intake Temperature K
Flow (kg/hr)
Flow (MMscfd)
Gas
Conditions
24.60
10.7
315.3
125,625
102.4
Table 9. Compressor inlet condition in HYSYS for degradation investigation
Table 10A. HYSYS input and output for linear degradation
Table 10B. HYSYS input and output for non-linear degradation
118
Figure 34. HYSYS input and output for linear and non-linear degradation
119
Parameter
Unit
m
ηp
T2
N
Gp
Kg/hr
C
rpm
kW
Clean
(time 0)
125,628
86.1
136.2
9500
6031
Compressor degraded “actual” measurements
Relative to
NonRelative to
Linear
clean
Linear
clean
122,487
84.8
137.5
9475
9583
-2.5%
-1.5%
+1.0%
+0.3%
+0.8%
120,226
84.2
138.2
9441
5917
-4.5%
-2.3%
+1.5%
+0.6%
+1.9%
Table 11. HYSYS output taken as ‘measurements’ six month after initial
operation
Figure 35. Comparison of ‘actual’ measurements for linear and non-linear
degradation
120
Figure 36. Performance curve (T2 vertical/Horizontal) shift - linear degradation
case
Figure 37. Performance curve (T2 vertical/Horizontal) shift – Nonlinear
degradation case
121
Figure 38. Performance curve (Gp vertical/Horizontal) shift - linear
degradation case
Figure 39. Performance curve (Gp vertical/Horizontal) shift - Nonlinear
degradation case
122
Table 12. Predicted (by scaling) and Actual degradations using discharge
temp. rise
Table 13. Predicted (by scaling) and Actual degradations using power
demand rise
123
Figure 40. Compressor diagnostics based on discharge temperature
measurements (Figures from Table 12)
Figure 41. Compressor Diagnostics based on power measurements
Table 13)
124
(Figures from
7 Novel Scaling Method and Diagnostics Applied to Site
Compressor
7.1 About Site Compressor and Instrumentation
The overview of plant is shown in Figure 42. The wet process gas form
various wellhead sources get conditioned through separators and knockout
drums, get compressed through 3 stages of compression so that the
discharge pressure is sufficiently high enough to be transferred through
pipelines to a stripping plant. The site compressor under observation is the
3rd stage compressor (14K-5201) and its position within the plant is as shown
in Figure 42. A typical input/output of the compressor from the DCS
(distributed control system) in the control room is demonstrated in Figure 43.
The compressor model is MCL525 (Nuovo Pignone) and is powered by Solar
Turbines, Mars 100 model. The compressors were put into commission in
2004 and since that time up to now there has been no compressor overhaul.
For the compressor under observation there are available measured
compressor speeds, flow rates, pressure ratios, efficiencies and a host of
other measureable parameters such as inlet/outlet temperatures and power
consumption reported electronically to DCS as well as hand written from field
instruments once every 4 hours. All the instruments are regularly calibrated
with online self diagnosing (fault finding) capabilities. Although measurement
noise is inevitable but its interference with sensor accuracy is minimized
diminishing the reading errors.
125
Figure 42. The gas gathering and compression overview showing the position
of 3rd stage compressor within the plant
126
Figure 43. A snapshot of the DCS (distributed control centre) room showing
the typical input and output to and from the 3rd stage compressor
127
7.2 Performance Adaptation by Successive Iteration
In Chapter 4 it was stated that the compressor should ideally be tested at site
by OEM as early as it is moved to site and for as wide a speed range as
possible in order to establish the actual site compressor performance. This
performance mapping also represents the performance of a clean or undegraded compressor. If this actual performance mapping is not done at the
start of the operational life, then it should be done as early as possible. With
the latter scenario, the issue here is that production has already started and
the operator is already committed to deliver the process gas under predefined
conditions to the downstream facilities or end users, hence testing the
compressor under a wide range of speeds is difficult while on-line.
For the site, the compressor was not tested at full range of speeds at the
beginning of its life to map the actual performance of the clean compressor.
The earliest set of available operational data (early 2006) at various speeds
(see Appendix D) were identified and treated through referring followed by
successive iteration method in accordance with Chart 2 in Chapter 4 to
deduce accurate performance maps of the compressor and this is shown in
Graph 2. Appendix D contains the full sample calculations. Graphs 2 and 3
show the build up of performance information in pressure ratio and polytropic
efficiency versus mass throughput.
Graph 2. Performance Adaptation by Successive Iteration for Pressure Ratio
(In accordance with Chart 2 and operational data and treatment in Appendix D)
128
Graph 3. Performance Adaptation by Successive Iteration for Polytropic
Efficiency (In accordance with Chart 2 and operational data and treatment in Appendix D )
Tables 14-19 show the expected (OEM) values and actual (site) values for
compressor performance (pressure ratio, efficiency and discharge
temperature). Graphs 4-66 demonstrate the errors before and after the
adaptation technique for pressure ratio, efficiency and discharge temperature
respectively. The analysis may be repeated for any required variable.
Referring to Graph 7, it shows the overlap of referred performance curves for
varied and extreme ambient conditions (Source data taken from OEM, Table
D15 in Appendix D) showing that generation of performance curves on
referred basis are independent of varied ambient conditions demonstrating
the wider application of dimensionless numbers in performance analysis. For
un-referred data, performance maps do not overlap.
To summarize all the above, the site compressor data at various RPMs (Ref.
Appendix D) were used in successive iteration method to generate accurate
performance data (performance adaptation) in accordance with Chart 2 in
Chapter 4. These adapted data were used as input block data into the
simulation programme HYSYS which utilized and completed these data for
the generation of polytropic heads and completion of accurate performance
models. Figure 44 shows the model in HYSYS and Graphs 8 and 9
demonstrate the actual and “clean” performance curves for the site
compressor in terms of polytropic head and efficiency.
129
Mass Flow,
OEM (-)
Site (-)
Scale Factor
Nref
ref(kg/hr)
150,400
0.996
3.27
2.93
0.896
140,499
0.942
2.468
2.76
1.118
122,227
0.928
2.68
2.99
1.116
Table 14. Generated Pressure Ratio Scale Factors (Appendix D for Calculation Details)
Mass
Nref
PR
PROEM
PROEM
Error
Error
Before
Adaptation
Before
After
After
Adaptation
Flow,
Site
Adaptation
Adaptation
ref(kg/hr)
150,400
0.996 3.27
2.93
3.27
-11.6%
0%
140,499
0.942 2.47
2.76
2.47
+5.8%
0%
122,227
0.928 2.68
2.99
2.68
+10.4%
0%
Table 15. Pressure Ratios Before and After Adaptation (Appendix D for Calculation Details)
Mass Flow,
Nref
OEM (%)
Site (%)
Scale Factor
ref(kg/hr)
150,400
0.996
84.5
86.5
1.024
140,499
0.942
84.8
87.5
1.032
122,227
0.928
86.4
86.8
1.005
Table 16. Generated Polytropic Efficiency Scale Factors (Appendix D for Calculation Details)
Mass
Nref
EFFP EFFPOEM
EFFPOEM
Error
Error
Before Adaptation After Adaptation
Before
After
Flow,
Site
Adaptation
Adaptation
ref(kg/hr)
150,400
0.996 86.5
84.5
86.5
-2.4%
0%
140,499
0.942 87.5
84.8
87.5
-3.2%
0%
122,227
0.928 86.5
86.4
86.5
-0.1%
0%
Table 17. Polytropic Efficiencies Before and After Adaptation (Appendix D for Calc. Details)
Mass Flow,
Nref
OEM (-)
Site (-)
Scale Factor
ref(kg/hr)
150,400
0.996
401.0
398.2
0.993
140,499
0.942
395.0
394.6
0.999
122,227
0.928
402.2
401.9
0.999
Table 18. Generated Discharge Temp. Scale Factors (Appendix D for Calculation Details)
Mass
Nref
Tout
Tout OEM
Tout OEM
Error
Error
Before
Before
After Adaptation
After
Flow,
Site(K)
Adaptation
Adaptation
Adaptation
ref(kg/hr)
150,400
0.996 398.2
401.0
398.2
+0.251% 0%
140,499
0.942 394.6
395.0
394.6
+0.253% 0%
122,227
0.928 401.9
402.2
401.9
+0.249% 0%
Table 19. Discharge Temp Before and After Adaptation (Appendix D for Calculation Details)
The OEM supplied data for the prediction of performance is for a fleet of
compressors and not the specific compressor supplied at site and it is
expected that there will be differences due to manufacturing tolerances.
Furthermore, the OEM supplied performance curves are based on a specific
and defined set of compressor inlet and gas conditions. As soon as the actual
130
site conditions differ from these conditions, as they often do, direct use of
OEM supplied curves become impractical for diagnostic purposes where
accurate performance prediction is required. Thus a common datum or
reference data must be established by the application of θ and δ such that the
performance can be predicted for any inlet conditions. Also a unique iterative
performance adaptation method must be devised in order to reduce the
performance prediction errors. The preceding works carried out have
demonstrated a successful implementation of both of these methods.
The advantages of the proposed method are that it is accurate in the
prediction of compressor performance for the tested range and it can
accurately predict the performance outside the tested range of speeds
because of successive application of scale factors to improve the prediction of
performance curves for lower speeds. The more number of test data, the
more the scale factors and the accuracy of performance prediction is
increased.
Pressure Ratio Prediction Error Chart Before and After Adaptation Technique
15.00%
10.00%
Error (%)
5.00%
0.00%
1
N ref = 0.996 Bef ore Adaptation
-5.00%
N ref = 0.996 Af ter Adaptation
N ref = 0.942 Bef ore Adaptation
N ref = 0.942 Af ter Adaptation
-10.00%
N ref = 0.928 Bef ore Adaptation
N ref = 0.928 Af ter Adaptation
-15.00%
Graph 4. Pressure Ratio prediction error for three speed curves before and
after adaptation technique
131
5
4
Polytropic Efficie ncy Pre diction Error Chart Be fore and Afte r
Adaptation Te chnique
N ref = 0.996 Bef ore Adaptation
N ref = 0.996 Af ter Adaptation
3
Error (%)
2
1
N ref = 0.942 Bef ore Adaptation
N ref = 0.942 Af ter Adaptation
N ref = 0.928 Bef ore Adaptation
N ref = 0.928 Af ter Adaptation
0
-1
1
-2
-3
-4
-5
Graph 5. Polytropic Efficiency prediction error for three speed curves before
and after adaptation technique
0.30%
Discharge Te mpe rature Pre diction Error Chart Be fore and Afte r
Adaptation Te chnique
0.25%
N ref = 0.996 Bef ore Adaptation
Error (%)
0.20%
N ref = 0.996 Af ter Adaptation
N ref = 0.942 Bef ore Adaptation
N ref = 0.942 Af ter Adaptation
0.15%
N ref = 0.928 Bef ore Adaptation
N ref = 0.928 Af ter Adaptation
0.10%
0.05%
0.00%
1
Graph 6. Discharge Temperature prediction error for three speed curves
before and after adaptation technique
Graph 7. Referred values obtained from extreme summer and extreme winter
OEM data superimposed on data derived for OEM reference case (reference
Appendix D, Table D15)
132
Figure 44. Site compressor simulation model in HYSYS
Graph 8. HYSYS performance output of polytropic head versus flow for site
compressor (data input source was the performance data from the final performance map by successive
iteration method (Ref, Graphs 7&8))
133
Graph 9. HYSYS performance output of polytropic efficiency versus flow for
site compressor (data input source was the performance data from the final performance map by successive
iteration method (Ref, Graphs 7&8))
134
7.3 Application of Health Estimation Methodology
It was stated in previous section that the adapted performance data generated
by the innovative successive iteration method were used as input block data
into the simulation programme HYSYS which complemented and utilized
these data for the generation of polytropic heads and building of accurate
performance models of the “clean” compressor. Figure 44 shows the model in
HYSYS and Graphs 8 and 9 demonstrate the actual and “clean” performance
curves for the site compressor.
It was also stated in Chapter 4 that compressor degradation is represented by
the shift of the characteristic curves on the respective maps and such shifts
are represented by degradation or health indices. For a compressor the
primary performance indicators are pressure ratios (PR) and efficiencies while
the compressor health status represented by PR, efficiency and flow capacity
indices [Ref 14]. These will determine the measure of performance shift and
can describe the compressor degradation. Based on these definitions, it was
deduced in Chapter 4 that:
SFfl = m deg / m clean
(1)
SFPR=PRdeg / PRclean
(2)
SFeff = ηdeg / ηclean
(3)
Where,
SF=Scale Factor
m=mass throughput
PR=Pressure Ratio
η=Polytropic Efficiency
And subscripts fl, deg and c represent flow, degraded and clean compressor
respectively.
The application of compressor health estimation methodology shall be made
in two steps.
In step 1, in order to separate out apparent degradation in performance from
actual degradation due to environmental changes, the latter effects on
performance are eliminated by the application of θ and δ.
In step 2 suitable test data will be selected to obtain the degradation index
and applied to the whole map to map out the degraded performance
expectation. Instrumentation errors are considered as minimal here with
negligible effect on the reading accuracy as all key instruments are regularly
calibrated and these instruments have self-diagnosing and fault reporting
capabilities.
135
Step 1: Tables C1A and C1B (Appendix C(I)) refer to the new site base cases
taken in 2009 and 2010 to analyze and diagnose the status and health of the
compressor. Reference to Chapter 4, correction parameters (θ and δ) are
used to refer the OEM and site data to a common datum such that,
Theta (θ) = T1/ Tref (Dimensionless)
(4)
Delta (δ) = P1/ Pref (Dimensionless)
(5)
The subscript “1” refers to compressor inlet conditions. Tref and Pref are
arbitrary chosen values as a base temperature and base pressure upon which
all the operating variables either from OEM or site are compared. It is normal
to set reference values same as the most common site compressor inlet
conditions for temperature, pressure and the molecular weight controlling the
mass flow. For an “n” set of site cases, there will be an “n” set of Thetas (θ)
and Deltas (δ).
The following are the equations used to refer the OEM and test data:
T referred = Tn / θ
(6)
Preferred = Pn / δ
(7)
m referred = m * √ (θ) / δ
(8)
PWR referred = PWR/ ( δ * √ (θ) )
(9)
N referred = N/√ (θ)
(10)
η P, referred = η P / 1
(11)
PWR α m Hp / η P
(12)
Fan laws apply, especially accurate for the small changes in our designated
compressor degradation:
Q α N, Hp α N2, ℓn PR α N2, ∆T α N2, PWR α N3
(13) [Ref 28]
Where Q is the volumetric flowrate and hence mass flowrate is also
proportional to speed, for the same inlet gas and environmental gas
conditions. Referring to equation (8) if there is a change in molecular weight
over time, then this change must be compensated when the mass flow rate is
referred. HYSYS does this automatically by clicking the available radio button
on the compressor input data tab.
Tables C2A and C2B (Appendix C(I)) demonstrate converting the data in
Tables C1A and C1B (Appendix C(I)) to referred values by the application of
136
Theta (θ) and Delta (δ) through choosing T ref and P ref as 315.3 K and 1070
kPaa respectively with a base line molecular weight of 24.6. These values are
the most common inlet conditions of the compressor.
STEP 2: Referring to Base Case 5 of Table C2A in Appendix C(I) which are
the referred site measured parameters in August 2009 and referring to the
compressor actual performance map deduced for 2Q 2006 (solid lines of
Graphs 15 and 16, obtained by performance adaptation described in the
previous section),
Using Equation (2) above:
SFPR = PR deg / PR clean
PR deg = 2.82 (Base Case 5 of Table C2A, Appendix C(I))
PRclean= 3.10 (The earliest treated performance data presently available for
the compressor back in 2Q 2006 and these were used for performance
adaptation model by successive iteration method and these are shown on
graphs 13, 14, 15 and 16)
Therefore the scaling factor using Equation 2 above for pressure ratio is:
SFPR = 2.82 / 3.1 = 0.9097
(14)
In order to fix a point on the performance map, at least 2 points are needed.
We have obtained The degraded Pressure Ratio has been obtained, how
