OPTIMIZATION OF ELEMENTAL SULPHUR RECOVERY DURING ACID MINE WATER TREATMENT

OPTIMIZATION OF ELEMENTAL SULPHUR RECOVERY DURING ACID MINE WATER TREATMENT

OPTIMIZATION OF ELEMENTAL SULPHUR RECOVERY DURING ACID MINE

WATER TREATMENT

FRANCINE DIMITE ALESIA

A thesis submitted in fulfilment of requirements for the degree of

MAGISTER SCIENTIAE: ENVIRONMENTAL TECHNOLOGY in the

FACULTY OF ENGINEERING, BUILT ENVIRONMENT AND INFORMATION

TECHNOLOGY

UNIVERSITY OF PRETORIA

2014

ABSTRACT

Title: Optimization of Elemental Sulphur Recovery during an Acid Mine

Water Treatment

Author:

Supervisor:

Department:

Francine Dimite Alesia

Professor Evans M.N. Chirwa

Chemical Engineering

University of Pretoria University:

Degree: Master of Science (Applied Science): Environmental Technology

The South African mining industry is a major contributor to South Africa’s Gross Domestic product (GDP). The negative consequences of mining include toxic effluents from mineral processing and decanting streams, even after mine closure. Large volumes of Acid Mine

Drainage (AMD) are expected to increase as the mining industry grows. Currently, biological treatment of mine waters are preferred to chemical methods, due to various advantages offered such as low operational cost and small environmental footprint. Biological treatment of AMD primarily rely on the activity of sulphate reducing bacteria which reduce sulphate to sulphide in the presence of organic matter thus allowing the precipitation of the metals and increase in pH. However, excess of sulphide remains in the system and if not removed, can be oxidized to sulphate.

A sustainable AMD management plan could entail development of treatment technologies to remove total sulphur (sum of sulphur species) from the system. Production of elemental sulphur, which involves partial oxidation of sulphide, has been a recent subject of interest.

The use of colourless sulphide oxidizing bacteria, especially Thiobacillus species has been widely reported.

Six isolates of sulphide oxidizing microorganisms, of which 4 bacterial and 2 filamentous fungal species from a gold mine (Johannesburg, South Africa) were tested in this study to achieve partial oxidation of sulphide to sulphur. The microbial species were selected for high sulphide oxidation in the presence of carbon sources (glucose and lactate).

Lysinicibacillus

fusiformis was observed to be the most suitable microorganism for sulphide oxidation. In

-i-

order to investigate optimal conditions for sulphur recovery,

L. fusiformis bacterial activity was tested under different conditions of pH and redox potential. It resulted that at a pH of 8 and

Eh of -80mV up to 95% of sulphur was recovered.

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DECLARATION

I Francine Dimite Alesia, declare that the thesis which I hereby submit for a Master of

Science in Environmental Technology degree at the University of Pretoria is my own work and has not been previously submitted by me for any degree at this or other institutions.

Francine Dimite Alesia Date

Evans M.N. Chirwa Date

(Promotor)

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Dedicated to:

The MOUDIO MONEY Family

A family who teaches me every day about God, Love, Unity and Family values.

EYINDO MOUYEMA MARIE-THÈRÈSE

For the seed of faith that grows in me, and;

PRISO KOTTO ISIDORE JOAQUIM

My Hero.

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ACKNOWLEDGEMENTS

Sincere gratitude and appreciation is expressed to my supervisor, Prof Evans M.N. Chirwa for giving me the opportunity to study in his research group, for his patience, support and direction.

To Alette Devega, for attending to my every needs around the laboratory beyond the call of duty.

The author’s gratitude also goes to her colleagues from the research group at Water

Utilization Division of the Department of Chemical Engineering, University of Pretoria and her friends including the “Cool Kids” for their support, encouragement, laughter and enthusiasm.

The author is grateful to her god mother, M. Mbella, and her late grand-aunt Sita Manga for their love for life and family and to her lovely Aunt M. Moukoko Sosso Franciska for her faith and strength in harsh times. She will cherish every single memory she share with them.

The author is also very grateful to her brother, Stean Herve Dimite, her mother Moudio Son

Odile and her father Priso Kotto Isidore Joaquim, for their financial, spiritual and moral support.

The author is finally grateful to God, Jesus Christ and the Holy Spirit for their guidance from day 1.

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TABLE OF CONTENTS

ABSTRACT .......................................................................................................................... i

LIST OF FIGURES ............................................................................................................. x

LIST OF TABLES ............................................................................................................. xii

LIST OF ABBREVIATIONS .......................................................................................... xiii

CHAPTER 1 - INTRODUCTION .................................................................................... 14

1.1 Background .............................................................................................................................................. 14

1.2 Objectives of Study .................................................................................................................................. 15

1.3 Outline of Dissertation ............................................................................................................................ 15

CHAPTER 2 -LITERATURE REVIEW .......................................................................... 17

2.1 Acid Mine Drainage ................................................................................................................................ 17

2.1.1 The Sulphur Cycle ................................................................................................................................. 17

2.1.2 Formation of Acid Mine Drainage ........................................................................................................ 18

2.1.3 Impacts of Acid Mine Drainage............................................................................................................ 19

2.2 Production of Sulphide ............................................................................................................................ 21

2.2.1 Physiological and Environmental Effects of Sulphide.......................................................................... 23

2.3 Treatment of Acid Mine Drainage .......................................................................................................... 24

2.3.1 Passive Treatment Systems ................................................................................................................... 25

2.3.1.1 Examples of physic-chemical processes used as passive systems ..................................................... 28

2.3.1.2 Examples of Biological Processes uses as a Passive Systems ............................................................ 28

2.3.2 Active Treatment Systems .................................................................................................................... 30

2.3.2.2 Examples of Biological Systems Used as Active Systems ................................................................. 32

2.4 Sulphide Removal Processes ................................................................................................................... 34

2.4.1 Sulphur Production via the Chemical Pathway .................................................................................... 34

2.4.2 Sulphur Production via the Biological Pathway ................................................................................... 36

2.4.2.1 Photosynthetic Sulphur Bacteria ........................................................................................................ 36

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2.4.2.2 Colourless Sulphur Bacteria ............................................................................................................... 37

2.4.2.3 Heterotrophic Sulphur Bacteria ......................................................................................................... 38

2.5 Factors Affecting Sulphide Oxidation ..................................................................................................... 38

2.6 Optimization of Sulphur Recovery………………………………………………………………39

(a) Biotrickling Filter Reactor…………………………………………….………………………….39

(b) Linear Flow Channel Reactor (LFCR)…………………………………………………………..39

(c) Expanded Granular Sludge Bed (EBSB) Reactor………………………………………..……..40

2.7 Chapter Summary .................................................................................................................................... 40

CHAPTER 3 - MATERIALS AND METHODS .............................................................. 41

3.1 Chemical Reagents .................................................................................................................................. 41

3.1.1 Preparation of Media and Stock Solutions ............................................................................................ 41

3.2 Microbial Isolation and Enrichment ....................................................................................................... 42

3.2.1 Microorganisms source ......................................................................................................................... 42

3.2.2 Microbial Isolation ............................................................................................................................... 43

3.3 Identification of Sulphide Oxidizing Microorganisms ............................................................................ 43

3.3.1 Gram Stain ............................................................................................................................................ 43

3.3.2 16s rRNA Sequencing ........................................................................................................................... 44

3.3.3 Sulphide Removal Test ......................................................................................................................... 44

3.3.4 Total Biomass Analysis ........................................................................................................................ 45

3.3.5 Viable Biomass Analysis ....................................................................................................................... 45

3.4 Batch Experiments .................................................................................................................................. 45

3.4.1 Effects of pH and Redox Potential on Sulphide Oxidation .................................................................. 45

3.4.2 Sulphate Batch Tests ............................................................................................................................ 46

3.5 Mixed Batch Reactor System .................................................................................................................. 47

3.6 Analytical Methods .................................................................................................................................. 47

3.6.1 ICP-OES Analysis ................................................................................................................................. 47

3.6.2 UV/Vis Spectrophotometry .................................................................................................................. 48

3.6.3 Mass Balance Analysis ......................................................................................................................... 48

3.6.4 Microscopic Analysis of Isolated Bacteria ............................................................................................ 49

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3.6.4.1 Scanning Electron Microscopy (SEM) .............................................................................................. 49

3.6.4.2 Transmission Electron Microscopy (TEM) ...................................................................................... 49

CHAPTER 4 - RESULTS .................................................................................................. 51

4.1 Removal of sulphide under heterotrophic conditions ............................................................................. 51

4.2.1 Rate of removal at 50 mg/L Sulphide ................................................................................................... 52

4.2.2 Rate removal at 57 mg/L Sulphide ....................................................................................................... 52

4.3 Investigation of Optimal Conditions ....................................................................................................... 55

4.3.1 Optimum pH Values ............................................................................................................................ 55

4.3.1.1 Effect of pH on

Lysinicibacillus fusiformis

on sulphide removal activity ......................................... 56

4.3.1.2 Effect of pH on Sulphide Removal Activity by Mixed Culture .......................................................... 57

4.3.2 Oxidation Reduction Potential (Eh) .................................................................................................... 58

4.3.2.1 Effect of Eh on sulphide removal activity by Mixed Culture ............................................................. 59

4.3.2.2 Fate of Elemental Sulphur in the Reactor. ......................................................................................... 59

4.4 Biological Treatment of Acid Mine Drainage in Batch Reactor ............................................................. 66

4.4.1 Influence of COD/Sulphate Ratio on pH ............................................................................................ 66

4.4.2 Influence of COD/Sulphate ratio on sulphate removal rate ................................................................. 67

4.4.3 Influence of COD/Sulphate ratio on Biogenic Sulphide production ................................................... 69

4.4.4 Partial Sulphide Oxidation and Elemental Sulphur Recovery .............................................................. 70

4.5 Chapter Summary .................................................................................................................................... 74

CHAPTER 5 - KINETIC MODELLING ........................................................................ 75

5.1 Viable Biomass of

Lysinicibacillus fusiformis

Incubated on sulphide (50-100 mg/L) under optimum conditions ..................................................................................................................................................... 75

5.2 Kinetic Modelling Theory ....................................................................................................................... 76

5.3 Parameter Evaluation .............................................................................................................................. 78

5.3.1 Kinetic Parameter Estimation............................................................................................................... 79

5.3.2 Sensitivity Analysis ............................................................................................................................... 81

5.4 Chapter Summary .................................................................................................................................... 83

CHAPTER 6 - CONCLUSIONS AND RECOMMENDATIONS ................................. 85

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6.1 Conclusion ............................................................................................................................................... 85

6.2 Recommendations................................................................................................................................... 85

REFERENCES…………..……………………………………………………………..90

APPENDIXES…………………………………………………………………………102

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LIST OF FIGURES

Figure Figure Title

Figure 2.1 Sulphur cycle in the environment…….............................................

Figure 2.2 Various mechanisms generating hydrogen sulphide ………….

Figure 2.3 Ionic species of hydrogen sulphide at different pH……...…….

Figure 2.4 Sulphur Species at different pH values and Redox Potential (Eh) values…………………………………………………….

Figure 2.5 Decision flowchart used for selection between active and passive treatment of AMD...…..……………………………….

Figure 2.6 Different passive treatments ………..…………………………

Figure 2.7 Decision tree for selection the passive system to treat

Figure 2.8

Figure 2.9

AMD…..................................................................................................

Decision tree for selection of the active treatment systems...

Pourbaix Diagram representing stable sulphur compounds in

(contact with) aqueous solution at different oxygen pressure

(redox potential, E in volts) and acidity (pH), calculated for the sulphate, iron and sodium ion concentrations in aqueous solution

(calculated for [SO

42-

] tot

=350mM, [Fe

3+

] tot

=50mM and

[Na

+

] tot

=400mM…...…………………………………………

Figure 3.1 Flow diagram of the various steps taken during the experimental phase……………………………….………………………….

Figure 3.2 Schematic diagram of the reactor used for elemental sulphur

Figure 4.4 production during the complete treatment of synthetic acid mine drainage…………………………………………………………..

Sulphide oxidation rate by individual species under lactate and Figure 4.1 glucose as solo added carbon sources…………………….…....

Figure 4.2 Sulphide removal rate in the presence of lactate………………..

Figure 4.3 Sulphide Removal rate by L. fusiformis and L. sphaericus after 4 hours of incubation………………………………...………….

Sulphide removal rate in the presence of glucose……………..

Figure 4.5

Figure 4.6

Sulphide removal rate at the different pH values……………...

Box and Whisker plots showing the mean distribution of the

-x-

51

52

46

53

54

55

40

35

27

31

25

26

Page

18

22

23

23

sulphide removal rate by

L.fusiformis…….…………...…………

Figure 4.7 Sulphide concentration at different pH values……….………....

Figure 4.8 Box and Whisker plots showing the mean distribution of the

Sulphide removal rate by both L. fusiformis and L. sphaericus……

Figure 4.9 Sulphide Concentration over time using mixed culture at (A) pH

6.7 and (B) pH 7..………………………………… ………...…..

Figure 4.10 (A) Scanning electron micrographs and (B) Transmission electron micrographs of elemental sulphur…………………..........................

Figure 4.11 Mass balance analysis of the sulphur containing compound at pH

6.7 and Eh=-130 mV……………………………………………

Figure 4.12 Sulphide Concentration at pH 8 and Eh -80 mV………………..

Figure 4.13 Mass balance analysis of the sulphur containing compound at pH

8 and Eh=-80 mV………………………...……………….

Figure 4.14 Change of pH during anaerobic culture with COD/Sulphate

Ratio…………………………..……………………………….

Figure 4.15 Change of Sulphate Concentrations during the batch culture with

COD/Sulphate ratio……………………………………...

Figure 4.16 Sulphate removal rate at the different COD/Sulphate ratios…..

Figure 4.17 Variation of sulphide concentration over time during batch culture with COD/Sulphate ratio………………………….…...

Figure 4.18 Changes of Sulphide (A), Sulphur and Sulphate (B) concentrations during the incubation period…………….…......................................

Figure 4.19 Variation of pH and Eh throughout the experiment…….…...

Figure 5.1 Biomass concentration at 50 and 100 mg/L……………..…...

Figure 5.2 Sulphur Species oxidation in batch cultures at (A) pH 6.7 and Eh

– 130mV and (B) pH 8 and Eh – 80mV ………………

Figure 5.3 Sensitivity test for the sulphide oxidation and sulphur oxidation processes under optimal conditions (A) sulphide concentration,

(B) sulphur concentration and (C) sulphate concentration…….

56

57

57

72

74

69

70

79

82

66

67

68

64

61

62

58

60

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LIST OF TABLES

Table Table Title

Table 2.1 Health effects from short-term exposure to hydrogen sulphide…..

Table 2.2 Advantages and disadvantages of active treatment………………...

Table 2.3 List of chemicals used in active treatment………………………...

Table 3.1 Experimental Plan for Investigating Effects of pH and Redox

Potential on Sulphide Oxidation …………………………………

Table 3.2 COD/Sulphate ratio……………………………………………...

Table 4.1 List of the sulphide oxidizing microorganisms isolated from the gold mine sludge……………………………………………......

Table 4.2 Reactor performance on sulphur containing compounds in the batch reactor during the experimental phase (EP 3) at pH 6.7……

Table 4.3 Reactor performance on sulphur containing compounds in the batch reactor during the experimental phase (EP 3) at pH 7…….

Table 4.4 Reactor performance on sulphur containing compounds in the batch reactor at pH 8 and Eh=-80mV…………………….……..

Table 4.5 Single batch reactor performance on sulphur containing compounds………………………………………………………

Table 5.1 Optimum Kinetic Parameter in Biological Sulphide Oxidation…..

Page

23

29

31

45

45

50

63

71

78

59

59

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LIST OF ABBREVIATIONS

AMD

BLAST bp

COD

DNA

DO

Eh

EPA

HPLC

Acid mine drainage

Basic local alignment search tool

Base pairs

Chemical oxygen demand

Deoxyribonucleic acid

Dissolved oxygen

Redox potential

Environmental Protection Agency

High performance liquid chromatography

ICP-OES Inductively coupled plasma optical emission spectropscopy

M Molarity

MB

SA

SEM

Methanogenic bacteria

South Africa

Scanning electron microscopy

SOB

SOM

SRB

TEM

Sulphide oxidizing bacteria

Sulphide oxidizing microorganisms

Sulphate reducing bacteria

Transmission electron microscopy

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CHAPTER 1

INTRODUCTION

1.1 Background

The mining industry represents the largest income generating activity for South Africa.

Mining operations in South Africa focus on the extraction principle of precious minerals such as gold, as well as fossil fuels like coal. Coal is one of the sources of energy in South

Africa, dominating other energy sources by up to 77% and serves as the region’s main source of electricity (Department of Energy South Africa). Coal mining also contributes towards the national automobile fuel supply, with more than 40% of fuel coming from the Sasol synthetic fuel process based on coal as its feed stock (Department of Energy South Africa).

Mining activities in South Africa have led to an inevitable occurrence of Acid Mine Drainage

(AMD). AMD negatively impacts on soil and water in regions where mines are situated. It is an environmental ill-effect characterised by low pH, high sulphate concentration (often referred to as high salinity) and high concentrations of heavy metals (McCarthy, 2011;

Pentreath, 1994; Jenkins et al. 2000 ) . These characteristics make AMD a hazardous phenomenon that should be urgently addressed (Ochieng et al., 2010).

There have been various attempts to mitigate the effects of AMD on the environment.

Physical and chemical treatment methods have been explored, but have been considered less advantageous compared to biological methods (Johnson et al., 2005, Sahoo et al., 2013;

Johnson & Hallberg, 2005; Neba, 2006).

Biological treatment of AMD relies on the use of

Sulphate Reducing Bacteria (SRB), which are capable of reducing sulphate to sulphide in the presence of organic matter. The activities of these bacteria lead to the accumulation of bicarbonate as a by-product. The bicarbonate produced increases the pH of affected water, which results in the precipitation of heavy metals from solution (Greben et al., 2005; Cao et

al., 2009; Tang et al., 2009, Singh et al., 2011). Sulphide at medium and low pH leaves the solution as the gaseous hydrogen sulphide (H

2

S) which as a strong smell and is corrosive to metallic surfaces (Celis-Garcia et al., 2007). For these reasons an effort is made to remove sulphide from effluents from sulphate reduction processes as a means to reduce environmental impacts.

14

Studies that focused on the removal of sulphide from AMD include precipitation as metal sulphide (Dvorak et al., 2004), oxidation to elemental sulphur (Janssen et al., 1999; Rein,

2002; Molwantwa, 2008 and Mooruth, 2011), solvent extraction (Janssen et al., 2000 and

Johnson, 2000) and electrochemical oxidation (Waterson et al., 2006). The partial oxidation of sulphide to produce elemental sulphur is of great interest because the produced elemental sulphur could be used as fertilizers, as a substrate for the bioleaching processes and as raw material for production of plastics and fire retardants (van Lier et al., 2001; Celis-Garcia et al.,

2008). Other studies have attempted to optimize the production of elemental sulphur by manipulating biological pathways in sulphide oxidizing bacteria (Reinhoudt & Moulijin, 2000;

Rein, 2002; Molwantwa, 2008; Krishnakumar et al., 2005; Cardoso et al., 2006; Lee et al.,

2006). In spite of the above efforts, a lot is still unknown about the optimal conditions for elemental sulphur production, hence the need to investigate the possibility of enhancing elemental sulphur production.