about degraded mass throughput?. The degradation in mass flow rate cannot
be directly measured but it is recalled that fan laws apply and are accurate
especially for small changes of parameters. Using relationships equation (13)
above:
ℓn PR α N2 or PR α exp(N)2
and Q α N
This implies: PR 1 / PR 2 = exp(N 1)2/exp(N 2)2
3.10 / 2.82 = exp 0.852 / exp (N2)2
Therefore N2= 0.793
The above shows that, had the compressor has stayed clean, it should have
achieved the PR of 2.82 with a referred speed of 0.793. But with reference to
Base case 5 of Table C2A in Appendix C(I), the compressor under
observation was actually achieving the stated PR of 2.82 with the higher
referred speed of Nref=0.88. The increase in speed requirement, and therefore
the increase in power draw by compressor is due to degradation.
137
Since Q α N, deterioration in throughput is equivalent to increase in speed to
achieve the target PR, i.e.
SFfl = m deg / m c (Equation 1 above)
SFfl Ξ Ndeg/Nclean= 0.793 / 0.88 = 0.9011
(15)
Now the two points that fix the shift on the performance map of PR versus
mass throughput are known and the whole map is shifted as follows using
scale values obtained in (14) and (15) above respectively:
Pressure Ratio degradation (vertically downward on the performance map) by
(1-0.9097) x 100% = 9.03%
Throughput degradation (horizontally leftward on the performance map) by (10.9011) x 100% = 9.89%
Thus the mass throughput and PR has deteriorated about the same amount
and by 9-10% each.
As it was referred earlier in Chapter 4.1, as an alternative to the use of Fan
laws described above, the curve of test point A (clean performance) on Graph
10 below can manually be shifted until it hits point B (degraded performance)
and in this manner the degradation mass throughput is implicitly solved.
The degradation indices for Pressure Ratio and flow are now obtained as
shown above (0.9097 for SF PR and 0.9011 for SFfl) and the performance
maps can be shifted accordingly. This is shown in Graph 10 below. Reference
to Figures 24 and 25, it is found that the shift angle is around 45° as the
degradation in both directions (down and left) are almost the same. It is
expected that the shift angle increases with the time interval of analysis. The
smaller the time gap between analysis, the smaller the shift angle and the
more accurate the analysis as the shift trend will be built up on regular
intervals.
Similar as in PR calculations, but for polytropic efficiency:
SFeff = η deg / η clean (Equation 3 above)
η clean = 87.0% (“Clean” compressor, from 2Q 2006 records)
η deg = 84.1% (“Degraded” compressor, from August 2009 records, Appendix
C(III)-Table C2A)
Therefore, the scale factor for efficiency is
SF eff= 84.1/87.0=0.9670
138
Thus the degradation in polytropic efficiency is (1-0.967) x 100% = 3.3%
The degradation of mass throughput for efficiency curves is expected to be
the same as for pressure ratio because the same compressor applies. Refer
to Graph 11 below for the shifted efficiency curve of the site compressor.
The methodology and application above is an improvement to the works
already carried out by others [Ref 14], where the degradation in PR is
assumed to be same as that for throughput. In this work, these degradations
have been estimated by the application of affinity (Fan) laws.
It is to be noted that the compressor degradation under investigation is less
pronounced in efficiency (3.3%) than in pressure ratio (9.0%) and in mass
throughput (9.9%). The degradation has had more effect on pressure ratio
and mass throughput. The effect on both pressure ratio and mass throughput
are about the same. An important realization here is that the downward shift
combined with shifting to the left results in narrower surge margins built in
design. If surge margins are exceeded, the compressor may be damaged in a
short time. There are strict controls to avoid surge by mounting an Anti-Surge
Valve between the discharge and inlet to the compressor. However, the
operator must realize that compressor degradation results in surge settings
change, therefore the operator should be in a position the change the valve
settings in accordance with the compressor operation, online.
Reference to Graph 10 below, as it was described earlier in this section, to
evaluate the search space or extreme solution for entirely vertical drop or an
entirely horizontal shift of the performance curve to the left, the performance
curve on which point “A” is situated is dropped vertically downward until it his
point B. Thus it is noted from Graph 10 that in the case of entirely vertical drop
a PR of 2.63 is achieved (a scale factor of 0.85) or 15.0% degradation and
similarly in the case of purely horizontal shift to the left, an m of 107,000 kg/hr
is obtained (a scale factor of 0.823) or 17.7% degradation.
Thus in a vertical map fall, degradation in m=0 & degradation in PR=15.0%.
In a horizontal shift left, degradation in m=17.7% & degradation in PR=0.0%.
Therefore the search space for degradation in PR is anywhere between 0 to
15% and degradation in mass throughput is anywhere between 0 to 17.7%.
As shown above, using real site data and applying proportionality (Fan) laws a
diagonal shift is experienced with degradation in m=9.9% and degradation in
PR=9.0%.
139
Nref (Clean)=0.7
Site Compressor Referred Performance Data
PR Vesus Mass Flow
Clean Compressor:July 2006, Degraded: Aug 2009
4.00
Nref (Clean)=0.8
Nref (Clean)=0.85
Nref (Clean)=0.88
3.80
Nref (Clean)=0.95
Nref (Clean)=0.996
3.60
PR (Referred)
Nref (Degraded)=0.7
3.40
Nref (Degraded)=0.8
3.20
Nref (Degraded)=0.85
Nref (Degraded)=0.88
3.00
2.80
2.60
2.40
2.20
2.00
50000
60000
70000
80000
90000 100000 110000 120000 130000 140000 150000 160000 170000 180000 190000 200000
Mass Flowrate (Referred), kg/hr
Graph 10. Site compressor characteristic map modification (PR shift) due to
degradation
Site Compressor Referred Performance Data
Efficiency Vesus Mass Flow
Clean Compressor:July 2006, Degraded: Aug 2009
90
Polytropic Efficiency (Referred)
88
86
Nref (Clean)=0.7
Nref (Clean)=0.8
84
Nref (Clean)=0.85
82
Nref (Clean)=0.88
80
Nref (Clean)=0.95
Nref (Clean)=0.996
78
Nref (Degraded)=0.7
76
Nref (Degraded)=0.8
Nref (Degraded)=0.85
74
Nref (Degraded)=0.88
72
Nref (Degraded)=0.95
70
50000
60000
70000
80000
90000 100000 110000 120000 130000 140000 150000 160000 170000 180000 190000 200000
Mass Flowrate (Referred), kg/hr
Graph 11. Site compressor characteristic map modification (Efficiency shift)
due to degradation
140
7.4 GPA Index Calculations
It was stated in Chapter 5, Section 2 that evaluation of GPA Index for a
particular diagnostic application is a powerful indicator of estimation success.
It was stated that a GPA Index approaching 1 indicates a very accurate
degradation prediction while a GPA Index close to 0 means the other way
around.
Using the formulas shown in Chapter 5 as follows:
GPA index = 1/(1+ε)
Where ε is a measure of the difference between the measured and predicted
deviations of compressor gas path measurements and it is mathematically
expressed as:
∆Zi, measured / Zi, measured and ∆Zi, predicted / Zi, predicted are the measured and
predicted deviations of measurement Z i, respectively.
Applying the above equations to the site compressor data in Appendix C(I), ε
which is a measure of the difference between the measured and predicted
deviations of compressor gas path measurements and GPA Index are
calculated for each case.
Table 20 shows the GPA Indices for the site compressor based on the
developed performance adaptation methodology. Since they are very close 1,
the methodology is considered to be accurate. In fact, the application of GPA
Indices has proven very effective in compressor diagnostics with an average
indices figure of 1.03 and variance of 0.003 over the last 4 years period.
141
Time:
Case
July-Aug 09
(Appendix C(I))
Pressure Ratio
N ref
Predicted
1
2
3
4
5
0.85
0.87
0.88
0.84
0.88
Time:
Apr-10
Case
N ref
1
2
3
4
5
6
Time:
Apr-10
Case
N ref
1
2
3
4
5
Measured
2.83
2.80
3.00
3.02
3.02
2.83
GPA Index
-0.1033
0.0145
-0.0037
-0.1367
0.0000
1.1152
0.9857
1.0037
1.1583
1.0000
Remark
(Design Case)
ε
3.01
3.05
3.00
3.21
3.20
3.02
GPA Index
-0.0598
-0.0820
0.0000
-0.0592
-0.0563
-0.0629
1.0636
1.0893
1.0000
1.0629
1.0596
1.0671
(Design Case)
(Appendix C(I))
Efficiency
Predicted
0.84
0.87
0.88
0.84
0.88
ε
2.71
2.76
2.70
2.78
2.82
(Appendix C(I))
Pressure Ratio
Predicted
0.90
0.90
0.88
0.91
0.91
0.90
Measured
2.43
2.80
2.69
2.40
2.82
84.0
84.2
83.9
84.0
84.1
Measured
82.0
82.0
81.8
85.8
84.1
ε
GPA Index
0.0244
0.0268
0.0257
-0.0210
0.0000
0.9762
0.9739
0.9750
1.0214
1.0000
(Design Case)
Table 20. GPA Indices for the site compressor based on the developed
performance adaptation methodology
142
7.5 Establishment of Degradation Indices for the Site Compressor and
Data Trending
As stated in Sections 4.1, the Degradation Indices for Pressure Ratio and
Polytropic Efficiency are:
SFPR=PRdeg / PRclean, and,
SFeff = ηdeg / ηclean
Applying the above to the site compressor data (Appendix C(II)), the
degradation indices are calculated for both pressure ratio and polytropic
efficiency through time. Table 21 is thus obtained and the respective trends
for pressure ratio and efficiency are plotted on Graph 12 and Graph 13.
Compressor
Case
Date
Pressure Ratios
Clean
Dirty SF (PR)
Polytropic Efficiency
Clean
Dirty
SF (EFF)
*
1
8
20
29
33
13/06/2006
07/09/2006
06/07/2009
06/04/2010
24/08/2010
3.26
3.16
3.42
3.67
3.62
3.26
2.91
2.90
2.86
2.72
1.0000
0.9209
0.8480
0.7793
0.7514
87.50
87.30
87.04
87.12
86.96
87.50
87.00
85.00
81.00
80.00
1.0000
0.9966
0.9766
0.9298
0.9200
Table 21. Establishments of degradation indices for the site compressor
(*refer to Appendix C(II) for site data source)
143
Graph 12. Derived PR degradation index versus time for the site compressor
Graph 13. Derived efficiency degradation index versus time for the site
compressor
144
7.6 Health trend generation for the site compressor
Due to the performance deterioration of compressor, the “expected”
performance value is not a constant and changes with time. It would not be
correct to assume a constant base line for establishing the compressor’s
health status. It would thus be necessary to set a trend for the expected
deterioration in performance, either from OEM before moving the compressor
to site as described in Chapter 4, Section 4 or from performance observations
at site over time. For the site compressor under investigation, OEM was not
requested to produce the expected trend. However, the fall in efficiency over
time has been established [Ref. Appendix C(I, II)] for the compressor and this
is shown on Graph 14. Taken the expected trend in efficiency profile follows
as shown on the referred Graph, then it is established that the expected
efficiency trend is a polynomial fit and the equation is shown on Graph 15,
with “Y axis” representing the expected efficiency and “X axis” representing
operational time. On these bases, the equation may be used for diagnostic or
prognostic purposes. Depending on how the compressor is performing, the
actual trend could be having a smaller slope, meaning the compressor is
doing better than expected or the slope could be steeper than the established
curve fit meaning the compressor condition is deteriorating faster than
expected (see Graph 15). In either case, the maintenance schedule or
partial/full overhaul can be adjusted to suit the actual condition of compressor.
Only by on-line monitoring or frequent evaluation of health indices could
establish the compressor performance trend.
Graph 14. Trend Analysis of the Site Compressor: Degradation in Efficiency
versus Time
145
The expected or normal falling trend in efficiency, modeled in above quadratic equation
The compressor performing better than expected
The compressor has accelerated performance deterioration
Graph 15. Site compressor efficiency degradation modeling to indicate the
expected fall in performance
146
7.7 Sensitivity Analysis
It was referred in Chapter 5, Section 3 that compressor degradation does not
have equal effect on measurable parameters variables. It is important to
recognize which variables are sensitive and which ones are insensitive to
degradation so that maintenance items and schedules are classified
accordingly. Furthermore the nature of relation (linear or non linear) between
degradation and compressor dependent variables is critical to be established
so that the future behaviour can be estimated by extrapolation.
Using the advanced simulation programme HYSYS, Graphs 16-22 were
obtained to show the effect of degradation, neglecting geometrical changes as
a result of degradation, on measurable parameters. The analysis of these
graphs show that the trend effect of increasing degradation levels in
compressors on measurable parameters is fairly linear and that variations in
performance as a result of degradation is not independent of the compressor
speed meaning degradation do not have the same effect on performance
parameters at high speeds compared at low speeds. It shall be noted that the
graphical sensitivity analysis shown on Graphs 16-22 are based on
thermodynamic changes only and do not consider any geometrical, or shape
changes. For a detailed analysis, in addition to thermodynamic
considerations, CFDs should also be engaged in modeling the detailed and
accurate geometries of the compressor.
147
Compressor (Mass Flow) Deterioration Effect On
Pressure Ratio
3.50
Nref=1.0178
Nref=0.996
3.00
Pressure Ratio Variation %
Nref=0.9698
2.50
Nref=0.873
2.00
Nref=0.7762
Nref=0.6795
1.50
1.00
0.50
0.00
0.0
-1.0
-2.0
-3.0
-4.0
-5.0
Deterioration %
Graph 16. Compressor (Mass Flow) Deterioration Effect on Pressure Ratio
Compressor (Mass Flow) Deterioration Effect On
Polytropic Head
3.50
Nref=1.0178
Polytropic Head Variation %
Nref=0.996
3.00
Nref=0.9698
Nref=0.873
2.50
Nref=0.7762
Nref=0.6795
2.00
1.50
1.00
0.50
0.00
0.0
-1.0
-2.0
-3.0
Deterioration %
-4.0
-5.0
Graph 17. Compressor (Mass Flow) Deterioration Effect on Polytropic Head
(Hp)
148
Compressor (Mass Flow) Deterioration Effect On
Discharge Temperature
Outlet Temperature Variation %
0.7
Nref=1.0178
Nref=0.996
Nref=0.9698
Nref=0.873
Nref=0.7762
Nref=0.6795
0.6
0.5
0.4
0.3
0.2
0.1
0
0.0
-1.0
-2.0
-3.0
Deterioration %
-4.0
-5.0
Graph 18. Compressor (Mass Flow) Deterioration Effect on compressor outlet
temperature
Compressor (Mass Flow) Deterioration Effect On
Shaft Power
0.0
-1.0
-2.0
Deterioration %
-3.0
-4.0
-5.0
0.00
Shaft Power Variation %
-0.50
-1.00
-1.50
-2.00
-2.50
Nref=1.0178
Nref=0.996
Nref=0.9698
Nref=0.873
Nref=0.7762
Nref=0.6795
-3.00
-3.50
Graph 19. Compressor (Mass Flow) Deterioration Effect on compressor
power
149
Compressor Polytropic Efficiency Deterioration Effect On
Pressure Ratio
0.000
0
-1.000
-2.000
-3.000
Deterioration %
-4.000
-5.000
Pressure Ratio Variation %
-0.1
-0.2
Nref=1.0178
-0.3
Nref=0.996
-0.4
Nref=0.9698
Nref=0.873
-0.5
Nref=0.7762
-0.6
Nref=0.6795
-0.7
Graph 20. Compressor (efficiency) Deterioration Effect on compressor
pressure ratio
Compressor Polytropic Efficiency Deterioration Effect
On Discharge Temperature
Outlet Temperature Variation %
1
0.9
0.8
0.7
0.6
0.5
Nref=1.0178
Nref=0.996
Nref=0.9698
Nref=0.873
Nref=0.7762
Nref=0.6795
0.4
0.3
0.2
0.1
0
0.000
-1.000
-2.000
-3.000
Deterioration %
-4.000
-5.000
Graph 21. Compressor (efficiency) Deterioration Effect on compressor outlet
temperature
150
6
5
Compressor Polytropic Efficiency Deterioration Effect
On Shaft Power
Nref=1.0178
Nref=0.996
Shaft Power Variation %
Nref=0.9698
4
3
Nref=0.873
Nref=0.7762
Nref=0.6795
2
1
0
0.000
-1.000
-2.000
-3.000
-4.000
-5.000
Deterioration %
Graph 22. Compressor (efficiency) Deterioration Effect on compressor power
151
8. Discussion
8.1 Use of HYSYS for Compressor Applications and Limitations
HYSYS is an advanced simulation tool for the thermodynamic modeling of
compressors. It has the capability of mimicking the actual compressor at site
with extremely high accuracy. All the programme needs is in essence the inlet
and outlet measurable parameters of the compressor and with the user
intervention the programme builds the performance curves. For rotational
speeds between the tested regions, the programme uses interpolation
techniques which are also used and practiced in the industry. HYSYS carries
out accurate interpolation between two curves. However, the accuracy of
extrapolation, over the maximum speed curve or extrapolation lower than the
minimum speed, could not be independently verified. Therefore it should
always be ensured that the value of simulated speed is within the range of
input maximum and minimum speed curve data. HYSYS also has transient
analysis capabilities but it is more geared towards dynamic issue such as
compressor surge, anti-surge control valve design, determining stonewall
region and the settle out pressures in case of compressor trip. The user can
model a degraded compressor on HYSYS in much the same way as modeling
a new compressor. Once a model is set, a whole range of sensitivity analysis
could be run by HYSYS given independent variables such as mass
throughput, pressure ratio or efficiency decrease with time. Overall, HYSYS is
a recommended simulation package for thermodynamic performance analysis
of centrifugal compressors.
8.2 Degradation Modeling by Simulation and Health Estimation by
Scaling
When compressors degrade, the performance behaviour change and the
compressor characteristic curves fall downwards and generally to the left,
reducing the deliverability, efficiency and increasing the performance
uncertainty and reducing the operational area. Thus in order to know the
factors limiting the operational flexibility and power demand, a a-priori
knowledge is required on how degradation of various key parameters (PR, m
and η) at various levels affect the compressor performance and these works
was carried out under this research.
A representative performance map for a clean compressor has been
developed from basic gas and operational data by applying the equations of
thermodynamics and affinity laws. For an accurate performance mapping,
CFD analysis is required to complement the thermodynamic simulation to
precisely model the stage by stage physical geometries of the compressor.
152
The degraded compressor maps for various scenarios of degradation and
rerates were superimposed on the same map to define the operational limits
of the compressor. HYSYS was utilized to analyze the trends in measurable
and non measurable parameters, over a period of 1 year, following a linear
and nonlinear degradation patterns.
By simulation, it is found that linear degradation produce nearly linear
changes in measurable output and non-linear degradation produce non-linear
changes in measurable output.
The compressor degradation indices have been applied to examine the
health of compressor over the time. In this thesis, it is demonstrated that
estimation of degradation in health indicators (throughput and efficiency
drops) by scaling the measurable parameters is a useful tool. The operator
can simply take compressor measurements and estimate the current health of
the compressor relative to when it was new by indirectly evaluating the drop in
mass throughput and efficiency as the health indicators.
It was found that power demand variation produced the least errors in
predicting the compressor health as a diagnostic tool. Using the speed
measurement as a diagnostic tool, require further works and analysis since
there are several factors that can affect speed.
In this study, the measurements are the discharge temperature, power and
speed and the compressor health indicators are mass throughput and
efficiency health indices.
The diagnostic method developed above need further refinement and
research for minimization of errors between predicted and actual degradation
levels.
8.3 Performance Adaptation by Successive Iteration Method and Data
Trending
For operational companies to confirm acceptance, the compressor must be
tested at full range of speeds and flowrates as soon as it is moved to site and
operated to establish the real performance maps. At this time the performance
can be scaled from OEM performance curves. The compressor under
investigation was moved to site and started operations in 2004 but at the time,
these tests were not done. This is one of the lessons learnt, which clearly
indicate that the degradation of compressor does shift the performance map
but the scale of shift or shift factor is not constant for all speeds. The proposed
method of performance adaptation by successive iteration is based on the fact
that scaling method work best at the or around the local test point but the
accuracy is affected when moving further away from the locality if the same
scale is applied. The successive iteration method gives an opportunity for the
153
new scale factor, obtained from site performance data, at each speed to be
applied to performance map. In each iteration, the previous curve is fixed in
position with the test point falling exactly on the curve until all curves are fixed
in position. The wider the test point range, the better. Graphs 4-6 show the
errors before and after the adaptation technique and it is evident that the
technique is useful for accurate mapping of the performance curves. The
performance maps should be built on referred conditions so that the
environmental and inlet gas condition changes do not influence the curves
and this was shown and proved on Graph 7 with the referred performance
maps overlapping for extreme summer and winter conditions. The full data
and calculation details are reflected in Appendix D.
The expected performance of compressor is not constant with time due to
deterioration and the performance will decrease with time, i.e., the base line is
not horizontal and falls with time unless and until a total overhaul is carried
out. Comparing the actual performance with a constant expected value will
lead to a wrong diagnosis. It is highly useful, if not critical, that at the time of
compressor construction the OEM is requested to give a prediction in
performance trend so that the actual compressor performance is evaluated
against the expected performance for diagnostic or prognostic purposes any
time during operation by online monitoring. The predicted OEM trend may be
updated and revised with monitoring the compressor under observation.
Reference to Graph 15, once the expected performance trend is established,
the operator can compare the actual performance versus expected
deterioration trend and decide in advance for remedial actions such as partial
or full maintenance. This will provide an excellent opportunity for improving
the availability of compressor and reduction of the operational cost which are
critical part of process plant and operational strategy respectively.
8.4 Health Index and Diagnostics
Tables C1A and C1B in Appendix C (I) show the actual site data for August
2009 and several months later in April 2010. Tables C2A and C2B in
Appendix C (I) show the derived quasi-dimensionless measured figures. After
plotting these figures on the graphs, it was observed that the differences are
too minute to report due to the relatively short time of between data capturing.
Hence the compressor data of August 2009 were superimposed on July 2006
normalized performance curves.
The proposed method of developing health indices for independent
parameters of PR, efficiency and mass throughput, based on performance
indices developed from comparison of degraded and clean performance at a
test point followed by the map , have been applied to the site compressor and
these are shown on Graphs 14 and 15. The graphs clearly show compressor
degradation over the 3 years period.
154
The health parameters that determine the current health status or degradation
in a compressor are the three independent parameters of pressure ratio,
efficiency and throughput capacity. Referring to the results obtained in Section
7.3 for the compressor under observation, the degradation in efficiency is less
pronounced than in PR and mass throughput.
The fall in performance as reported by instruments and data readings at
critical locations of the compressor is not just due to true degradation but a
combination of true degradation, environmental effects and reading errors.
Thus to obtain the true degradation the environmental impact should be taken
out and reading errors otherwise environmental effects on performance and/or
reading errors may be wrongly diagnosed as part of compressor degradation.
Reading errors and noise and bias cannot be totally eliminated in practice but
it is minimized by regular instrumentation calibration with self-diagnosing
capabilities. Due to the continuous performance changes, data averaging
cannot also be applied by an individual researcher (the author). To overcome
these limitations, first of all the original compressor characteristic maps must
be highly accurate, performance data should be referred to a common datum
so that changes are relative to a common condition making all cases
comparable & independent of environmental or gas inlet condition changes
and the most valid set of site data should be utilized as analysis by
performance adaptation is a “snap shot” of the compressor performance.
Generation of GPA indices to establish confidence, credibility and accuracy of
applied health evaluation is an effective tool. Reference to Table 20 in
Section 7.4 and analyzing the GPA Indices being mostly close to 1, the results
show a very accurate estimation of actual compressor data especially at the
tested speeds. A statistical analysis of GPA indices in Table 20 (Section 7.4)
show an average GPA Index of 1.03 and a variance of 0.003 over the last 4
years of operation. This is an indication of accuracy and applicability of the
compressor health indexing by the scaling.
Rotational speeds cannot be scaled in the same manner as setting up scale
factors for the independent parameters of such as throughput, pressure ratio
and efficiency. It must be emphasized here that it is normally expected that
the compressor is run continuously at the design or continuous speed during
the steady state period which constitute by far the majority of the plant life.
Nevertheless the non-linearity for much lower speeds can be viewed as a
shortfall in applying scale factor to the whole map which is a linear process.
To overcome this situation the performance adaptation method must be
applied at regular intervals updating the predicted performance curves. The
intensity of interval depends on the cleanliness of the process gas and the
environmental condition. Coherently, the non linear trend that will appear in
the derived scale factors or health indices with time can be used for diagnostic
and prognostic purposes.
155
It is also prudent to test the compressor at the whole envelope of rotational
speeds so that the degraded compressor performance can be modeled as
completely as possible. The gas path analysis method whether linear or non
linear require ‘a priori’ information on compressor performance which can be
established by testing the compressor at site at various speeds as earlier on
as possible. With the degraded compressor, the same pressure ratio as the
clean compressor can be achieved but only at a higher RPM and this requires
higher absorbed power which is also an indication of lower compressor
efficiencies under the degraded conditions.
Due to the criticality of the operation, all main instruments at compressor site
are, and shall be, regularly calibrated for accuracy and maintained. The
instrumentation at site has self-diagnostic capability so that the operator at the
control room (DCS) is notified of any failed instrument. Therefore the
instrument readings taken from site are very accurate. Some measurement
noise is expected to interfere with the transmitted or recorded readings,
although these interferences are not anticipated to have detrimental effect for
diagnostic purposes taking note of the site’s superior design in terms of low
noise interference, accuracy of parameter measurements and operational
conditions. It may also be further noted that the noise effect is particularly
applicable to gas turbines rather than process gas compressors because the
measurements for compressor are taken “at source” (rotor) because of heavy
structure whereas in gas turbines the readings are normally are taken on the
casing.
For the site compressor under research, the degradation in efficiency is less
pronounced (3.3%) than degradation in PR (9.0%) and in mass throughput
(9.9%). In extreme cases (a vertical drop of performance map) the PR
degradation is 15.0% and for a horizontal shift, mass throughput capacity is
degraded by 17.7%.
Right from the start of compressor design OEM should be requested to deliver
expected fouling factors for the supplied compressor by the operator providing
the OEM with the anticipated process gas data during the project’s life cycle,
the environmental data and any other data OEM may request to build the
expected fouling factors. This means during the design stage, the operator
should request OEM to provide Graph 15 and this will be monitored during
actual compressor operation.
In addition to providing Graph 15, or
alternatively, OEM may be requested to produce H p/N2 versus Q/N for several
fouling factors expected during the compressor life as shown on Figure 23B in
Chapter 4, Section 4.4. The aforementioned measures will lead toward valid
compressor diagnostics. For the site compressor under investigation which
started operating in 2004, Graph 15 nor Figure 23B were requested prior to
hand over. The Graph 15 has been produced under this research by the
156
Author. This is another lesson learnt to be implemented for future compressor
designs.
The various levels of degradation have different levels of effect on compressor
performance and some compressor performance parameters are more
sensitive toward degradation than other parameters. Carrying out these
analyses is important because the operator can get a knowledge in advance
which parameters of the compressor are affected the most by degradation
and which parameters are least affected. In this manner the operator can plan
ahead the expected changes in compressor delivery and change the
production strategy accordingly. These are numerically demonstrated in
Graphs 16-22 inclusive. To make the discussion clear, an example may be set
as follows: reference to Figure 35, it is seen that for this particular
compressor, medium degradation does not have any significant effect on
discharge temperature, so the site operator does not need to be concerned
with remarkable increase in cooling water requirement in the heat exchanger
downstream of the compressor in case his compressor degrades. On the
other hand however, for this compressor, the same amount of deterioration
has a significant effect on power consumption which also shows the efficiency
shows a marked deterioration. This finding is in line with most of the papers
published on compressor performance deterioration modeling [Refs.14, 15,
16, 18]. Then the operator needs to be worried about power limits or
availability for his deteriorated compressor. In fact, carrying out this exercise
will inform him in advance to what degree of deterioration the plant can
tolerate before power limitation apply and this could set the tolerance limit for
compressor deterioration before an appropriate action is taken and an
economical analysis based on unit cost of power may be beneficial. The
Graphs 16-22 also show the degradation effect (due to fouling) is fairly linear
on measurable parameters . These graphs also show that the variations in
performance as a result of degradation is not independent of the compressor
speed meaning degradation does not have the same effect on performance
parameters at high speeds compared to low speeds. It shall be noted that the
graphical sensitivity analysis shown on Graphs 16-22 are based on
thermodynamic changes only and do not consider any geometrical change.
Graphs 12 and 13 demonstrate the derived health indices versus time for the
site compressor from Table 21. It is noted that PR degradation is fairly
substantial compared to efficiency degradation.
It is found that in terms of PR degradation, the site compressor is likely to be
in the region of 9.0% and that for the mass throughput is 9.9% using Fan laws
as fully explained in Chapter 7, Section 3. The shift angle of performance
curve is about 45°. In addition, to obtain a search space for the possible range
of degradation, purely vertical drop and purely horizontal shift of performance
curve were also taken. In extreme cases (a vertical drop of performance map)
157
the PR degradation is 15.0% and for a horizontal shift, mass throughput
capacity is degraded by 17.7%.
8.5
Major Contributions of This Thesis