1.2 Objectives of Study

The production and recovery of elemental sulphur via the use of Floating sulphur Biofilms during AMD treatment has been highlighted to be of great importance previously (Rein,

2002, Neba et al., 2006, Molwantwa, 2008; van Hille & Mooruth, 2011, Rose et al., 2013), using Biotrickling filter reactors mentioned by Fortuny et al., 2009 and 2011) or the expanded granular sludge bed reactor (Chen et al., 2008). However, optimal conditions for the process remain unknown. The primary objective of this study is to investigate the optimal conditions for simultaneous acid mine water treatment and partial sulphide oxidation to produce and recover elemental sulphur. It was imperative, during the course of the study, to monitor key parameters such as temperature, redox potential and pH to determine optimal values that could positively influence the production of elemental sulphur.

1.3 Outline of Dissertation

This dissertation includes:

Chapter 1- Introduction provides a brief background of the AMD

problem and its various treatments are given with a highlight of the importance to produce elemental sulphur during the treatment.

15

Chapter 2- Literature Review gives sufficient background information regarding previous attempts to optimize elemental sulphur production and recovery. The chapter also covers studies that have focused on the occurrence of different sulphur species in water, the impact of sulphur species pollutants present in water, on living organisms and the environment, the different remediation strategies that have been explored as well as identified species of sulphide oxidizing microbes and the biological pathways of the sulphur oxidizing process.

Chapter 3- Materials and Methods explains the research methodology including details on the analytical methods employed for successful completion of this study.

Chapter 4- Culture characterization and performance analysis explains the identification and characterization of microbial species as well as their ability to oxidize sulphide.

Chapter 5- Kinetic Modelling theory describes the use of an appropriate kinetic model performed in order to determine the optimum conditions for sulphur recovery.

Chapter 6- Conclusion and Recommendations represents a summary of the findings and conclusions of the work performed in the study and recommendations are given for further study.

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CHAPTER 2

LITERATURE REVIEW

2.1 Acid Mine Drainage

2.1.1 The Sulphur Cycle

Sulphur is the tenth most common element on earth. It is found in proteins and vitamins and several organic constituents. Plants take up dissolved sulphur, while animals consume plants to obtain sufficient sulphur to maintain their health. In the natural environment, sulphur is mostly stored in sedimentary rocks and salted sea. It can enter the atmosphere through natural processes (volcanic eruptions, bacterial processes and evaporation) or industrial processes (Figure 2.1).

Figure 2.1: Sulphur Cycle in the Environment

17

The natural sulphur cycle maintains the balance of distribution of elemental sulphur in the global environment. Unfortunately, human activities tend to disrupt the natural cycle in the environment, by increasing the concentration of hazardous sulphur species in certain environmental compartments to levels that may be detrimental to indigenous organisms.

Sulphur dioxide (SO

2

) is one of the hazardous sulphur species released into the environment due to combustion of fossil fuels such as coal, and is generated from activities also producing acid mine drainage. SO

2

generated during combustion of coal is easily oxidized in the atmosphere and protonates to form sulphuric acid upon contact with atmospheric water.

Sulphuric acid (H

2

SO

4

) can further react with other compounds to form di-methylsulphide which is eventually emitted to the atmosphere by plankton species. These particles are either deposited on earth or react with the rain to form acid deposition, also known as acid rain.

2.1.2 Formation of Acid Mine Drainage

The presence of sulphide minerals, particularly pyrite (FeS

2

) and their oxidation products drive the occurrence of AMD. Nordstrom & Alpers (1999) reported that pyrite concentration, grain size and its availability affect the generation of AMD. In the presence of oxygen and water, pyrite dissociates into ferrous ions (Fe

2+

) and sulphide (S

2-

), which are readily oxidized to sulphate ions (SO

42-

) (Equation 2.1). The oxidation process includes production of hydrogen ions (H

+

), which contribute to the acidity of the solution.

2FeS

2

+7O

2

+2H

2

O→ 2Fe

2+

+ 4SO

42-

+ 4H

+

(2.1

)

The ferrous ions (Fe

2+

) produced in Equation 2.1 are further oxidized by oxygen to release

Ferric ions (Fe

3+

) (Equation 2.2). The Fe

3+

ions are hydrolysed to produce an insoluble compound ferric hydroxide [Fe(OH)

3

)

(s)

] (Equation 2.3).

4Fe

2+

+O

2

+4H

+

→4Fe

3+

+2H

2

O (2.2)

Fe

3+

+3H

2

O→Fe(OH)

3(s)

+3H

+

(2.3)

The reaction (Equation 2.3) shows a net production of 3 moles of H

+

per mole of pyrite oxidized. Owing to this, the pH of the solution drops to create a highly acidic environment, which is characteristic of AMD.

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FeS

2

+14Fe

3+

+8H

2

O→15Fe

2+

+ 2SO

42-

+16H

+

(2.4)

Several factors have been highlighted as crucial to the occurrence of AMD. These include the presence of iron oxidizing and sulphide oxidizing bacteria, the temperature, oxygen content and sulphate concentration as well as concentration of heavy metals (Valenzuela et al.

2005).

Thus AMD is characterized by:

• Low pH

• High Sulphate concentration (or salinity)

• High metal content

2.1.3 Impacts of Acid Mine Drainage

There are severe adverse effects that occur due to AMD one of which is the pollution of receiving water bodies-both surface and groundwater. The discharge of AMD into natural water bodies increases the acidity of the receiving waters and the resulting acidity negatively impacts animal and plant life in natural ecosystems (Riley

et al., 1972). Jamal (1997) indicated that the effect of AMD is not restricted to the local area at the source but may extend further if the acid water is discharged into a stream or river without adequate treatment. If acid producing mines are located in regions with permeable soil structures, the polluted acid mine water could percolate into the aquifers and spread over a wide area through groundwater movement. It has also common knowledge that acid generation and discharge still occurs long after a mine is closed or abandoned (Lottermorser, 2003).

The pollution of groundwater is not the only hazard that AMD poses. A study by Atkins and

Singh (1982) showed that acidic water was responsible for the corrosion of equipment at mines. AMD accelerates the formation of scales in delivery pipes as well as the pollution of the mine surface environment, affecting surface ecology. AMD can inhibit or kill some aquatic plants and animals thereby causing undesirable ecological shifts. Low pH values, such as those experienced in regions affected by AMD could cause respiratory or osmo–regulatory

19

failure in fish and low order vertebrates (Kimmel, 1983). At low pH levels, hydrogen ions may be absorbed by cells displacing vital sodium ions (Morris et al., 1989), which are important for normal body operation. Ferric hydroxide [Fe(OH)

3(s)

]

,

commonly known as

Ochre (Equation 2.3), is one of the by-products of AMD. It is a low density compound with an orange colour and is often found at the bottom of affected rivers. The presence of

Fe(OH)

3

disrupts the food supply for benthic organisms leading to their death and disruption of the aquatic food chain. It has also been reported that ochre reduces the surface area available for fish to lay their eggs thereby affecting their production cycle (Pentreath,

1994).

AMD is known to be a serious threat to humans and ecological systems due to the presence of heavy metals. Heavy metals cannot be degraded; they mostly accumulate higher in the food chain (Moreno et al.,2001 and Carlson et al., 2002). The low pH of the mine water increases solubility of the heavy metals in water thereby increasing the concentration of the metals and their toxicological effects on aquatic ecosystems. According to the study done by

Lewis and Clark (1996), exposure of higher order organisms to high concentrations of heavy metals results in acute effects such as stunted growth, lower reproduction rates, deformities and increased mortality. For example, in fish, acidity from AMD may cause various physiological disturbances such as reduced growth and reproduction rates (Kimmel, 1983).

Furthermore, the high concentration of heavy metals in affected water has been reported to affect algal growth. Algae are the primary producers in aquatic systems and suppression of their growth rate affects the proliferation of aquatic life (Hoehn and Sizemore, 1977). Direct input of ochre in fish population includes blockage of gills and suffocation of eggs which could drastically reduce the fish population (Hoehn and Sizemore, 1977).

Aluminium is one of the metals found in water bodies whose organisms and speciation in water bodies can be affected by AMD. Significant amounts of aluminium in water combined with a low pH increases the rate of sodium loss from blood and tissue, resulting in death.

Brown and Sadler (1989) showed that the loss of sodium ions from blood resulted in high mortality in fish and other aquatic organisms in water bodies that were polluted by AMD.

Additionally, the precipitation of aluminium in fish gills affects their breathing. Earle and

Callaghan (1998) observed that receiving waters contaminated by AMD had low biodiversity.

20

The effects of iron precipitates are similar to those of aluminium precipitates, i.e., they form a blanket at the stream bottom, adversely affecting both macro-invertebrates and fish.

2.2 Production of Sulphide

Different mechanisms including sulphate reduction are known to produce sulphide (Figure

2.2) and industries such as tanneries as well as paper and pulp manufacturers are also known to generate sulphide (Janssen et al., 1999).