The application of thermodynamics and proportionality laws for the
build up of a representative performance map for centrifugal
compressors(Chapter 6)

Development of a novel performance adaptation method mapping out
the actual performance of centrifugal compressor under new and
degraded conditions using site data (Chapter 7)

Establishment of scaling factors being not uniform for all speeds. Thus
as a result of degradation, the performance curves shift but the shift of
each speed line is unique (Chapter 7)

Calculation of flow capacity index due to degradation, rather than
assuming it is equal to pressure ratio degradation (Chapter 7, Section
3)

Publication of three relevant papers at international oil and gas
conferences on compressor performance adaptation, diagnostics and
the need for online performance monitoring of the rotating equipment
(Appendix A)
8.6
Scope for Future Work

Based on the success of the established method of performance
adaptation and diagnostics presented in this thesis applied to the site
compressor, followed by publication of three relevant papers between
2008 and 2012 (See Appendix A) discussing the methods of
performance adaptation, performance monitoring and diagnostics, it is
recommended that Cranfield offers an opportunity to the Author as a
post doctoral work to carry out further research by writing a computer
programme that is capable of adapting and establishing the actual
performance of compressors freshly moved to site and is capable of
performance monitoring on continuous basis and performance data
trending online for diagnostics and prognostic purposes. The referred
compressor shall not be confined to process centrifugal compressor
types but also expanded into the axial type compressors (i.e., gas
turbine axial compressor or axial process compressor).
158
9. Conclusions
The conclusions of this thesis are listed below:




The performance maps supplied by OEM at early stages of project
development are not accurate and have limited application as they are
for a fleet of compressors and not for the specific compressor supplied
for specific site related project. Moreover, these maps are for a defined
gas inlet and environmental conditions. Hence, compressors must be
tested at a full range of speeds and flow rates as soon as it is moved to
site from the OEM shop and this will constitute the performance of the
“clean” or “un-degraded” compressor. The results of errors in
performance prediction before and after the adaptation technique by
successive iteration developed in this thesis show that, once the model
is set up, it could be applied to OEM and test data for an accurate
performance prediction. The performance adaptation by successive
iteration stems from the fact that curve correction factor is not the same
for all speeds and this method allows for the scale variation for each
speed.
While on continuous operation, compressor starts to degrade via
various mechanisms including wear and flow leak in seals and
erosion/corrosion and fouling causing a shift in performance maps and
these shifts must be investigated through establishing degradation
indices for major independent parameters of pressure ratio, polytropic
efficiency and mass throughput at regular intervals in the manner
shown in this thesis. The actual performance can also be monitored in
real time by continuous monitoring and data trending will lead to valid
compressor diagnostics.
Compressor health estimation method has been successfully
developed and applied to the site compressor diagnostics (Chart 3).
The established GPA Index for the applied method (Index as 1.03 and
0.003 as variance) over 4 years of operation indicate the approach
taken for the health estimation is accurate. The three independent
parameters that determine the health of the compressor are pressure
ratio, efficiency and mass throughput capacity.
The expected performance of the compressor is not constant during
compressor operation but it is a variable depending on the
environmental and gas inlet conditions, component ware out such as
seals rubbing, as well as the expected health of the compressor since
last major overhaul. That is, the base line for comparing the actual
performance to the expected performance is time dependent and has a
specific trend. Comparing the actual performance to a constant value
will lead to a wrong diagnostic. Performance data trending for referred
efficiency has been carried out for the site compressor and the trend
159





was found to be a polynomial fit. If this is taken as the normal expected
trend in efficiency then monitoring the actual performance vis-a-vis the
expected performance will lead to diagnostic and prognostic analysis
and establishment of maintenance strategies for the compressor.
Degradation of compressor results in a shift of performance curves to
the bottom and to the left of the performance map (shift angle, A°D).
The amount of shift indicates the degree of degradation. For the
compressor under research, the degradation in efficiency is less
pronounced (3.3%) than degradation in PR (9.0%) and in mass
throughput (9.9%). The degradation in pressure ratio and mass
throughput are more or less the same. In order to find the degradation
search space, in the extreme case, the degradation is 0-15.0% in PR
(assuming purely a vertical drop of performance curve due to
degradation) and a degradation of 0-17.7% in mass throughput
(assuming purely a horizontal shift of performance curve due to
degradation).
In this work it is demonstrated that by the application of proportionality
(Fan) laws, it is possible to identify the degradation in throughput, by
relating the degradation in mass throughput with the increase in
compressor rotational speed requirement as a result of degradation.
The previous works by others have assumed degradation in PR and
throughput are the same. In this work it is found that degradation in
throughput is in the same order of magnitude as in PR by taking site
measurements.
The downward shift combined with shifting to the left due to the
compressor degradation results in narrower surge margins built in
design by the OEM. If surge margins are exceeded, the compressor
may be damaged at start up. There are strict controls to avoid surge by
mounting an Anti-Surge Valve between the discharge and inlet to the
compressor. However, the operator must realize that compressor
degradation results in changes in surge settings, therefore the operator
should be in a position to change the valve settings in accordance with
the compressor operational parameter requirements (i.e., PR and m)
on-line.
Based on supplied data to OEM on variations and quality of process
gas flow properties and environmental conditions, OEM should be
requested to produce the expected fouling factor curves for the
supplied compressor in the manner described in this thesis and
thereafter the compressor should be monitored accordingly.
For measurable performance readings, faulty instruments, smearing
effects, noise and bias should be avoided. For this site compressor the
instruments are regularly calibrated, maintained and have selfdiagnosing capability although some instrument noise cannot be
160





avoided. Furthermore, the noise effect on instrument error is less in
process gas compressors than in gas turbines as the instrument
readings are taken “at source” in compressors whereas in gas turbines
reading are taken on the casing.
The rate of degradation buildup varies with time. With reference to the
sensitivity analysis cases ran by simulation in this thesis on the site
compressor, the effect of degradation on performance parameters is
not independent of compressor rotational speeds. This means that the
shifting of performance curves as a result of degradation is not the
same for all speeds.
Application of degradation scale factors is an accurate description of
degraded performance particularly at the tested points. It is also quick
and robust. The accuracy somewhat reduces moving well away from
the test point(s). This apparent limitation is not viewed critical, as the
operators are expected to run the compressor at maximum speed
continuously for most of the plant life. To overcome this limitation is to
have the compressor performance tested at regular intervals and with
updated degradation indices at each interval.
HYSYS is a suitable simulation tool to complement the performance
adaptation and simulate the real performance of compressors
accurately. This is important for performance based diagnostic
techniques as errors or uncertainties in performance prediction could
be wrongly confused with degradation.
Rotating equipment diagnostic is a powerful application to examine the
health of the compressor and to predict which and when a component
requires servicing. By fault reporting, data logging and trending one
shall be able to link the type of degradation and the likely location of
fault. This is currently being done by the major compressor /turbine
manufacturers. It is recommended that all compressor packages
should include the technology of on-line performance monitoring as a
part of the package to a purposeful level capable of carrying out
several diagnostic techniques complete with statistical analysis in order
to have confidence in the results as critical operational and
maintenance decisions shall be based on the diagnostic results. This
has been echoed in the conference papers presented by the Author.
Degradation simulations show that that linear degradation produce
nearly linear changes in measurable output and non-linear degradation
produce non-linear changes in measurable output. In this thesis, it is
demonstrated that estimation of degradation in health indicators
(throughput and efficiency drops) by scaling the measurable
parameters is a useful tool. However, the diagnostic method developed
using simulated measurements and applying scale factors to estimate
the degraded health parameters of simulated compressor require
161
further considerations and refinement to reduce the differences
between the predicted and simulated values.
162
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Appendix A
Published Papers by the Author during Research Thesis
(1) Paper Title: Performance Modeling of Degraded Compressors and
Fault Diagnostics
Presented at: The International Production and Operation Conference and
Exhibition, May 15th 2012, organised by SPE, Doha, SPE Paper 154136
(2) Paper Title: Compressor Degradation, Performance Adaptation and
Fault Diagnostics
Presented at: Middle East Offshore, October 13th, 2010, Doha, Produced by
PennWell and Sponsored by Oil & Gas Journal, Qatar Petroleum, Shell and
other majors.
(3) Paper Title: Compressor Performance Adaptation for Gas Path
Analysis and Diagnostics
Presented at: The 13th International Conference on Applied Mechanics and
Mechanical Engineering (AMME), 27-29th May, 2008, Cairo
167
Appendix B
Compressor and Expander Calculations in HYSYS
B1. Interactive page in HYSYS for Compressor Data Input
The parameters for compressor design may be entered by an interactive
process. The initial compressor page looks like the figure below. Single or
multiple performance curves could be built into the model by pressing on
“Rating” (see Figure B1). In case of multiple performance curves, a series of
heads are entered for a set of flowrates and RPMs and the corresponding
efficiencies for a single molecular weight or a series of different molecular
weights using appropriate radio buttons. Either adiabatic or polytropic efficiency
could be entered for calculations. The efficiency type must be the same for all
input curves.
Figure B1. Input page in HYSYS for design and worksheet
The “Curves Page” in HYSYS is embedded below in Figure B2. This is the page
that allows the user to enter the performance curves.
Figure B2. Input page in HYSYS for entering the available performance curves
By clicking on each curve name, the following interactive page appears for
which data are entered:
168
By pressing the Curve property view the following data may be entered as
shown on Table B1:
Table B1. Input requirement for the generation of speed curves
By disabling its “Activate” box, a specific curve can be removed from the
calculations. Once a curve or curves have been created the relevant buttons are
enabled to View or Delete or Plot the curves. If multiple curves have been
installed, an operating speed has been specified, and none of the multiple
curves' speed equals the operating speed, then all of the curves will be used
within the calculation. For example, if curves for two speeds (l000/min and
2000/min) are provided, and an operating speed of l500/min is specified,
HYSYS interpolates between the two curves to obtain the solution. Also an inlet
pressure and one of the following variables should be provided: flow rate, duty,
outlet pressure, or efficiency, as explained above. HYSYS can calculate the
appropriate speed based on the input. In this case, it is needed to provide the
feed composition, pressure, and temperature as well as two of the following four
variables:


Flow rate
Duty
169


Efficiency
Outlet Pressure
Once the necessary information is provided, the appropriate speed is
determined, and the other two variables are then calculated.
B2. Centrifugal Compressor Calculations in HYSYS
The Centrifugal Compressor operation is used to increase the pressure of an
inlet gas stream with relative high capacities and low compression ratios.
Depending on the information specified, the Centrifugal Compressor calculates
either a stream property (pressure or temperature) or compression efficiency.
The Centrifugal Compressor operation takes into account the compressibility of
the liquid, thus performing a more rigorous calculation.
There are several methods for the Centrifugal Compressor or Expander to solve,
depending on what information has been specified, and whether or not you are
using the compressor's characteristic curves. In general, the solution is a
function of flow, pressure change, applied energy, and efficiency. The
Centrifugal Compressor or Expander provides a great deal of flexibility with
respect to what you can specify and what it then calculates. You must ensure
that you do not enable too many of the solution options or inconsistencies may
result.
The operating characteristic curves of a compressor is usually expressed as a
set of polytropic head and efficiency curves made by manufacturers.