Figure 2.2: Various mechanisms generating hydrogen sulphide ( Modified from Edwards et al, 2011)

Sulphide is mostly present in three different forms, depending on the pH of the liquid medium in which it is found (Figure 2.3). The three forms in which sulphide is found are:

• Hydrogen sulphide (H

2

S) - mostly dominant at pH 5-6

Bisulphide ion (HS

-

) - recurrent at pH 7-9

Sulphide ions (S

2-

) - dominant at a pH>9

21

The sum of the three compounds above is referred to as total sulphide expressed as a concentration with the units mg/L S

2-

. The existence of other forms of sulphide also depends on the oxidation reduction potential (ORP) as shown in Figure 2.4.

Figure 2.3: Ionic species of hydrogen sulphide at different pH (Modified from Thompson et

al., 1995)

22

Figure 2.4: Sulphur species at different pH values and Redox potential (Eh) values

(Modified from Kriek et al., 2013)

2.2.1 Physiological and Environmental Effects of Sulphide

The rotten smell egg of hydrogen sulphide renders contaminated water undesirable for drinking and if ingested stomach discomfort, nausea and vomiting will occur (Health Canada,

1992). However sulphide poisoning mostly occurs via inhalation: Chou (2003) explained that an average individual can detect the presence of hydrogen sulphide at 11μg/m

3

but at higher concentration (140-700 mg/m

3

) it will be fatal. Table 2.1 summarises the various health effects that could result from short-term exposure to sulphide.

23

Table 2.1: Health effects from short-term exposure to hydrogen sulphide (Adapted from

Skrtic, 2006)

Concentration (ppm)

0.01-0.3

1-20

20-50

100-200

250-500

500

>1000

Health effect

Odour threshold

Offensive odour, possible nausea, tearing of the eyes or headaches with prolonged exposure

Nose, throat and lung irritation; digestive upset and loss of appetite; sense of smell starts to become fatigued; acute conjunctivitis may occur (pain, tearing and light sensitivity)

Severe nose, throat and lung irritation; ability to smell odour completely disappears.

Severe lung irritation, excitement, headache, dizziness, staggering, sudden collapse, unconsciousness and death within few hour, loss of memory for the period of exposure resulting in permanent brain damage if not rescued immediately

Respiratory paralysis, irregular heartbeat, collapse and death without rescue

Rapid collapse and death

Sulphide is also associated with corrosion problems to drill strings in mining operations, transport pipes or drainage pipes and effluent storage pipes. These problems increase the operation and maintenance cost of plants handling H

2

S contaminated water. According to a report by Lyn (1992), hydrogen sulphide removal could be achieve using an oxidative method but this method leads to increased turbidity and colour in the effluent water.

2.3 Treatment of Acid Mine Drainage

The systems employed in the treatment of acid mine drainage can be classified as either active or passive systems, depending on whether the system requires energy or labour inputs during its operation. A range of configurations incorporating both physical/chemical and biological processes have been successfully employed in both passive and active processes.

Various factors influence decision making during the selection of a treatment system. Figure

2.6 shows the decision tree for selection between active and passive systems. Many active mining companies prefer active systems since the cost of treatment process is covered by the

24

profits of the company. On the other hand, budget limitations towards remediation operations at closed mines, favours the implementation of passive systems.

Figure 2.5: Decision flow chart for selection between active and passive treatment of AMD

(Modified from Waters et al., 2003)

2.3.1 Passive Treatment Systems

Passive treatment refers to a treatment process where human intervention is minimal or absent. Pulles et al (2004) defined passive system as a water treatment system that uses natural available energy sources such as topographical gradient, microbial metabolic energy, photosynthesis and chemical energy. Some of the processes require a regular but infrequent maintenance to operate successfully throughout their lifespan. Examples of passive systems that have been used in treatment of AMD are shown below in Figure 2.7. Natural systems such as aerobic and anaerobic wetlands are utilized for removing organic matter where as

25

physical chemical processes such as Alkalinity Producing Systems (APS), Anoxic Limestone

Drains (ALD) and Limestone ponds are suitable for the precipitation of metals in AMD.

Figure 2.6: Different Passive Treatments (Skousen et al., 1998)

A specific example of a decision to choose between the different passive treatments towards specific water quality goals is shown in Figure 2.8 based on the process proposed by Herdin et al. (1994). This decision diagram was later modified by the Piramid consortium (2003) and later by Gusek (2008) who updated it in in order to include a wider range of chemical elements since previous versions were only focused on iron and magnesium.

26

Figure 2.7 Decision Tree for selection the passive system to treat AMD (Adapted from

Herding et al., 1999).

27

2.3.1.1 Examples of physic-chemical processes used as passive systems

(a) Anoxic Limestone Drains (ALD)

In the anoxic limestone drain (ALD) system, acid mine drainage is allowed to flow over crushed limestone cells. The cells are sealed at the point of discharge of AMD to minimize oxygen ingress and to ensure that carbon dioxide (CO

2

) accumulates in the system. In an earlier study by Hedin and Watzlaf (1994), ALDs were capped with clay to prevent contact with oxygen. Perforated plastic sheets are sometimes placed between the cells to prevent clay and dirt from entering the cells. The limestone dissolves in the acid water, raising the pH to around 7 (Equation 2.5). During this process, iron is maintained in its reduced form, thus inhibiting the formation of ferric hydroxide, which could reduce the effectiveness of the

ALD system (Johnson and Hallberg, 2005).

CaCO

3

+H

+

→Ca

2+

+HCO

3-

(2.5)

Although, an increase in pH is achieved at low cost in ALDs, the system is susceptible to the coating by ferric and aluminium precipitates. Another shortcoming is the formation of ferrous carbonate and manganous carbonate gels within the limestone which causes fast dissolution of the limestone pebbles (Evangelou, 1998).

(b) Open Limestone Drains (OLD)

Open limestone drains (OLDs) are designed to increase pH through dissolution of exposed limestone surface in specially constructed limestone drains. The design and operation of the system demand special attention during the armouring and coating of the limestone drains.

Ziemkiewicz et al. (1997) utilised this system and observed that the OLDs were efficient in removing iron and increasing pH. In spite of the feasible performance observed, the increase in pH was eventually hampered by a ferric iron coating that developed during operation in the long-term. Construction with steeper gradients for high flow velocities could alleviate this problem. The OLDs can also be periodically flushed to remove any accumulated precipitates.

2.3.1.2 Examples of Biological Processes uses as a Passive Systems

Wetlands are usually the favourable option for use as passive treatment of AMD mainly because they are relatively self-sustainable once they have been established, and they can be

28

constructed and operated at a low cost. Designed constructed wetlands are used in the treatment of AMD although natural wetlands have also been integrated in AMD treatment schemes (Johnson and Hallberg, 2002).

a) Aerobic Wetlands

Aerobic wetlands are primarily used when iron is the main contaminant. They are usually shallow, and include plants that are used to immobilise heavy metals. The following operational conditions are used to maintain aerated conditions on the wetland:

(1) Relatively shallow water depths to allow aeration of the mine drainage

(2) Cascades to further enhance aeration

(3) Sufficient residence time to allow the treatment reactions to take place

(4) Space for the settling and accumulation of the metal precipitates and solids

(5) Promote algal growth to further increase the pH and facilitate manganese oxidation and precipitation

Items 3 and 4 require large land areas and pose a challenge for mines in operation (Skousen et al., 1998). A drawback of aerobic wetlands is the accumulation of precipitates over time that will limit their abilities to treat the acid mine waters (Costello, 2003).

b) Anaerobic Wetlands

Anaerobic wetland systems depend on abiotic and biotic reactions to precipitate metals and neutralize acidity. The ability of these wetlands to generate alkalinity passively makes them suitable for treatment of discharge from abandoned mines. The reduction reactions that occur within the wetlands are driven by electron donors that originate from organic matter present in the environment. There are two types of anaerobic wetlands namely: (1) a basic wetland where dissolved oxygen which is of 2-5 mg/L in the acid water (see Figure 2.6) is replenished thereby promoting the reduction of iron and sulphate, and (2) a variant anaerobic wetland, which is the combination of a basic anaerobic wetland and the addition of limestone gravel to produce more alkalinity. The bicarbonate produced from the addition of limestone helps to neutralize the acidity of AMD by increasing the pH and subsequently, the precipitation of metals. A drawback for the system is the slow mixing of the alkaline

29

substrate water with AMD near the surface. This can be overcome by constructing very large wetlands.

2.3.2 Active Treatment Systems

Active systems refer to systems that require constant human intervention, operation or maintenance and monitoring, and require a constant supply of energy mostly in the form of heat or electricity. Table 2.2 list the shortcomings and advantages of active treatment systems.

Table 2.2: Advantages and Disadvantages of Active Treatment (Adapted from Motsi, 2010)

Advantages

Effective and fast removal of acid and metals

Frequent process monitoring

Precise process control

Can be accommodated at small sites

Disadvantages

High initial capital costs

High chemical costs

High operational costs

Disposal of the sludge

2.3.2.1 Examples of physic-chemical systems used as Active Systems

Active abiotic systems involve the addition of a chemical agent that will raise the pH resulting at the acceleration of the chemical reactions rates so promoting metal precipitation through the formation of hydroxides or carbonates. Coagulants can be added to facilitate settling and mechanisms for the removal of resultant sludge are included. An example of a decision to choose among the various active treatments towards specific water quality goals is shown in Figure 2.8.

30

Figure 2.8: Decision Tree for selection of the active treatment systems (Modified from

Rajaram et al., 2001).

31

Table 2.3: List of Chemicals used in Active treatment (Adapted from Motsi, 2010)

Name

Oxidants

Calcium Hypochlorite (Ca(ClO)

2

Sodium Hypochlorite (NaClO)

Calcium Peroxide (CaO

2

)

Hydrogen Peroxide (H

2

O

2

)

Potassium Permanganate (KMnO

4

)

Acid Neutralisation

Limestone (CaCO

3

)

Hydrated Lime (Ca(OH)

2

)

Pebble Quick Lime (CaO)

Soda Ash Briquette (Na

2

Caustic Soda (NaOH)

Ammonia (NH

Fly Ash (CaCO

Coagulants/Flocculants

Alum (Al

2

(SO

4

3

3

)

3

)

, Ca(OH)

Copperas (FeSO

4

)

Ferric Sulphate ( Fe

2

(SO

4

CO

3

)

2

3

Sodium Aluminate (NaAlO

)

2

)

)

Comments

Strong oxidant

Strong oxidant

Acid neutraliser

Strong oxidant

Very effective and commonly used

Used in anoxic limestone drains and open limestone channels

Cost effective reagent, requires mixing

Very reactive, needs metering equipment

System for remote locations but expensive

Very soluble, can be either in solid or liquid form. It is cheaper in liquid form

Very reactive and soluble

Neutralisation value varies with each products

Acidic material, forms Al(OH)

3

Acidic material, usually slower reacting than alum

Ferric products react faster than ferrous

Alkaline coagulant

2.3.2.2 Examples of Biological Systems Used as Active Systems

Active biological systems require continuous direct intervention in their operations. Due to their high performance, they are usually chosen over passive systems in mines operations, which are still open. Active biological systems rely on the activity of sulphate reducing bacteria (SRB) that reduce sulphate to sulphide and ferrous iron oxidizing bacteria that facilitate the removal of iron as iron hydroxide precipitate. Examples of active biological systems that have been implemented successfully include activated sludge and floating sulphur biofilms processes.

32

(a) Activated Sludge with Sulphate Reduction

Ingvorsen et al., (2003) demonstrated that sulphate reducing bacteria (SRB) can remove sulphate in an activated sludge plant under anaerobic conditions. Bade et al., (2000) and

Cypionka (2000) however reported that some SRB strains can tolerate oxygen while other strains could use oxygen as an electron acceptor for the production of adenosine triphosphate (ATP).

(b) The BioSURE Process

The Rhodes BioSURE process was developed in Grahamstown, South Africa in the early

1990s in collaboration with the Council for Scientific and Industrial Research (CSIR) (Rose,

1992, Rose et al., 1996 and Dunn, 1998). The process utilizes chemical oxygen demand

(COD) in sewage sludge as an electron donor or carbon source for the sulphate reduction by microbes. The sustainability of the BioSURE process is dependent on a variety of factors, some of which were listed by Neba (2006). Some of these factors are explained below:

(1)

Environmental sustainability- the process includes the removal of sulphate present in AMD and the disposal of primary sewage sludge. This will benefit a water scarce country such as South Africa.

(2) Technical sustainability- Low cost of technical setup.

(3) Financial sustainability- Low cost of the process due to readily available and affordable carbon sources.

(4) Social Sustainability- Kumalo (2005) and Rose et al (2009) emphasized the fact that the treatment of AMD could create employment, hence alleviate poverty.

In spite its advantages, only pilot studies have been conducted using BIOSURE process.

(c) Floating Sulphur Biofilms

The use of floating sulphur biofilms to treat acid mine drainage has been a subject of interest in the development of a bacterial system. The appearance of white films (biofilms) on sulphate reducing systems had been observed in the past (Jørgensen & Revsbech, 1985;

Janssen et al., 1997; Rose et al., 1996; Dunn, 1998). However their application has only been

33

experimental although these systems have been observed to occur naturally in sulphate reducing environments. Gilfillan (2000) reported that biofilms most likely consisted of differentiated structures, made up of microbes of different morphologies. Bacteria in biofilms were identified as sulphide oxidizing organisms. Bowker (2000) confirmed the presence of sulphide oxidizing bacteria in biofilms and suggested their possible arrangement within biofilm structures.

Rein (2002) investigated the use of the sulphur biofilms as a system to treat sulphur polluted waters. Molwantwa (2008) reported a detailed explanation on the structural/functional relationship that occurs within biofilms and how this could facilitate the production of elemental sulphur. Van Hille & Mooruth (2011) provided insight into the kinetics and mass balance that occur during the process.

2.4 Sulphide Removal Processes

Sulphide is a toxic, corrosive and odorous compound that should be removed after treatment of acid mine drainage. Various strategies have been used to treat sulphide-rich water. The oxidation of sulphide to elemental sulphur has aroused great interest, given the economic benefits of elemental sulphur in the production of fertilizer and bioleaching processes (Janssen et al., 1999, Rein et al., 2002, Jonhson, 2000, Dvorak et al., 2004; Waterson

et al., 2006; Celis-Garcia et al., 2007).

2.4.1 Sulphur Production via the Chemical Pathway

The two significant sulphide oxidation reactions are shown in Equations 2.6 and 2.7 based on studies by Janssen et al., (1999). The reactions show that sulphide can either be oxidized partially to sulphur or completely back to sulphate under different conditions so it was observed that the oxidation of sulphide is crucial in order to avoid oxidation back to sulphate.

2HS

-

+O

2

→2S o

+2OH

-

(2.6)

2HS

-

+4O

2

→2SO

42-

+2H

+

(2.7)

34

In order to achieve partial oxidation of sulphide to elemental sulphur, the stoichiometric ratio of sulphide to oxygen should be kept at 2:1. Other possible products of sulphide oxidation include thiosulphate (S

2

O

3-

), polythionates (-SO

3

—Sn-SO

3-

) and polysulphides

(Sn

2-

, n= 2 to 5). Polysulphides have been identified as important intermediates that occur during sulphide oxidation (Yao and Millero, 1996; Steudel, 1996; Janssen et al., 1999; Stuedel,

2000) . The oxidative reaction in Equation 2.6 implies that sulphide ions are in contact with oxygen under a narrow pH and Eh range (Also refer to Figure 2.9) conditions to produce elemental sulphur and hydroxide ions. The elemental sulphur produced consists of cyclic S

8 molecules that combine to form larger crystals that can be separated from the solution either by flotation or other physical separation techniques (Steudel, 1996; Janssen et al., 1999;

Steudel, 2000).

Figure 2.9: Pourbaix Diagram representing stable sulphur compounds in (contact with) aqueous solution at different oxygen pressure (redox potential, E in volts) and acidity (pH), calculated for the sulphate, iron and sodium ion concentrations in aqueous solution

(calculated for [SO

42-

] tot

= 350 mM, [Fe

3+

] tot

= 50 mM and [Na

+

] tot

= 400 mM.

35

2.4.2 Sulphur Production via the Biological Pathway

Biological sulphide oxidation is carried out by sulphide oxidizing bacteria (SOB) which use sulphide as an electron donor and produce sulphur particles in the submicron range (Bruser

et al., 2000). The sulphur particles are made of a core of elemental sulphur which is covered by a layer of naturally charged polymers, making the particles hydrophilic (Steudel, 1996;

Bruser et al., 2000; Janssen et al., 2000). The known groups of microorganisms involved in the sulphide oxidation include photosynthetic sulphur bacteria, colourless sulphur bacteria and certain heterotrophic bacterial groups.

2.4.2.1 Photosynthetic Sulphur Bacteria

Photosynthetic sulphur bacteria include green and purple sulphur bacteria. These bacteria use sulphide as electron donor, carbon dioxide (CO

2

) as carbon source and electron acceptor in the presence of light to produce elemental sulphur (Equation 2.8).

CO

2

+H

2

S→CH

2

O+H

2

O + 2S

0

(2.8)

Larsen (1952) explained that under limiting light and CO

2

conditions, sulphur is the major product of sulphide oxidation, whereas sulphate is generated in the presence of abundant light and CO

2.

(Equation 2.9):

2CO

2

+H

2

S+2H

2

O→2(CH

2

O)+H

2

SO

4

(2.9)

The sulphur produced by these organisms is either intra-cellular (e.g.

Chromatium sp.) or extracellular (e.g. Chlorobium sp.) in transitional states or final products, respectively, (Prange and

Dahl, 2006). Sulphur K-edge X-ray Absorption Near-Structure Spectroscopy (XANES) is a technique used for sulphur speciation analysis. XANES has been widely used in geochemical

(Rowe et al., 2007), soil (Zhao et al., 2006 and Prietzel et al., 2007) and biological samples

(Zhao et al., 2007; He et al., 2009). Pickering et al. (2001) reported that extracellular sulphur globules were in the form of S

8

, while Prange et al. (2002) reported intracellular sulphur globules produced by Beggiatoa alba and Thiomargarita namibiensis were cyclic, consisting of 8 sulphur atoms and sulphur chains respectively.

36

Chlorobium limicola is a green sulphur bacterium that has been used for sulphide oxidation in bioreactors, with 90% of sulphide had been converted to sulphur (Johnson, 2000). However, the light requirement complicates the design of the reactor, resulting in high operating costs.

Furthermore, the oxidation of sulphide is strictly coupled to bacterial growth, making photosynthetic sulphur bacteria expensive to use for sulphide oxidation (Kim et al., 1990).