Some of the features in the dynamic Centrifugal Compressor operations
include:
Dynamic modeling of friction loss and inertia in the Centrifugal
Compressor.
Dynamic modeling which supports shutdown and startup behaviour.
Multiple head and efficiency curves.
Modeling of Stonewall and Surge conditions of the Centrifugal
Compressor.
A dedicated surge controller which features quick opening capabilities.
Handling of phase changes that may occur in the unit operation.
Linking capabilities with other rotational equipment operating at the same
speed with one total power.
B3. Compressor Solution Methods With and without Performance Curves
When the compressor is at design stage, the performance curves are not
normally available. In this instance the compressor performance is estimated
without the curves by fixing specific design parameters. However, when an
existing compressor(s) is being analyzed for performance evaluation or data
170
matching, the performance curves are normally available from the manufacturer.
It is to be noted that these performance curves are the expected values supplied
by the manufacturer in the beginning and these do not necessarily represent the
true or current performance of the compressor(s) as predicted by the
manufacturer. This is when data matching becomes critical in the modeling so
that effect of variables on the performance of the compressor could be reliably
analyzed.
The table below (Table B2) represents the variables that need fixing for the
analysis to begin with or without performance curves being available:
Without Curves
With Curves
3. Flow rate and inlet pressure
are known. Then:
Flow rate and inlet pressure are
known. Then:
A) Specify outlet pressure.
And,
B) Specify either Adiabatic or
Polytropic efficiency.
HYSYS calculates the
required energy, outlet
temperature, and other
efficiency.
B) Specify operating speed.
B)HYSYS uses curves to
determine efficiency and
head.
HYSYS calculates outlet
pressure, temperature, and
applied duty.
4. Flow rate and inlet pressure
are known. Then:
A) Specify efficiency and
duty.
Flow rate, inlet pressure, and
efficiency are known. Then:
A)HYSYS interpolates curves
to determine operating speed
and head.
HYSYS calculates outlet
pressure, temperature, and
other efficiency.
HYSYS calculates outlet
pressure, temperature, and
applied duty.
Table B2. Variables requiring values in HYSYS compressor calculations
171
B4. HYSYS Simulation Theory at Steady State
For a Centrifugal Compressor, the isentropic efficiency is given as the ratio of
the isentropic (ideal) power required for compression to the actual power
required:
Power Required
Isentropic Efficiency (%) =
Power Required Actual
Isentropic
x 100%
Polytropic
x 100%
And
Power Required
Polytropic Efficiency (%) =
Power Required Actual
For an adiabatic Centrifugal Compressor, HYSYS calculates the centrifugal
compression rigorously by following the isentropic line from the inlet to outlet
pressure. Using the enthalpy at that point, as well as the specified efficiency,
HYSYS then determines the actual outlet enthalpy. From this value and the
outlet pressure, the outlet temperature is determined.
For a polytropic Centrifugal Compressor or Expander, the path of the fluid is
neither adiabatic nor isothermal. For a 100% efficient process, there is only the
condition of mechanical reversibility. For an irreversible process, the polytropic
efficiency is less than 100%. For compression, the work determined for the
mechanically reversible process is divided by an efficiency to give the actual
work.
Notice that all intensive quantities are determined thermodynamically, using the
specified Property Package. In general, the work for a mechanically reversible
process can be determined from:
W = ∫ VdP
Where:
W= Work
V= Volume
dP= Pressure Difference
As with any unit operation, the calculated information depends on the
information which is specified by the user. In the case where the inlet and outlet
pressures and temperatures of the gas are known, the ideal (isentropic) power
of the Operation is calculated using one of the above equations. The actual
power is equivalent to the heat flow (enthalpy) difference between the inlet and
outlet streams.
For the Centrifugal Compressor:
172
Power Required
actual
= Heat Flow
outlet
- Heat Flow
inlet
where the efficiency of the Centrifugal Compressor is then determined as the
ratio of the isentropic power to the actual power required for compression.
In the case where the inlet pressure, the outlet pressure, the inlet temperature
and the efficiency are known, the isentropic power is once again calculated
using the appropriate equation. The actual power required by the Centrifugal
Compressor (enthalpy difference between the inlet and outlet streams) is
calculated by dividing the ideal power by the compressor efficiency. The outlet
temperature is then rigorously determined from the outlet enthalpy of the gas
using the enthalpy expression derived from the property method being used. For
an isentropic compression (100% efficiency), the outlet temperature of the gas is
always lower than the outlet temperature for a real compression or expansion.
An essential concept associated with the Centrifugal Compressor (and
Expander) operations is the isentropic and polytropic power. The calculation of
these parameters and other quantities are taken from "Compressors and
Exhausters - Power Test Codes" from the American Society of Mechanical
Engineers.
The isentropic or polytropic power, W, can be calculated from:
W=F1(M) (n/(n-1))CF (P1/ρ1) x [ (P2/P1)((n-1)/n)) -1) ]
where:
n = volume exponent
CF = correction factor
P1 =pressure of the inlet stream
P2 = pressure of the exit stream
ρ1 = density of the inlet stream
F1 =molar flow rate of the inlet stream
M = molecular weight of the gas
Isentropic power is calculated by defining the volume exponent as:
Where:
n= ℓn (P2/P1)/(ℓn(ρ’2/ ρ1))
ρ’2 = density of the exit stream corresponding to the inlet entropy
Polytropic power is calculated by defining the volume exponent as:
Where:
ρ2 = density of the exit stream
n= ℓn (P2/P1)/(ℓn(ρ2/ ρ1))
173
The correction factor is calculated as:
CF= [h’2-h1] / [ (n/(n-1)) x (P2/ ρ’2)-(P1/ ρ’2)]
Where:
h’2 = enthalpy of the exit stream corresponding to the inlet entropy
h1 = enthalpy of the inlet stream
An isentropic flash is performed to calculate the values of h' 2 and ρ'2.
HYSYS calculates the compression rigorously by following the isentropic line
from the inlet to the exit pressure. The path of a polytropic process is neither
adiabatic nor isothermal. The only condition is that the polytropic process is
reversible.
B5. Equation used in HYSYS
If the compressor (icon) is selected, the compressor equations are used. If the
expander (icon) is selected, the expander equations are used.
GLOSSARY
Ideal :
Actual :
H
:
Out :
In
:
P
:
M
:
Z
:

:
f
:
n
:
k
:
Isentropic (100% Efficiency)
Given efficiency
Mass Enthalpy
Product Stream (discharge)
Feed Stream (suction)
Pressure
Molecular Weight
Compressibility Factor
Mass Density
Polytropic Head Factor
Polytropic Exponent
Isentropic Exponent
B5.1 COMPRESSOR – Efficiencies
The Adiabatic and Polytropic Efficiencies are included in the compressor
calculations. An isentropic flash (Pin and Entropyin) is performed internally to
obtain the ideal (isentropic) properties.
AdiabaticE ff 
Work Re quired (ideal)
Work Re quired ( actual)

( H out  H in ) ideal
H out  H in actual
174
(1)
 n 1 






P
 out   n   1   n    k  1 
 P 
  (n  1)   k 

in 



 
Polytropic Eff 
 AdiabaticE ff
 k 1 




 Pout   k   1
 P 

 in 

P
log out 
Pin 

n


log out,actual


in 

(3) and
P
log out 
Pin 

k


log out,ideal


in 

(2)
(4)
B5.2 COMPRESSOR – Heads
The Adiabatic and Polytropic Heads are performed after the compressor
calculations are completed, only when the “Results” page of the compressor is
selected. The Work Required (actual) is the compressor energy stream (heat
flow).
Work Re quired ( actual) 
1
AdiabaticH ead  
  AdiabaticE ff 
g g c 
 MassFlowRate 
(5)
The Polytropic Head is now calculated based on the ASME method (“The
Polytropic Analysis of Centrifugal Compressors”, Journal of Engineering for
Power, J.M. Schultz, January 1962, p. 69-82).
1
 n   Pout   Pin 
Polytropic Head  f  

  



 n  1    out,actual    in   g g c 
f 
H out,ideal  H in
(6)
(7)
 k   Pout   P in 


 

 k  1    out,ideal    in 
P
log out 
Pin 

n


log out,actual


in 

As in equation (3) above, and,
P
log out 
Pin 

k


log out,ideal


in 

As in equation (4) above
175
B5.3 EXPANDER - Efficiencies
The Adiabatic and Polytropic Efficiencies are parts of the expander calculations.
An isentropic flash (Pin and Entropyin) is performed to obtain the ideal
(isentropic) properties. The flash is done internally on the expander fluid, and
the results are not stored.
AdiabaticE ff 
Work Pr oduced ( actual)
Work Pr oduced (ideal)

H out  H in actual
( H out  H in ) ideal
 k 1 






P
 out   k   1
 P 

 in 

Polytropic Eff 
 AdiabaticE ff
 n 1 




 Pout   n   1   n    k  1 
 P 
  (n  1)   k 
in 



 
(8)
(9)
P
log out 
Pin 

n


log out,actual


in 

As in equation (3) above, and,
P
log out 
Pin 

k


log out,ideal


in 

As in equation (4) above
B5.4 EXPANDER - Heads
The Adiabatic and Polytropic Heads are performed after the expander
calculations are completed, only when the “Results” page of the expander is
selected. The Work Produced (actual) is the expander energy stream (heat
flow).
Work Pr oduced ( actual) 
1
1
AdiabaticH ead  


 MassFlowRate  AdiabaticE ff  g g c 
(10)
1
 n   Pout   Pin 
Polytropic Head   f  

  



 n  1    out,actual    in   g g c 
(11)
f 
H out,ideal  H in
 k   Pout   P in 


 

 k  1    out,ideal    in 
176
As in equation (7) above
P
log out 
Pin 

n


log out,actual


in 

As in equation (3) above
P
log out 
Pin 

k


log out,ideal


in 

As in equation (4) above
B5.5 APPLICATION IN SI UNITS
: Mass Enthalpy (kJ/kg)
: Pressure (kPa)
: Mass Density (kg/m3)
H
P

Equation (5):


Work Re quired ( actual) 
1
N m 
AdiabaticH ead (m)  
  AdiabaticE ff  1000
kJ
 MassFlowRate 
9.8066 N 
 kg 
Equation (6):
N 2
1
 m 
 n   Pout   Pin 
Polytropic Head (m)  f  


1000
  

 kPa  
 n  1    out,actual    in 

 9.8066 N 


 kg 
Equation (7):
f 
H


 H in   1000 N  m
kJ
N 2
 m 
 k   Pout   P in 


  
  1000



 k  1    out,ideal    in 
 kPa 


out,ideal
Equation (10):


 Work Pr oduced(actual) 
1
1
AdiabaticHead (m)  
 1000 N  m


kJ
MassFlowRate

 AdiabaticEff
9.8066 N 
 kg 
Equation (11):
N 2
1
 m 
 n   Pout   Pin 
Polytropic Head (m)   f  

  
  1000



 n  1    out,actual    in 
 kPa  9.8066 N 


 kg 
177
B5.6 APPLICATION IN FIELD UNITS
H
P
: Mass Enthalpy (Btu/lb)
: Pressure (psia = lbf/in2)

: Mass Density (lb/ft3)
Equation (5):
Work Re quired ( actual) 
1
 ft  lb f

AdiabaticH ead ( ft )  

  AdiabaticE ff  778.1692
Btu

 1 lb f 
 MassFlowRate 
 lb 


Equation (6):
2
1
 n   Pout   Pin  12in 
Polytropic Head ( ft )  f  

  

 


ft


 lb f 
 n  1    out,actual    in 
1

 lb 
Equation (7):
 ft  lb f

 H in   778.1692

Btu


f 
2
 k   Pout   P in  12in 



  



ft 
 k  1    out,ideal    in  
H
out,ideal
Equation (10):
Work Pr oduced ( actual) 
1
1
 ft  lb f

AdiabaticH ead ( ft )  
 778.1692


Btu
lb
MassFlowRa
te
AdiabaticE
ff

 1 f 


 lb 


Equation (11):
2
1
 n   Pout   Pin  12in 
Polytropic Head ( ft )   f  



  