2.4.2.2 Colourless Sulphur Bacteria

Colourless sulphide oxidizing bacteria can be found in both archaea and eubacteria domains

(Johnson, 2000). During the oxidation processes, oxygen (O

2

), nitrate (NO

3

), manganese (IV) or iron (III) can be used as terminal electron acceptors. Colourless sulphide oxidizing bacteria exist in diverse species such as

Acidithiobacillus sp., Thiomicrospira sp., Thiospaera sp.,

Sulfolobus sp., Leptospirillum sp., Acidianus sp., Thermothrix sp., Thiovulum sp., Beggiatoa sp., Thiothrix sp., Thioploca sp., Thiodendron sp., Thiobacterium sp., Macromonas sp., Achromatium sp. and Thiospira

sp. The members of these genera differ in their pH and temperature requirements for growth. In addition, some of the above organisms are capable of denitrification while others are not (Nielsen

et al., 2000; Ito et al., 2004). Thiobacillus species are the most common studied genera of colourless SOB. They are gram-negative, rod-shaped bacteria that obtain their energy from the oxidation of inorganic sulphur compounds (Lens et al., 2000; Widdel,

1988). Kelly (1985) explained that the wide variety in the genus complicates the identification of the enzymatic pathway which is involved in the sulphur metabolism.

Colourless Sulphur bacteria are either aerobic or anaerobic. Anaerobic bacteria use hydrogen or ferrous iron as electron acceptors. Sublette and Sylvester (1987) observed that

Thiobacillus

denitrificans, characterised as an anaerobe, was able to oxidize sulphide to very low levels under aerobic conditions. Under anaerobic conditions, nitrate is used as the terminal electron acceptor and it is converted to nitrogen (Equation 2.10).

5H

2

S+8KNO

3

→4K

2

SO

4

+H

2

SO

4

+ 4N

2

+ 4H

2

O (2.10)

Sublette and Sylvester (1987) also observed that Thiobacillus denitrificans was responsible for oxidizing sulphide from the gas phase due to its tolerance to high pressure as well as tolerance to other sulphide derivatives such as CS

2

, COS and CH

3

SCH

3

. These compounds are easily partition to the gas phase as are this difficult to treat since in most situations they

37

will not be available to the bacteria in water. The presence of molecular oxygen increases the tolerance where as its absence reduces tolerance drastically. Cadenhead & Sublette (1990) observed tolerance to sulphur compounds under loadings as high as 15.1 - 20.9 mmol/g/h in the presence of oxygen. The same culture was susceptible to sulphur compounds under loading rates as low as 5.4-7.6 mmol/g/h in the absence of oxygen.

Other species that possess sulphide oxidizing include Beggiatoa sp. which has been reported to form a symbiotic relationship with sulphate reducing bacteria under micro-aerophilic conditions, to convert biogenic sulphide to intracellular sulphur or sulphate (Basu et al.,

1995).

2.4.2.3 Heterotrophic Sulphur Bacteria

There have not been a lot of studies carried out using heterotrophic bacteria for sulphur oxidation. However, few studies have reported the use of Pseudomonas putida to achieve sulphide oxidation (Chung et al., 1996a; Chung et al., 1996b and Huang et al., 1997). The latter indicated that sulphide oxidation in the 5-60 mg/L range can be oxidized in the ratio

15:18:50 (sulphate, sulphite and elemental sulphur, respectively as the major products), with approximately 12% as the residual.

2.5 Factors Affecting Sulphide Oxidation

Buisman et al. (1990) reported that organic matter such acetate or glucose had little or no impact on biological sulphide removal, while Brigmon et al. (1997) reported that the presence of organic matter stimulated the growth of filamentous sulphide oxidizing bacteria such as

Thiothrix species. These bacteria are considered undesirable for sulphide oxidation because the generated sulphur is intracellular and the presence of these bacteria may cause serious bulking problems. A previous study done by Rein (2002) demonstrated the importance to develop a sulphur recovery process under heterotrophic conditions. He also reported that the presence of organic matter and heterotrophic bacteria could favour the production of elemental sulphur as the main product. van Hille and Mooruth (2011) then emphasized the crucial role of the carbon source in the sulphide oxidation process. Cultures provided with a simple organic matter as acetate show improved conversion of sulphide to sulphur.

38

The effect of redox potential on sulphide oxidation has not been widely studied. Janssen

et

al., (1998) reported a relationship between sulphur production and redox value. The optimal

Redox value for sulphur production in a continuous flow gas lift reactor was reported to be between -147 and -137 mV.

2.6 Optimization of Sulphur Recovery

(a) Biotrickling Filter Reactor

Biological oxidation of sulphide to elemental sulphur was observed to be technical feasible when using biotrickling fliter reactors have been observed to remove high concentration of sulphide from gases (Fortuny et al, 2008 and 2011). The trickling liquid velocity (TLV) is important for the attachment of biomass to the packing material for proper gas-liquid mass transfer and for elemental sulphur flushing in case of accumulation. Fortuny et al., (2011) observed that an increased TLV greatly affects the flushing out of accumulated elemental sulphur thus reducing sulphur recovery. Therefore it is still needed to optimize TLV to avoid the accumulation of elemental sulphur on the packing material.

(b) Linear Flow Channel Reactor (LFCR)

The functionality of the LFCR is based on the use of floating sulphur biofilms at the air/water interface. Molwantwa (2008) provided a descriptive model of the different processes that do occur within the biofilm. The reactor consists of 8 parallel channels that operate individually. Each channel is further divided into 8 compartments by a series of under or over baffles which retain the biofilm within each compartment so allowing the harvest of individual compartments, biofilm growth and sulphur production. The reactor operates in such a way that the final compartment is free of biofilm therefore ensuring a complete sulphide oxidation to elemental sulphur. Van Hille & Mooruth (2011) provided an insight into the kinetics and mass balance that occur during the process.

39

(c) Expanded granular sludge bed (EGSB)

This type of reactor allows the biological treatment of wastewater which passes through at high upflow velocity without the biomass being washed thus accomplishing a high removal rate of organic matter. Chen et al., (2008) were the first to use the reactor to remove simultaneously sulphide, nitrate and organic matter. However Chen et al., (2009) provided a mass balance calculation to justify their findings (Chen et al., 2008) which were higher than anterior studies (Reyes-Avila et al., 2004).

2.7 Chapter Summary

Literature survey shows that chemical and biological methods have been investigated for the treatment of AMD. Biological methods are of great interest compared to chemical treatment procedures. The biological recovery of sulphur has been investigated further by several authors and this has led to development of Linear Flow channel Reactor. In spite of several studies conducted, the optimum conditions for production of elemental sulphur from biological sulphide oxidation remain unknown.

40

CHAPTER 3

MATERIALS AND METHODS

Collection of the mine sludge

Isolation of SOB

Selection of the Most efficient Microoganism from the isolated SOBs

Investigation of the

Optimal

Condition for elemental sulphur recovery:

(a) pH

(b) Redox Potential

Figure 3.1: Flow Diagram of the various steps taken during the experimental phase

3.1 Chemical Reagents

The chemicals and reagents that were used for successful completion of this study were of analytical grade from Merck, Johannesburg, South Africa.

3.1.1 Preparation of Media and Stock Solutions

All media and stock solutions used throughout the study were autoclaved at 121°C for

15minutes for sterilisation using Hirayama HV 50 autoclave. All chemical reagents used, were accurately weighed using Precisa 4000C balance (Vactech, Johannesburg, South Africa.

Sulphide and sulphate stock solutions used in the study were prepared separately as the following: 1.48g of Sodium Sulphate (Na

2

SO

4

) and 7.50g of Sodium Sulphide (Na

2

S. 9H

2

O) were weighted were transferred into 500ml of distilled water then the water was stirred till dissolution of the powders and finally more water was added to make up 1000mL. Both

41

Solutions were then stored in the freezer at 4°C till usage. 500 mL of sodium lactate was prepared by combining 1M lactic acid and 1M of sodium hydroxide. Both nutrient broth and agar were prepared by dissolving 16g and 23g of nutrient broth and agar respectively into

1000mL of distilled water then the solutions were sterilized for 15minutes at 121°C. After the sterilization process, they were cooled to 50-55°C and nutrient agar was poured into purified petri dishes till solidification. Nutrient glucose and lactate broths were similarly made as the nutrient broth but 5g of glucose and 5g of lactate were added to the broth prior to the disinfection step.

A modified selective medium of Nagarajan et Sudhakar, (2012) for sulphide oxidizing bacteria (labelled (SM ox

) was composed of four solutions. Solution A was prepared by dissolving Na

2

S

2

O

3

.5H2O (5.0 g), KNO

3

(2.0 g) and NH

4

Cl (1.0 g) in 250 mL distilled water.

Solution B was prepared by dissolving KH

2

PO

4

(2.0 g) in 250 mL distilled water. Solution C was prepared by dissolving NaHCO

3

(2.0 g) in 250 mL distilled water. 100 mL solution D was prepared by dissolving MgSO

4

.7H

2

O (0.8 g) and FeSO

4

.7H2O (2%w/v) in 100 mL HCl.

The solutions A, B, C and D were autoclaved separately at 121°C for 15 minutes and eventually mixed together. The pH was adjusted to 7 using 1N NaOH.

The selective medium for sulphate reducing bacteria (labelled SM red

) was prepared by dissolving KCl (0.3 g), MgSO

4

•7H

2

O (3.0 g), MgCl

2

• 6H

2

O (2.5 g), NH

4

Cl (0.5 g), NaCl (1.0 g), KH

2

PO

4

(0.6 g), Sodium lactate (20 L), yeast extract (2.0 g), peptone (2.0), L-cysteine (0.5 g), Ascorbic acid (0.5 g) and FeSO

4

•(NH

4

)2SO

4

•6H

2

O (2.0 g) in 1000 mL distilled water.

Ascorbic acid, L-cysteine and FeSO

4

•(NH

4

)2SO

4

•6H

2

O which were separately sterilized with a bacteria filter membrane (0.22 μm of aperture) were added to the medium. The pH was then adjusted to 6.0~6.5, according to the medium preparation procedure by Jiang

et al.,

(2009).

3.2 Microbial Isolation and Enrichment

3.2.1 Microorganisms source

The cells were isolated from dry sludge collected from the final sludge treatment process of a local gold mine in Johannesburg, South Africa. The sludge was poured into sterilized bottles and stored in the freezer at 4°C.

42

3.2.2 Microbial Isolation

1 g of sludge was inoculated into 10 mL volumetric flask of SM ox

prepared. A serial dilution of the inoculated medium was carried out to promote the formation of clear single colonies.

0.1 mL from each diluted sample was then spread onto the plates using the spread technique.

Three different types of media were used for the isolation process: Nutrient agar to determine the presence of chemolithotrophic bacteria, nutrient agar plates with glucose and nutrient agar with sodium lactate to check for the presence of chemoorganotrophic bacteria.

The different agar plates were incubated at ±30°C in a dark room to avoid the growth of photosynthetic bacteria.

In order to isolate anaerobic, non-photosynthetic sulphate reducing bacteria, 1 g of dried sludge was inoculated into SM red

. Oxygen was removed by purging the medium with 99%

Nitrogen (N

2

) gas for 5-10 min. The purged flasks were then sealed with parafilm. The flasks were kept in a dark room at ±30°C for 3 days to avoid the growth of photosynthetic sulphate reducing bacteria. The medium was observed for colour change. Black coloration of the medium indicates the presence of sulphate reducing bacteria and formation of metallic sulphide precipitates.

3.3 Identification of Sulphide Oxidizing Microorganisms

To identify sulphide oxidizing species, bacteria isolated from dry sludge were grown in the presence of sulphide. The bacteria were characterized by physical morphology first using gram staining technology followed by genotypic identification using the 16S rRNA gene finger printing. Isolated species were evaluated for their sulphide oxidizing capability by observing sulphide removal from solution.

3.3.1 Gram Stain

The gram stain procedure used is the Hucker Method (APHA, 2005). 1 mL of the inoculum of each isolated bacteria grown separately till exponential phase was spread on a microscope slide and heat-fixed. The heat-fixed glass side was immersed in crystal violet and air-dried for one minute. The glass side was gently and directly washed under tap water for a few seconds.

Iodine mordant was added and left for one minute. The fixed cells on the glass side were

43

rinsed again with water for 10 seconds. A safranin solution was added and left for 30 seconds, before rinsing with water again for another 10 seconds. The slides, each containing a different bacterium, were dried with absorbent paper before observation using a ZEISS

AXIOSCOP II Microscope equipped with a 100*/1.30 oil plan- NEOFLUAR objective

(Carl Zeiss, Oberkochen, Germany). The cells were differentiated by their colour: blackviolet and red-pink for gram-positive and negatives cells respectively.

3.3.2 16s rRNA Sequencing

The phylogenetic characterization of isolate species, using conserved regions of their 16s rRNA was carried out. In preparation for the 16S rRNA sequence identification, the isolated colonies were streaked onto nutrient agar followed by incubation at 30°C for 24hours. 6 colonies were obtained. DNA was extracted from the pure cultures derived from the colonies using the DNeasy Tissue kit (QIAGEN Ltd, West Sussex, UK). The 16S rRNA genes were amplified by reverse transcriptase-polymerase chain reaction (RT-PCR) using primers pA (corresponding to positions 8-27) and pH1 (corresponding to positions 1541-

1522 of the 16S gener) (Coenye

et al., 1999). A primer pD corresponding to positions 519-

536, was used for internal sequencing. The resulting sequences were deposited in the

Genbank of known microorganisms using the basic Blast Tool Search of the National Centre for Biotechnology Information (NCBI, Bethesda, MD). The nucleotide sequences of the 16S rRNA genes were compared with reference sequences from the GenBank database. The 16S rRNA gene sequences of the purified strains were aligned with reference sequences corresponding to sulphate reducing and sulphide oxidizing. Sequence alignment was verified manually using the program BIOEDIT. Pairwise evolutionary distances based on an unambiguous stretch of 1274 bp were computed by using the Jukes and Cantor method

(Jukes and Cantor, 1969). Phylogenetic tree diagrams were then constructed using the neighbour-joining method. Confidence in the tree topology was determined analysis based on 100 re-sampling.

3.3.3 Sulphide Removal Test

Each isolated cultures at their exponential phase were used in order to conduct the sulphide oxidation tests in 250 mL Erlenmeyer flasks in which 100 mL of sterilized nutrient broth was

44

amended with sulphide to give a final concentration of 20-60 mg/L. 2 sets of tests were conducted: the first set being the control and the second test a heterotrophic condition. In this study lactate and glucose were tested separately to determine which of these carbon source facilitated higher sulphide oxidation.

3.3.4 Total Biomass Analysis

5 mL of bacteria culture was taken from each flask every 6 hours and centrifuged for 10 minutes at 6000 rpm. The pellet obtained was used for total biomass analysis. The pellet was re-suspended in 1mL distilled water and filtered through a pre-weighed Whatman filter paper. The filter paper was dried in the oven at 75-80°C to obtain a dry weight value. The difference between the dried filter paper with cells and the empty filter paper was considered to be the total dry cell biomass.

3.3.5 Viable Biomass Analysis

1 mL of bacteria culture was taken from each flask every 6 hours. Samples were serially diluted into 9 mL sterile 0.85% NaCl solution. 0.1 mL of the diluted samples was transferred into plate count agar using the spread plate technique. The plates were incubated for 24 hours at ±30°C. The colonies were counted after incubation and multiplied by the dilution factor. The bacterial count was reported as colony forming units (CFU) per mL of sample.

3.4 Batch Experiments

3.4.1 Effects of pH and Redox Potential on Sulphide Oxidation

These experiments were designed to identify the effect of pH and redox potential on sulphide oxidation. This phase of the study was further split into 3 experimental phases (EP).

During EP1, isolated bacteria were left to grow and adapt themselves to the pH and redox conditions at ±30°C and served as the control experiments. During EP 2, the effect of pH on bacteria growth was monitored while EP3 involved the investigation of redox potential.

45

The Redox potential and pH values were measured using a WTW pH/mV 330 meter

(Merck, Johannesburg, South Africa). Each test was conducted as duplicate sets.

Table 3.1: Experimental Plan for Investigating Effects of pH and Redox Potential on

Sulphide Oxidation

EP

EP 1

EP 2

EP 3

Time

7 days

4 hours

4 hours

pH value

7

6-8

6-8

Eh Temperature

Not controlled 30°C

Not Controlled 30°C

-130 mV -80mV 30°C

3.4.2 Sulphate Batch Tests

The aim of the test was to investigate the influence of different feed of Chemical Oxygen

Demand (COD)/sulphate ratio on the sulphate reduction rate. The test was conducted in

500 mL Erlenmeyer flasks numbered from R1 to R5. 50 mL sulphate reducing bacteria was inoculated in a 200 ml mixture in 1:1 ratio (v/v), which consists of SM red

and synthetic acid mine drainage, prepared by adding Mg SO

4

.7H

2

O (10 g), Fe

2

(SO

4

)

3

.

XH

2

O (20 g), ZnSO

4

(5 g) and CuSO

4

(0.5g) in 1000 mL tap water. The total sulphate concentration was determined to be 650 mg/L and was maintained constant throughout the whole experiment but the sodium lactate concentration was different in the medium 435.5; 1105; 1462.5; 1950 and

3250 mg/L using feed ratios COD/SO

42-

of 0.67; 1.7; 2.25; 3 and 5 respectively (see Table

3.2). Oxygen was removed from the medium by purging the batches with 99% Nitrogen (N

2

) gas for 10 minutes before the flasks were sealed with foil and parafilm. The samples were incubated at ±30°C. The sulphate reduction phase was monitored by taking samples every 12 hours by means of plastic syringes. The samples were then centrifuged in 2 mL eppendorf tubes at 6000 rpm for 10 min using a Minispin® Micro-centrifuge (Eppendorf, Hamburg,

Germany). The tests were conducted as duplicate sets.

Table 3.2: COD/Sulphate Ratio

Sample Nr

[SO

42-

]

(mg/L)

COD/SO

42ratio

R1

0.67

R2

1.7

R3

650mg/L

2.25

R4

3

R5

5

46

3.5 Mixed Batch Reactor System

A batch reactor with a total volume of 2L consisted of a glass wall vessel equipped with gas samples, pH and ORP probes and gas purging lines (Figure 3.1). At the beginning of the sulphate reducing phase, nitrogen gas was added to decrease the redox potential in order to promote sulphate reducing conditions. During the second phase of the experiment, a suitable bacteria culture was added to facilitate the oxidation of sulphide.

Figure 3.2: Schematic diagram of the reactor used for elemental sulphur production during the complete treatment of synthetic acid mine drainage.

3.6 Analytical Methods

3.6.1 ICP-OES Analysis

The concentration of elemental sulphur (S

0

) in solution was determined using Inductively

Coupled Plasma Optical Emission Spectroscopy (ICP-OES) (Varian Vista Pro; CCD

Simultaneous, Australia). Samples obtained were diluted and filtered to an ICP tube using a disposable syringe (10 ml NORM-JECT, Latex free) through a syringe filter (0.2 μm Supor membrane from Pall Corporation, U.S.A.). All analyses were carried out in triplicate. 90% of the samples were under detection limit of sulphur.

47

3.6.2 UV/Vis Spectrophotometry

UV/Vis spectrophotometry was used to determine the concentrations of sulphide (S

2-

), sulphate (SO

42-

).