ft 
 lb f 
 n  1    out,actual    in  
1

 lb 
178
B5.7 Compressor Discharge Temperature Calculation
P
Entropy Constant
S1-S2 =0
P2
T2R
P1
T2
T1
H1
H2
H2R
H
The above plot (Pressure Enthalpy) can be used to explain how HYSYS
calculate outlet temperature:
Ideal Path (blue curve) : the gas is compressed from P 1(Inlet Pressure) to P2
(Discharge Pressure) following the constant entropy curve.
1) At point 1 conditions are T 1(Inlet Temperature) and P1 are known+
composition and flow. HYSYS flash the stream and calculate enthalpy H 1 and
Entropy S1.
2) At point 2 the gas is compressed to P2 and S2 is known from S1-S2=0 and the
entropy (S(T,P)) is a function of T and P. T 2 (Discharge temperature) is calculate
using an iterative procedure.
When the calculation of T 2 are converged H2 and other properties at point 2 are
also calculated. Point 2 now is defined.
3) Ideal Work: W
ideal=
H2 –H1
4) To take the real path the efficiency (η) is introduced:
The real work = H2R- H1 = W
ideal/
η (R denotes real)
From this relation = H2R = H1 + (W ideal/ η)
In this point H2R and P2 are known and only T2R is unknown. By iterative
procedure HYSYS calculates T2R, after which all other properties are calculated.
179
Method used to interpolate compressor/expander curves in HYSYS
General description of compressor curve interpolation
In the HYSYS compressor operation, a cubic spline method is used for the interpolation of
the speed curves themselves and for the interpolation between speed curves. Here is an
outline of the procedure:
When multiple curves of different speed are provided (for example three curves for 9000 rpm,
11000 rpm and 13000 rpm), given the flow F, the first step is to calculate the Head and
Efficiency for the given flow (for each of the three curves) using a Cubic Spline Method (see
below). When the three pairs of data available (9000 rpm, HeadFor9000; 11000 rpm,
HeadFor11000; and 13000 rpm, HeadFor13000), the next step is to find out the head for a
specified speed for the speed specified (10000 rpm for example) using the Cubic Spline
Method again. The same procedure is used to find out the Efficiency.
If you input multiple curves into the compressor and then run the compressor at a speed
below the lowest curve (i.e. lowest speed), the method used in HYSYS includes a limited
functionality of extrapolation when the point lies outside of the data. Note that extrapolation
is generally not accurate enough depending on the degree of curve non-linearity.
Description of the cubic spline interpolation
The cubic polynomials are used to approximate the curve between each pair of data points.
The cubic spline uses a third-degree polynomial. Suppose there is a sets of data (t0,y0) .. (tn,
yn) known:
x:
y:
t0
y0
t1
y1
.....
....
tn
yn
The ti's are the knots and are assumed to be arranged in ascending order.
The cubic spline function S that is used to construct consists of n cubic polynomial pieces:
S(x)
= S0(x)
t0 <= x <= t1
= S1(x)
t1 <= x <= t2
......
= Sn-1(x)
tn-1 <= x <= tn
The interpolation conditions are:
S(ti) = yi (0 <= i <= n)
The other two conditions are:
S''(t0) = S''(tn) = 0
The function S can be determined as
S ( x)
zi 1
( x ti )3
6hi
zi
(t i 1
6hi
x) 3
(
yi 1
hi
hi
y
z i 1 )( x t i ) ( i
hi
6
hi
z i )(t i 1 x)
6
where h1 = ti+1- t1
When the values z0, z1, .. zn have been determined, the spline function S(x) is obtained from
equations of the above form for S0(x), S1(x), .., Sn-1(x).
180
The values z0, z1, .. zn can be obtained by solving a tridiagonal system of equations:
z0 = 0
hi-1zi-1 + ui zi + hi zi+1 = vi (1 <= i <= n-1)
zn = 0
where vi = 6(bi - bi-1)
bi = (1/hi) (yi+1 - yi)
181
Appendix C
Site Compressor data
(I)
(II)
(III)
Site base cases for health estimation
Site compressor untreated data log since 2006
Typical Performance Curves supplied by OEM
182
(I)
Site base cases for health estimation
Parameter
Base Case 1
Base Case 2
Base Case 3
Base Case 4
Base Case 5
Inlet T
322.06
318.95
320.15
323.15
313.15
Inlet P
1076
1057
1086
1050
1040
kPaa
Mol. Wt.
26.1
26.1
26.1
26.1
26.1
Kg/kmol
Outlet T
395.33
393.15
393.15
395.15
388.15
Outlet P
3102
3100
3120
3100
3120
kPaa
125,020
125,460
135,390
123,400
126,320
kg/hr
Shaft Power
N/A
5,596
6,042
5080
5430.4
Poly Efficiency
82.0
82.0
81.8
85.8
84.1
%
Flow rate
Rot Speed
K
K
kW
0.88
0.891
0.9
0.871
0.89
-
14:00
16:00
16:00
16:00
8:00
-
24/08/2009
06/07/2009
29/07/2009
23/08/2009
24/08/2009
-
Time
Test Date
Unit
Table C1A. Site compressor 5 base case data for analysis and diagnostics – August 2009
Referred
Parameter
Base Case 1
1.02144
Base Case 2
1.01158
Base Case 3
1.01538
Base Case 4
1.02490
Base Case 5
0.99318
1.00561
0.98785
1.01495
0.98131
0.97196
θ
Unit
-
δ
-
Inlet T
315.3
K
Inlet P
1070
kPaa
Mol. Wt.
Outlet T
372.98
374.54
24.6
373.13
371.55
376.62
Kg/kmol
K
Outlet P
2900.20
2950.43
2890.17
2970.10
3018.00
kPaa
Pressure
Ratio
2.71
2.76
2.70
2.78
2.82
-
Flow rate
118,984
120,960
127,288
120,554
122,650
kg/hr
Shaft Power
N/A
5198
5452
4720
5174
kW
Poly
Efficiency
Rot Speed
82.0
82.0
81.8
85.8
84.1
%
0.85
0.87
0.88
0.84
0.88
-
Date
24/08/2009
06/07/2009
29/07/2009
23/08/2009
24/08/2009
24/08/2009
Table C2A. The referred parameter values for the site compressor performance analysis
and diagnostics –August 2009
183
Parameter
Base Case
1
Base Case
2
Base Case
3
Base Case
4
Base Case
5
Base Case
6
Unit
Inlet T
308.15
310.15
321.15
314.15
309.15
303.15
Inlet P
1044
1034
1054
1008
9910
1024
kPaa
Mol. Wt.
26.2
26.2
26.2
26.2
26.2
26.2
Kg/kmol
Outlet T
384.15
386.15
396.15
391.15
387.15
379.15
Outlet P
3146
3151
3150
3232
3173
3096
kPaa
129,630
129,478
123,045
128,877
126,418
129,584
kg/hr
6,639
6,561
6,515
6,906
6,827
6573
kW
5,997
5,926
5,885
6,238
6,166
5,937
kW
82.0
82.0
81.8
85.8
84.1
83.8
%
Flow rate
Turbine
Power
Shaft Power
Poly
Efficiency
Rot Speed
Time
Test Date
K
K
0.89
0.89
0.89
0.91
0.9
0.88
-
08:00
12:00
16:00
20:00
00:00
04:00
-
06/04/2010
06/04/2010
06/04/2010
06/04/2010
06/04/2010
06/04/2010
-
Table C1B. Site compressor 5 base case data for analysis and diagnostics – April 2010
Referred
Parameter
Base Case
1
0.97732
Base Case
2
0.98367
Base Case
3
1.01855
Base Case
4
0.99635
Base Case 5
0.98049
Base Case
6
0.96147
0.97570
0.96636
0.98505
0.94206
0.92617
0.95701
Unit
-
θ
-
δ
315.3
K
1070
kPaa
24.6
Kg/kmol
Inlet T
Inlet P
Mol. Wt.
K
393.06
392.56
388.93
392.58
394.85
394.35
3224
3261
3198
3431
3430
3235
kPaa
3.01
3.05
2.99
3.21
3.2
3.02
-
123,322
124,772
118,367
128,215
122,546
124,663
6,217
6,183
5,920
6,634
6720
6,327
82.0
82.0
81.8
85.8
84.1
83.8
%
0.90
0.90
0.88
0.91
0.91
0.90
-
06/04/2010
06/04/2010
06/04/2010
06/04/2010
06/04/2010
06/04/2010
-
Outlet T
Outlet P
Pressure
Ratio
kg/hr
Flow rate
kW
Shaft Power
Poly
Efficiency
Rot Speed
Test Date
Table C2B. The referred parameter values for the site compressor performance analysis
and diagnostics –April 2010
184
(II)
Site compressor untreated data log since 2006
13/6/
06
13/6/0
6
13/6/0
6
14/6/0
6
14/6/0
6
1
2
3
4
5
08:0
0
16:00
00:00
08:00
16:00
20:00
00:00
38.2
10.1
0
39.1
32.0
36.6
41.0
32.4
10.90
10.20
10.40
10.30
24.6
24.6
24.6
Outlet T
24.6
119.
0
118.0
114.0
Outlet P
33
32.2
Flow rate
128,0
00
131,94
3
124.
6
5,29
7
Test Date
Test Time
Inlet T
Inlet P
Mol. Wt.
Poly Head
Shaft
Power
Poly
Efficiency
Rot Speed
14/6/06
1/8/08
9
10
10:19
08:00
12:00
31.5
38.7
36.0
47.0
C
9.75
10.52
10.10
11.00
11.40
24.6
24.6
24.6
24.6
25.6
25.6
Bara
Kg/k
mol
116.8
118.5
118.5
111.0
118.7
115.0
121.0
C
29.9
31.85
30.9
30.25
32
31.5
32
32
Bara
130,21
2
126,00
0
136,60
3
146,545
133,991
117,056
127,32
0
125,23
7
kg/hr
129.6
129.1
125.4
124.5
126.6
122.2
124.8
114.0
113.4
kJ/kg
5,301
5,440
5,160
5,440
6,520
5,367
4,805
5,114
4,954
kW
87.5
91.4
87.6
86.7
80.6
80.6
86.5
87.0
80.8
84.3
%
0.91
-
0.92
0.9
0.9
0.93
0.9
0.922
DCS
Screen
Shot
0.89
-
-
6
7
Remark
Test Date
Case
Test Time
Inlet T
Inlet P
Mol. Wt.
Outlet T
Outlet P
Flow rate
Poly Head
Shaft
Power
Poly
Efficiency
Rot Speed
1/8/
08
1/8/0
8
1/8/0
8
1/8/0
8
6/7/0
9
11
16:0
0
12
13
14
15
20:00
00:00
04:00
12:00
16:00
20:00
43.0
11.1
5
38.0
36.0
34.0
48.0
45.8
12.00
11.11
10.89
11.53
25.6
120.
0
32.7
6
125,
382
117.
3
25.6
25.6
25.6
117.0
116.0
32.20
123,4
12
7/9/06
8
-
6/7/0
9
23/8/
09
19
20
00:00
04:00
08:00
42.6
38.0
35.0
31.0
C
11.57
11.40
11.18
11.21
11.45
26.1
26.1
26.1
26.1
26.1
26.1
Bara
Kg/k
mol
116.0
122.0
120.0
117.0
117.0
114.0
119.0
C
32.40
123,6
33
31.89
123,0
40
31.90
131,1
31
32.00
32.40
32.60
127,160
143,604
31.98
123,4
39
Bara
135,410
32.00
127,5
97
6/7/09
16
6/7/09
17
6/7/09
18
kg/hr
105.8
114.4
114.5
109.2
108.4
110.1
111.9
108.4
109.3
kJ/kg
5,022
5,030
5,155
4,995
5,148
4,792
5,756
4,995
5,638
kW
84.4
74.0
80.0
77.5
81.2
80.8
82.8
79.1
85.0
68.0
%
-
-
-
-
-
0.891
0.921
0.93
0.9
0.88
-
23/8/
09
23/8/0
9
23/8/0
9
23/8/0
9
23/8/0
9
22
23
24
25
Test Time
21
12:0
0
16:00
20:00
00:00
04:00
08:00
12:00
Inlet T
31.4
31.0
31.0
31.3
31.2
35.0
37.0
Case
Unit
4,93
7
Remark
Test Date
Unit
1/8/08
Case
14/6/06
6/4/10
26
185
6/4/10
27
6/4/10
6/4/10
6/4/10
29
30
16:00
20:00
00:00
48.0
41.0
36.0
28
Unit
C
Inlet P
11.5
6
11.50
11.28
11.44
11.28
11.44
11.34
11.54
11.08
10.91
26.1
122.
0
32.3
8
123,
191
26.1
26.1
26.1
26.1
26.1
26.1
26.1
26.1
26.1
Bara
Kg/k
mol
122.0
116.0
115.0
113.0
111.0
113.0
123.0
118.0
114.0
C
32.00
123,4
10
32.00
124,4
00
32.38
123,9
99
32.21
121,8
58
32.46
32.51
32.5
129,478
123,045
32.73
126,4
18
Bara
129,630
33.32
128,8
77
Poly Head
Shaft
Power
Poly
Efficiency
66.0
65.1
71.6
72.5
75.3
107.3
109.1
111.2
115.9
113.9
kJ/kg
5,83
2
5,762
5,319
5,207
5,066
4,930
4,940
4,747
5,005
4,944
kW
66.0
65.1
71.6
72.5
75.3
79.9
81.0
81.7
84.5
82.5
%
Rot Speed
0.88
0.871
0.891
0.89
0.89
0.89
0.89
0.89
0.91
0.9
-
Mol. Wt.
Outlet T
Outlet P
Flow rate
Remark
Test Date
Case
Test Time
Inlet T
Inlet P
Mol. Wt.
Outlet T
Outlet P
Flow rate
Poly Head
Shaft
Power
Poly
Efficiency
Rot Speed
Remark
kg/hr
-
6/4/
10
24/8/
10
24/8/
10
31
04:0
0
32
33
08:00
14:00
30.0
11.2
4
40.0
48.9
C
11.40
11.76
26.1
106.
0
31.9
6
129,
584
110.
5
26.1
26.1
Bara
Kg/k
mol
115.0
122.2
C
32.20
126,3
20
32.02
125,0
20
Bara
Unit
kg/hr
108.6
107.6
kJ/kg
5,12
3
4,787
4,722
kW
85.1
81.2
80.0
%
0.88
0.89
0.89
DCS
Scree
n
Shot
-
-
186
(III) Typical Performance Curves supplied by OEM
187
Appendix D
Performance Adaptation by Successive Iteration – Calculation Details
188
Performance Adaptation by Successive Iteration
D.1 Site and OEM Compressor Data
Three (3) site base case data at various compressor speeds (2Q 2006) and a
wide range of OEM data are available for analysis in the proceeding works.
The site data are shown in Table D1 below. A sample of the OEM
performance curves for the site compressor closely matching the site
conditions are shown in Figures D1 and D2 and full range of OEM data
available for the compressor life cycle is shown in Table D15.
The site and OEM data will be treated as described in Chapter 4 for referring
and de-dimensioning in accordance with Chart 1 in Chapter 4. These data will
then be applied to deduce actual site performance using successive iteration
method in accordance with Chart 2 in Chapter 4.
Site data for method application
The compressor data at site for 3 bases cases ranging from N=0.996 to
N=0.900 are listed in Table D1 and these will be used in the analysis.
Table D1. Site compressor data for 3 base cases
Parameter
Base Case 1
Base Case 2
Base Case 3
Unit
Flow rate
150,400
145,000
126,000
kg/hr
14370
13190
11220
Act m3/hr
Mol. Wt
24.6
24.6
24.6
kg/kmol
Inlet Press
1070
1090
1070
kPaa
Inlet Temp
42.1/315.3
34/307.2
23.5/296.7
Cº / Kº
Outlet Press
3130
3010
3200
kPaa
Outlet Temp
125/398.2
111.3/384.5
105/378.2
Cº / Kº
Rot Speed
0.996
0.93
0.900
-
OEM data for analysis
The unique advantage in this study is that there is a very wide range of OEM
supplied data available for utilization and analysis. The OEM performance
curves for various rotational speeds together with the associated inlet gas
properties and compressor inlet conditions are listed in Table D15 at the end
of this Appendix. Referring to this table, OEM Case Reference no. (17) is
189
ideally chosen for analysis as it closely resembles the site conditions. Figures
D1-D2 show the performance curves for this case as supplied by OEM and
Tables D2A-D7A list the numerical values of these curves.
The OEM curves are generated for a fleet of compressors rather than the
specific compressor at site. However, these predicted curves are expected to
have a fairly accurate (although not exact) representation of expected
performance as the compressor is shop tested and commissioned under
observed conditions under the ASME PTC10 tolerance requirements.
190
Figure D1. Pressure Ratio and Discharge Temperature versus Compressor
Inlet Flow as supplied by OEM
191
Figure D2. Polytropic Head and Efficiency versus Compressor Inlet Flow as
supplied by OEM
192
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
C
kg/h
kJ/kg
%
kW
10000
3.830
38.5
132.0
103207
152.0
82.0
5400
10500
3.820
38.4
130.4
108367
151.2
82.7
5575
11000
3.790
38.1
128.9
113527
150.0
83.3
5742
11500
3.760
37.8
127.4
118688
148.5
84.0
5915
12000
3.720
37.4
126.1
123848
146.7
84.5
6075
12500
3.675
37.0
124.5
129008
144.5
84.5
6235
13000
3.623
36.4
123.0
134169
142.3
84.5
6390
13500
3.560
35.8
121.5
139329
140.2
84.5
6505
14000
3.490
35.1
120.0
144489
137.6
84.5
6590
14370
3.430
34.5
118.5
148308
135.3
84.5
6640
14500
3.410
34.3
118.0
149650
134.5
84.5
6660
15000
3.310
33.3
116.0
154810
131.0
84.5
6720
15500
3.190
32.1
114.0
159970
127.3
84.5
6770
16000
3.040
30.6
111.8
165131
122.0
84.0
6795
16500
2.870
28.9
109.0
170291
115.0
81.0
6815
17000
2.700
27.2
106.0
175451
107.0
77.5
6810
o
Table D2A. Rated Process Conditions Direct From OEM for N=1 (9500 RPM)
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
kg/h
kJ/kg
%
kW
137.0
80.3
4520
o
C
9200
9500
3.410
34.3
121.8
98046
135.0
81.7
4630
10000
3.407
34.3
120.4
103207
133.0
82.9
4780
10500
3.393
34.1
119.0
108367
131.4
83.7
4920
193
11000
3.380
34.0
117.5
113527
129.9
84.3
5055
11500
3.320
33.4
116.1
118688
129.2
84.4
5160
12000
3.281
33.0
114.7
123848
128.2
84.4
5260
12500
3.228
32.5
113.2
129008
126.4
84.5
5350
13000
3.171
31.9
111.6
134169
124.3
84.5
5435
13500
3.104
31.2
109.9
139329
121.5
84.5
5495
14000
3.030
30.5
108.1
144489
120.3
84.5
5560
14370
2.950
29.7
106.8
148308
118.1
84.5
5595
14500
2.930
29.5
106.4
149650
114.1
84.5
5610
15000
2.820
28.4
104.2
154810
109.7
83.6
5655
15500
2.700
27.2
102.0
159970
104.3
82.0
5685
16000
2.550
25.7
99.5
165131
97.1
79.5
5700
16500
2.385
24.0
170291
Table D3A. Rated Process Conditions Direct From OEM for N=0.952 (9048 RPM)
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
C
kg/h
kJ/kg
%
kW
9000
3.345
33.7
120.2
92886
134.0
80.5
4310
9500
3.323
33.4
118.8
98046
132.5
81.8
4440
10000
3.300
33.2
117.5
103207
131.0
83.0
4580
10500
3.273
32.9
116.0
108367
129.4
83.7
4740
11000
3.245
32.6
114.8
113527
127.9
84.4
4880
11220
3.220
32.4
114.1
115798
127.0
84.4
4930
11500
3.195
32.1
113.3
118688
126.0
84.4
5000
12000
3.145
31.6
112.0
123848
124.2
84.5
5100
12500
3.073
30.9
110.5
129008
122.1
84.5
5200
13000
3.000
30.2
109.0
134169
119.3
84.5
5290
13190
2.980
30.0
108.2
136130
118.0
84.5
5310
o
194
13500
2.917
29.3
107.0
139329
115.8
84.5
5350
14000
2.830
28.5
105.0
144489
111.8
84.5
5400
14500
2.705
27.2
102.8
149650
107.4
83.7
5450
15000
2.580
26.0
100.5
154810
102.0
82.6
5485
15500
2.420
24.3
97.8
159970
94.8
80.4
5500
16000
2.260
22.7
95.0
165131
78.0
5500
Table D4A. Rated Process Conditions Direct From OEM for N=0.941 (8936 RPM)
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
C
kg/h
kJ/kg
%
kW
106.0
79469
110.6
82.0
3038
7700
o
8000
2.770
27.9
105.0
82565
110.5
82.5
3115
8500
2.736
27.5
103.6
87726
109.6
83.3
3250
9000
2.710
27.3
102.5
92886
108.8
84.0
3385
9500
2.680
27.0
101.2
98046
106.8
84.3
3500
10000
2.640
26.6
99.8
103207
104.8
84.5
3615
10500
2.601
26.2
98.3
108367
102.6
84.5
3710
11000
2.550
25.7
96.8
113527
100.3
84.5
3810
11500
2.500
25.2
95.0
118688
97.7
84.3
3890
12000
2.420
24.3
93.2
123848
94.1
84.0
3950
12500
2.337
23.5
91.6
129008
90.6
83.4
3985
13000
2.225
22.4
89.9
134169
85.6
82.0
4000
88.5
137265
81.5
79.5
4008
13300
Table D5A. Rated Process Conditions Direct From OEM for N=0.857 (8143 RPM)
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
C
kg/h
kJ/kg
%
kW
89.8
70181
86.8
82.8
2077
6800
o
195
7000
2.270
22.8
89.2
72245
86.4
83.0
2115
7500
2.250
22.6
87.8
77405
85.8
83.9
2231
8000
2.200
22.1
86.3
82565
83.8
84.3
2315
8500
2.170
21.8
85.0
87726
82.6
84.5
2400
9000
2.120
21.3
83.4
92886
80.0
84.5
2469
9500
2.081
20.9
82.0
98046
77.4
84.5
2538
10000
2.028
20.4
80.6
103207
74.1
83.5
2608
10500
1.950
19.6
79.0
108367
70.6
82.0
2638
11000
1.860
18.7
77.2
113527
66.8
80.0
2692
Table D6A. Rated Process Conditions Direct From OEM for N=0.762 (7238 RPM)
Vol.
Press.
Disch.
Disch.
Mass
Polytrop
Polytrop
Comp
Flow
Rate
Ratio
Pressure
Temp
Flow
Head
Efficiency
Shaft
Power
m3/h
PR
bara
C
kg/h
kJ/kg
%
kW
6000
1.840
18.5
75.0
61924
65.6
83.0
1385
6500
1.810
18.2
74.0
67084
64.4
83.8
1462
7000
1.770
17.8
72.9
72245
62.7
84.2
1538
7500
1.730
17.4
71.5
77405
61.4
84.3
1600
8000
1.690
17.0
70.2
82565
57.9
83.8
1650
8500
1.640
16.5
68.6
87726
55.2
82.7
1692
9000
1.580
15.9
67.0
92886
51.8
81.0
1725
o
Table D7A. Rated Process Conditions Direct From OEM for N=0.667 (6334 RPM)
Table D8 below summarizes the gas inlet conditions as supplied by OEM
used in the analysis. It may be noted that it is a pure coincident that a single
set of OEM compressor data (One of the OEM Case References) resembles
the compressor site conditions in Table D1.
Parameter
Mol. Wt
Rated Performance Data By OEM
(OEM Ref Case 17 of Table D15)
24.88
196
Inlet P / kPaa
1006
Inlet T /K
303.85
Suction Z
0.960
Sp. Ht. Ratio
1.236
Table D8. OEM Supplied Compressor Data chosen for analysis
197
D.2 Compressor Data Analysis
D.2.1 Referring of OEM Performance Data
The OEM performance curves are supplied from the vendor for the rated
conditions based on a set of predefined compressor inlet conditions and
physical properties of throughput gas. However, the gas physical, gas inlet
and environmental conditions at site as well as the rotational speed hardly
ever matches with the OEM base data; therefore the OEM performance data
and performance curves need to be modified to enable comparison with the
site conditions. The most common condition of gas inlet to the site
compressor is nominally chosen as the datum or Referred conditions. The
closest available set of OEM data (Table D15) to the referred conditions is the
Site Base Case 1 (see Table D1) and these are listed in Table D9.
Referred Conditions (site base
case 1, Table D1)
Rated OEM Conditions (OEM Ref Case
17 of Table D15)
Gas Handled
Site Gas
Specific Gas
Mol. Weight
24.60
24.88
Suction Pressure, kPaa
1070
1006
Intake Temperature, K
315.30
303.85
Compressibility
0.959
0.960
Cp/Cv
1.210
1.236
Table D9 Basis of Conditions for generation of Referred OEM Performance Curves
D.2.1.1 Sample Calculation for the Determination of Referred Values for
the OEM and Site Data
Reference to the equations stated and developed under Chapter 4.1 in
Chapter 4, the following equations are used:
Theta (θ) = T1/ Tref (Dimensionless)
(1)
Delta (δ) = P1/ Pref (Dimensionless)
(2)
The subscript “1” refers to compressor inlet conditions. T ref and Pref are
arbitrary chosen values as a base temperature and base pressure upon which
all the operating variables either from OEM or site are compared. It is normal
to set reference values same as the most common site compressor inlet
conditions for temperature, pressure and the molecular weight controlling the
198
mass flow. For an “n” set of site cases, there will be an “n” set of Thetas (θ)
and Deltas (δ).
The flowchart for the methodology of the performance scaling is shown in
chart 1 in Chapter 4.
The following are the equations used to refer the OEM and test data:
Treferred = Tn / θ
Preferred = Pn / δ
mreferred = W * √ (θ) / δ
PW referred = PW/ ( δ * √ (θ) )
Nreferred = N/√ (θ)
EEPreferred = EEP / 1
(3)
(4)
(5)
(6)
(7)
(8)
Where, n=1, 2
In this work, subscript “1” refers to inlet of compressor and “2” refers to the
compressor outlet.
The Referring of OEM Data
Referring to equations (1) and (2) above:
Theta, θ = T1/ Tref
Delta, δ = P1/ Pref
Now,
T1 = 303.85 K (from Table D8)
Tref = 315.3 (Base Case 1, from Table D1)
Therefore:
θ = 303.85/315.3
θ = 0.964
And,
P1 = 1006 kPaa (from Table D8)
Pref = 1070 kPaa (Base Case 1, from Table D1)
δ = 1006/1070
δ = 0.940
Other cases follow suit. The Theta and Delta for OEM data are shown in
Table D10.
199
Scaling Parameter
Value for OEM
θ
0.964
δ
0.940
Table D10. Theta and Delta for OEM data
Note that for n set of cases there will be expected to be an n set of Theta and
Delta values. But here there is only one OEM case suitably close to all
available compressor inlet conditions at site. This is why there is one unique θ
and one unique δ.
By using Equations (3) to (9), these are the calculation procedures to obtain
the referred values for OEM data:
From Table D8 for rated OEM data at the compressor inlet:
Mol. Wt
:
24.88
Inlet P / kPaa :
1006
Inlet T /K
303.85
:
From Table D2A:
N=1 (9500 RPM),
q=10,000 m3/hr
PR=3.83
Outlet T= 132.0 C (405.15 K)
EEP=82.0%
We need to change volume flow into mass flow; using equation (9) for the
mass flow rate:
Mass Flow, w = 10,000 x (1006 x 24.88) / (0.960 x 8.314 x 303.85) = 103, 207
kg/hr
Outlet P = Inlet P x PR = 1006 x 3.83 = 3853 kPaa (or 38.5 Bara)
Now, from Table D10 :
θ = 0.964
δ = 0.940
By applying the values of θ and δ above into equations 3-8, the corresponding
referred values are obtained:
Referred Mass Flowrate = 103, 207 * √ (0.964) / 0.94 * (24.6/24.88) = 106,548
kg/hr
200
The last term in the equation is the correction for molecular weight change
from OEM to the referred value.
Referred Outlet T = 405.15 / 0.964 = 420.4 K
Referred Outlet P = 3853 / 0.94 = 4090 kPaa
Referred Shaft Power = 5400/ [0.94 * √ (0.964)] = 5851 kW
Referred N = 1 / √ (0.964) = 1.019
Referred EEP = 82 / 1 = 82.0 (Note that referred polytropic efficiency is same
as OEM [Ref. 56] )
Having shown the sample calculation the referred values for each OEM case
are developed and tabulated in Tables D2B-D7B. These referred Tables
correspond to Tables D2A-D7A which are the untreated or uncorrected OEM
data.
Table D2B. Corrected or Referred parameters of OEM data for N=1 (based on Table D2A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
kg/hr
K
Bara
-
kW
%
-
106548
420.4
40.9
3.827
5851
82.0
1.0187
111876
418.8
40.8
3.817
6040
82.7
1.0187
117203
417.2
40.5
3.787
6221
83.3
1.0187
122531
415.6
40.2
3.757
6409
84.0
1.0187
127858
414.3
39.8
3.718
6582
84.5
1.0187
133185
412.6
39.4
3.678
6755
84.5
1.0187
138513
411.1
38.7
3.618
6923
84.5
1.0187
143840
409.5
38.1
3.559
7048
84.5
1.0187
149167
408.0
37.3
3.489
7140
84.5
1.0187
153110
406.4
36.7
3.429
7194
84.5
1.0187
154495
405.9
36.5
3.410
7216
84.5
1.0187
159822
403.8
35.4
3.310
7281
84.5
1.0187
165149
401.7
34.1
3.191
7335
84.5
1.0187
170477
399.5
32.5
3.042
7362
84.0
1.0187
201
175804
396.6
30.7
2.873
7384
81.0
1.0187
181132
393.4
28.9
2.704
7378
77.5
1.0187
Table D3B. Corrected or Referred parameters of OEM data for N=0.952 (based on Table
D3A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
kg/hr
K
Bara
-
kW
%
-
101221
409.8
36.5
3.410
5016
81.7
0.9698
106548
408.4
36.5
3.407
5179
82.9
0.9698
111876
406.9
36.3
3.393
5331
83.7
0.9698
117203
405.4
36.2
3.380
5477
84.3
0.9698
122530
403.9
35.5
3.320
5591
84.4
0.9698
127858
402.5
35.1
3.281
5699
84.4
0.9698
133185
400.9
34.5
3.228
5797
84.5
0.9698
138513
399.2
33.9
3.171
5889
84.5
0.9698
143840
397.4
33.2
3.104
5954
84.5
0.9698
149167
395.6
32.4
3.030
6024
84.5
0.9698
153110
394.3
31.6
2.950
6062
84.5
0.9698
154495
393.9
31.4
2.930
6078
84.5
0.9698
159822
391.6
30.2
2.820
6127
83.6
0.9698
165150
389.3
28.9
2.700
6160
82.0
0.9698
170477
386.7
27.3
2.550
6176
79.5
0.9698
175804
-
25.5
2.385
-
-
-
Table D4B. Corrected or Referred parameters of OEM data for N=0.941 (based on Table
D4A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
202
kg/hr
K
Bara
-
kW
%
-
95893
408.2
35.8
3.350
4670
80.5
0.9586
101220
406.7
35.5
3.320
4811
81.8
0.9586
106548
405.4
35.3
3.300
4962
83.0
0.9586
111876
403.8
35.0
3.270
5136
83.7
0.9586
117203
402.6
34.7
3.241
5287
84.4
0.9586
119547
401.8
34.5
3.221
5342
84.4
0.9586
122531
401.0
34.1
3.191
5417
84.4
0.9586
127858
399.7
33.6
3.141
5526
84.5
0.9586
133185
398.1
32.9
3.072
5634
84.5
0.9586
138513
396.6
32.1
3.002
5732
84.5
0.9586
140537
395.7
31.9
2.982
5753
84.5
0.9586
143840
394.5
31.2
2.913
5797
84.5
0.9586
149167
392.4
30.3
2.833
5851
84.5
0.9586
154495
390.1
28.9
2.704
5905
83.7
0.9586
159822
387.7
27.7
2.584
5943
82.6
0.9586
165149
384.9
25.8
2.416
5959
80.4
0.9586
170477
382.0
24.1
2.256
5959
78.0
0.9586
Table D5B. Corrected or Referred parameters of OEM data for N=0.857 (based on Table
D5A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
kg/hr
K
Bara
-
kW
%
-
82042
393.4
-
-
3292
82.0
0.8730
85239
392.4
29.6
2.770
3375
82.5
0.8730
90566
390.9
29.3
2.736
3521
83.3
0.8730
95893
389.8
29.0
2.710
3667
84.0
0.8730
101221
388.4
28.7
2.680
3792
84.3
0.8730
203
106548
387.0
28.2
2.640
3917
84.5
0.8730
111876
385.4
27.8
2.601
4020
84.5
0.8730
117203
383.9
27.3
2.550
4128
84.5
0.8730
122530
382.0
26.8
2.500
4215
84.3
0.8730
127858
380.2
25.9
2.420
4280
84.0
0.8730
133185
378.4
25.0
2.337
4317
83.4
0.8730
138513
376.7
23.8
2.225
4334
82.0
0.8730
141709
375.3
-
-
4342
79.5
0.8730
Table D6B. Corrected or Referred parameters of OEM data for N=0.762 (based on Table
D6A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
kg/hr
K
Bara
-
kW
%
-
72453
376.6
-
-
2250
82.8
0.7762
74584
376.0
24.3
2.270
2292
83.0
0.7762
79911
374.6
24.1
2.250
2417
83.9
0.7762
85239
373.0
23.5
2.200
2509
84.3
0.7762
90566
371.6
23.2
2.170
2600
84.5
0.7762
95893
370.0
22.7
2.120
2675
84.5
0.7762
101221
368.5
22.3
2.081
2750
84.5
0.7762
106548
367.1
21.7
2.028
2825
83.5
0.7762
111876
365.4
20.9
1.950
2859
82.0
0.7762
117203
363.6
19.9
1.860
2917
80.0
0.7762
Table D7B. Corrected or Referred parameters of OEM data for N=0.667 (based on Table
D7A)
Referred
Referred
Referred
Referred
Referred
Referred
Referred
Mass Flow
Discharge T
Discharge P
Pressure
Ratio
Shaft
Power
Polytropic
Eff
N
kg/hr
K
Bara
-
kW
%
-
204
63929
361.3
19.7
1.840
1500
83.0
0.6795
69256
360.2
19.4
1.810
1584
83.8
0.6795
74584
359.1
18.9
1.770
1667
84.2
0.6795
79911
357.6
18.5
1.730
1734
84.3
0.6795
85239
356.3
18.1
1.690
1788
83.8
0.6795
90566
354.6
17.5
1.640
1834
82.7
0.6795
95893
353.0
16.9
1.580
1869
81.0
0.6795
The Referring of Site Performance Data
The determination of Theta and Delta for the Site Base Cases will follow the
same format as OEM described above and these are listed in Table
D11below.
Theta (θ) and Delta (δ) will be unity for the base case 1 because this case
was used as reference.
For site Base Case 2,
T1 = 307.2 K (from Table D1)
Tref = 315.3 (from Table D9)
Therefore for Site Base case 2,
θ = 307.2/315.3
θ = 0.9743
And,
P1 = 1090 kPaa (from Table D1)
Pref = 1070 kPaa (from Table D9)
δ = 1090/1070
δ = 1.0187
It may be noted that Theta and Delta are always unity for the datum values.
Base case 1
θ
1.0000
Base case 2
0.9743
205
Base case 3
0.9410
δ
1.0000
1.0187
Table D11. Theta and Delta for Site data
206
1.0000
By using Equations (3) to (8) and applying the values in Tables D10 and D11,
the following referred values are obtained for Site data in Table D12. It is to be
noted that the inlet conditions are the same for all cases.
Parameter
Base Case
1
Base Case
2
Base
Case 3
Unit
Inlet T
315.3
K
Inlet P
1070
kPaa
Mol. Wt.
24.6
kg/kmol
Outlet T
398.2
394.6
401.9
K
Outlet P
3130
2955
3200
kPaa
PR
2.93
2.76
2.99
-
150,400
140,499
122,227
kg/hr
Power
6784
6075
5775
kW
Rot Speed
0.996
0.942
0.928
-
Flow rate
Table D12. Corrected or Referred Site Test Data
D.2.3 Referred OEM data interpolation and deduction of scale factors
The numerical referred OEM speeds do not match the site referred speeds.
From Table D12 the referred speeds are 0.996, 0.942 and 0.928. We do not
have referred OEM speeds for these referred site speeds. Therefore the site
and OEM speeds are required to be made the same. In order to do this,
interpolate is needed, as described in Chapter 4, between the appropriate
speeds that fall above and below the referred OEM speed and these are the
details how the interpolation is accurately carried out:
Look at TWO referred OEM speeds that encompass the site referred speed,
case by case. For Site Base Case 1 (Table D1), there is a referred speed of
0.996. We do not have a referred OEM speed of 0.996 but there is a
performance curve for OEM referred speed of 1.0187 (Table D2B) and
another curve for OEM N referred value of 0.9698 (Table D3B). Thus the site Nref
of 0.996 falls between these two OEM values and OEM curves are generated
by dividing the space between two OEM curves into quality lines one of which
matched the site referred speed of 0.996. A Scale Factor is deduced by
comparing the Site referred pressure ratio with OEM referred pressure ratio
for the same referred speeds of OEM and Site and this scale factor is then
applied to the whole of the OEM referred curve to establish the site PR versus
207
referred mass flow curve. The referred OEM speed curves lower than 0.996
are shifted by the same scale factor.
For Pressure Ratios and Polytropic efficiencies the scale factors are
summarized in Table D13 and Table D14 respectively.
Site base case 1
Site base case 2
Site base case 3
Referred N
0.996
0.942
0.928
Scale Factor
0.896
1.118
1.116
Table D13. Pressure Ratio Scale Factors for site and OEM data matching
Site base case 1
Site base case 2
Site base case 3
Referred N
0.996
0.942
0.928
Scale Factor
1.024
1.032
1.005
Table D14. Polytropic Efficiency Scale Factors for site and OEM data matching
D2.4 Performance Adaptation by Successive Iteration
Reference to Chart 2 in Chapter 4, it is stated that for performance adaptation
by successive iteration the scale factor need to be obtained (by comparing
actual to ideal performance at the prevailing conditions) starting from the
highest referred speed in the first iteration, and shift all the performance maps
accordingly. Going to the second highest speed in the second iteration, the 1 st
speed curve remains fixed and the 2 nd speed curve and all the lower curves
are shifted down by the scale obtained in the 2 nd iteration. In the 3rd iteration,
the 1st and second speed curve stays fixed and the 3 rd and lower speeds are
shifted by the scale factor obtained from the 3 rd highest speed. This is
repeated until all referred test data are completed. At the end, there will be a
set of performance curves on the referred and modified OEM curves with the
test points falling exactly on the curves giving a much accurate prediction of
actual performance
The same procedure is applied to other site data and variables versus
referred mass flow rate and the overall performance fro pressure ratio is
shown in Graph D1.
Applying the same procedures above to polytropic efficiencies, Graph D2 is
obtained representing the polytropic efficiency versus referred mass flow rate
for the referred test site speeds as well as predictions for the lower speeds.
Graph D3 illustrates the superimposition of extreme winter and extreme
summer case on the referred PR versus referred mass flow. As expected,
there is good overlap of referred performance curves because regardless of
208
environmental conditions, it is still the same compressor performing and small
differences are contributed to differences in referred RPMs and tolerances in
performance predictions. The superimposition of extreme cases is for
illustration purposes only and it is recommended that the datum OEM data
chosen to establish theta and delta should be carefully chosen such that
differences between the site inlet conditions and the referred case or datum
are as close to each other as possible.
OEM and Site Compressor Referred Performance Data
PR Vesus Mass Flow
N referred=1.0187
5.000
N referred=0.9698
N referred=0.8730
4.500
N referred=0.7762
N referred=0.6795
4.000
N referred=0.996
PR (Referred)
N referred -site=0.996
PR=2.93, M=150400kg/hr
(Base case 1)
Nreferred=0.9698 - Scaled
3.500
N referred=0.8730 -Scaled
N referred=0.7762 -Scaled
3.000
N referred=0.6795 -Scaled
2.500
2.000
1.500
1.000
50000
60000
70000
80000
90000
100000
110000
120000
130000
Mass Flowrate (Referred), kg/hr
209
140000
150000
160000
170000
180000
190000
200000
Graph D1. Performance Adaptation by Successive Iteration for Pressure
Ratio
Graph D2. Performance Adaptation by Successive Iteration for Polytropic
Efficiency
210
Graph D3. Referred values obtained from extreme summer and extreme
winter OEM data (reference Table D15, below) superimposed on data derived
for OEM reference case.
211
Table D15. The Conditions At Which Predicted Performance Curves Are Available From OEM For The Site Compressor
OEM
Case
Ref.
Graph
Indicator
Time
M
Case
No.
No.
Intake
Press
Intake
Temp
Bara
C
[email protected]
Suction
SPEED RPM CURVES BY OEM
Cp/Cv
A
B
C
D
E
F
1
1
summer
04
27.24
11.31
45.3
0.953
1.213
9500
9048
8671
8142
7238
6333
2
11
summer
05
26.27
10.01
40.2
0.96
1.222
9500
9048
8824
8143
7238
6334
3
3
winter 04
25.71
10.42
17
0.949
1.239
9500
9048
8604
8142
7238
6333
4
4
summer
04
27.42
10.3
44.6
0.956
1.21
9500
9048
8526
8142
7238
6333
5
5
winter 11
24.59
9.43
6.1
0.952
1.251
9500
9048
8870
8142
7238
6333
6
6
winter 11
26.29
10.12
25.2
0.951
1.227
9500
9048
8523
8142
7238
6333
7
7
summer
04
27.57
11.06
47.4
0.953
1.209
9500
9048
8768
8142
7238
6333
8
8
summer
04
26.77
11.13
44.6
0.955
1.216
9500
9048
8671
8142
7238
6333
9
9
summer
04
27.52
11.1
46.7
0.953
1.21
9429
8980
8795
8081
7184
6285
10
10
summer
04
27.68
11.05
47.3
0.953
1.209
9492
9040
8763
8135
7232
6327
11
11
summer
26.02
9.32
40.1
0.963
1.222
9500
9188
9048
8142
7238
6333
12
11
winter
23.33
8.79
3.7
0.96
1.264
9500
9463
9048
8142
7238
6333
13
1
summer
04
28.24
11.77
45.5
0.947
1.207
9500
9048
8180
7238
6334
14
2
winter 04
25.18
10.28
19.4
0.954
1.243
9500
9048
8677
8143
7238
15
3
winter 04
26.92
11.1
20.5
0.941
1.23
9500
9048
8180
7238
6334
16
4
summer
11
27.69
10.86
43.1
0.952
1.21
9500
9048
8224
7238
6334
17
5
winter 11
24.88
10.06
30.7
0.96
1.236
9500
9048
8936
8143
7238
6334
18
6
winter 11
26.84
10.99
23.9
0.943
1.225
9500
9048
8143
8033
7238
6334
19
7
summer
04
28.26
11.75
45.5
0.947
1.207
9500
9048
8210
7238
6334
20
8
summer
04
28.15
11.8
45.4
0.947
1.208
9500
9048
8208
7238
6334
21
9
summer
04
26.92
11.79
47
0.953
1.215
9500
9048
8349
8143
7238
22
10
summer
04
28.24
11.77
45.4
0.947
1.207
9500
9048
8208
7238
6334
23
11
winter
23.48
9.4
4.1
0.957
1.264
9500
9135
9048
8143
7238
212
6334
6334
6334
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