Sulphide: The concentration of sulphide was determined using a Spectroquant Sulphide

Test Kit (Merck, Johannesburg South Africa). The bottles in the kit were labelled S

-1

, S

-2

and

S

-3

respectively. 1 mL of bacterial supernatant was added to 9 mL distilled water in a 10mL volumetric flask. 5 mL of each diluted bacteria sample was transferred into different test tubes. 1 drop of S

-1

was added to each test tube and mixed for 15-30 seconds. 5 drops of S

-2 was added to each test tube and mixed for 15-30 seconds. 5 drops of S

-3

was finally added to each test tube and mixed for 15-30 seconds. The mixture was then let to stand for about a two minutes for full colour development. The colour formed was measured at a wavelength of 665 nm using a UV spectrophotometer (WPA, Light Wave II and Labotec, South Africa).

Sulphate: 1 mL of sample was added to 99 mL with distilled water in a 250 mL Erlenmeyer flask. 5 mL of conditioning reagent was added to the flask, the solution was mixed by means of a magnetic stirrer and (0.2-0.3 g) of Barium Chloride (BaCl

2

) crystals was added to the solution during stirring. The solution was let to stir for one minute at constant speed.

Immediately after the stirring period, the mixture was poured into an absorbance cell for turbidity measurement at wavelength of 420 nm, using a UV spectrophotometer. Readings for a sample were recorded over a 4 min period after analysing 30 seconds between readings.

The final reading was recorded after the readings became stable.

3.6.3 Mass Balance Analysis

Mass balance analysis was performed in order to determine the most suitable environmental condition for sulphur formation. Mass balance was calculated to account for the initial and final sulphide (S

2-

), sulphate (SO

42-

) and elemental sulphur (S

0

).

Mass

Accumulated

= Mass

Input

– Mass

Output

+ (Mass

Generated/ Destroyed

) (3.1)

In a batch reactor there is no input or output therefore the above equation is transformed to:

48

Mass

Accumulated

= + Mass

Generated/ Destroyed

(3.2)

Mass

Accumulated

= V (dC/dt) and Mass

Generated/Destroyed

= V (dC/dt) reaction only

(3.3)

(dC/dt) = (dC/dt)

reaction only

(3.4)

Sulphide Removal Efficiency = {([Sulphide]

initial

– [Sulphide] final

) ÷ [Sulphide]

initial

} *100

Sulphur recovery (%) = ([Sulphur] final

÷ [Sulphur] initial

) * 100

3.6.4 Microscopic Analysis of Isolated Bacteria

Microscopy was used to visualise extracellular and intracellular sulphur globules and sulphur deposition on cell surfaces. Surface scanning techniques (FPX) were used to evaluate elemental species distribution inside and outside the cells.

3.6.4.1 Scanning Electron Microscopy (SEM)

SEM was used to visualise the presence of extracellular sulphur globules. Agar gel plates containing microbial colonies were first excised and trimmed to small sizes. The gel blocks were fixed in 2.5% glutaraldehyde for of 15 min. After the fixation step, the cells were rinsed with phosphate buffer to remove any excess fixative. The cells were rinsed once for 10 min then three times for 20 min. The cells were then dehydrated by immersing in graded series of ethanol. They were immersed in 50% ethanol for 5 min, 70% ethanol for 10 min, 80% ethanol for 10 min, 90% ethanol for 15 min and finally twice in 100% ethanol for 20 min.

The cells were then dried using the critical point drying method before mounting on metallic stubs using a double sticky tape. The cells were then coated with gold a conductive metal to increase their conductivity in the microscope and to avoid the build-up of high voltage charges on the cells.

3.6.4.2 Transmission Electron Microscopy (TEM)

TEM was also used to reveal the presence of intracellular sulphur globules since it allows the cells to be viewed at very high magnifications compared to SEM. The cells were cleaned to remove unwanted deposits. Phosphate buffer was used to rinse the cells three times for 10

49

min at room temperature. The cells were prefixed in 2.5% glutaraldehyde solution. Excess glutaraldehyde solution was removed by rinsing the cells with phosphate buffer once for 10 min then three times for 20 min. The secondary fixation was performed to preserve the structure of the cells and to protect the cells: the cells were fixed with 1% osmium tetroxide for 1.5 hours at room temperature. This was followed by dehydration in graded series of ethanol: 50% ethanol for 5 min, 70% ethanol for 10 min, 80% ethanol for 10 min, 90% ethanol for 15 min and 100% ethanol twice for 20 min at room temperature. The cells were then immersed in propylene oxide twice for 20 min at room temperature because ethanol is not miscible with the plastic embedding; the process was used for alcohol substitution. This was followed by the immersion of the cells in resin at room temperature overnight in a fume hood to allow evaporation of resin. The next day the cells were immersed in pure resin for 2 hours at room temperature. Polymerization of the epoxy mixture was achieved by placing the cells in a drying cabinet for 2 days at 40°C and for additional 2 days at 60°C. The cells were then cut into ultrathin slices and stained with uranyl acetate followed by lead citrate. The sections were then mounted in immersion oil.

50

CHAPTER 4

RESULTS

4.1 Removal of sulphide under heterotrophic conditions

From the dry sludge, 6 distinct microbial species of which bacteria and filamentous microorganisms were isolated. The bacterial population showed the presence of both gram positive and negative species (See Figure 1, Appendix A) which were in agreement with

Gilfillan (2000), Bowker (2000) and Molwantwa (2008) findings.

The 16S rRNA sequencing resulted in the identifications of the possible phenotypes with

99% probability match as shown in Table 3.1 and the phylogenetic trees of the bacterial species were shown in Figure 2 (Appendix A).

Table 4.1: List of the sulphide oxidizing microorganisms isolated from dry gold mine sludge

Sample Name

X1

X2

X3

X4

X5

X6

Blast Results

Micrococcus luteus

Lysinicibacillus fusiformis

Lysinicibacillus sphaericus

Pseudomona putida

Fusarium oxysporum

Penecillium simplicissimum

Max ID (%)

99%

99%

99%

99%

98%

98%

The Sulphide Removal Test was aimed at selecting the most efficient microorganisms for sulphide removal and also to investigate the effect of the carbon sources: lactate and glucose on sulphide removal. The carbon sources were both added at a concentration of 5 g/L for each experiment. Only organisms with sulphide removal rates above 90% were selected as the most efficient sulphide oxidizing microorganisms for later use.

51

4.2.1 Rate of removal at 50 mg/L Sulphide

Figure 4.1 illustrates that each microbial species reacted differently depending on the carbon source present. The species X

1

, X

5

and X

6

(identified in Table 4.1) achieved complete removal of sulphide in the presence of lactate 4 hours after incubation, whereas species X

2 only achieved complete removal of sulphide in the presence of glucose. Contrarily species X

4 did not show any difference in sulphide removal rate in the presence of either carbon sources. Based on the observed results, the strains were classified into two different groups, i.e., the lactate group and the glucose group. The lactate utilizing group consisted of X

1

, X

5 and X

6

while the glucose group of X

2

, X

3

and X

4

.

Isolated Cultures

Figure 4.1: Sulphide removal efficiency by individual species under lactate and glucose as solo added carbon sources

4.2.2 Rate removal at 57 mg/L Sulphide a) Lactate Group

Based on Figure 4.1, species X

1

, X

5

and X

6

were selected for a further study. The other species X

2

and X

3

, although they oxidized sulphide successfully, their oxidation rates were

15% lower than the oxidized sulphide in the presence of glucose as the solo carbon source.

Using species X

1

, X

5

and X

6

, a concentration-time series batch experiment was conducted and the results on sulphide removal are shown in Figure 4.2.

52

Figure 4.2: Sulphide removal efficiency in the presence of lactate

It was observed that the species X

5

and X

6

achieved the best oxidation rate with X

5

having the highest sulphide removal rate (95.8%) and X

1

being the least efficient with a sulphide removal efficiency of 68.25%. Sulphide concentration increased in each flask with more than

50% increase for X

1

and X

6

. The increase in sulphide concentration was assumed to be due to the active sulphate reduction taking place as a result of the readily available lactate present in the reactor. The presence of lactate was said to stimulate the growth of sulphate reducing bacteria so increasing the microbial diversity and finally making the sulphide oxidation less efficient (Kaksonen, 2004; Oyekola, 2008).

b) Glucose Group

From the cultures grown on glucose, the species X

5

, X

2

and X

3

strains showed the best results (Figure 4.1). However, X

2

and X

3

were observed earlier to also be able to grow in the absence of organic matter. Therefore they were tested for sulphide removal in the presence and absence of a carbon source. Figure 4.3 illustrates that sulphide removal by

Lysinicibacillus

sp. with glucose as the carbon source, was similar to sulphide removal in the absence of any carbon source after incubation for 4hours (up to 95% of sulphide removal efficiency was

53

recorded). The lowest sulphide removal rate was recorded when lactate was supplied as the carbon source.

Isolated Cultures

Figure 4.3: Sulphide Removal rate by L. fusiformis and L. sphaericus after 4 hours of incubation

The ability of the

Lysinicibacillus sp. to remove sulphide with or without glucose was recognized as a beneficial attribute for sulphide removal under heterotrophic conditions.

Figure 4.4 shows sulphide removal rate by individual and mixed cultures when the initial sulphide concentration was 57 mg/L.

54

Figure 4.4: Sulphide removal rate in the presence of glucose

Lysinicibacillus fusiformis showed constant sulphide removal efficiency throughout the test and had the highest efficiency (97.84%) while Lysinicibacillus sphaericus was decreasing as the incubation time was progressing. However, the mixed culture (X

2

+ X

3

) showed the lowest removal efficiency at the beginning of the test but as the time passed, the mixed strains reached its maximal sulphide oxidizing rate (96%) before sulphide concentration increased.

The sulphide concentration was recorded to increase by 4.57%; 4.00% and 1.34% in (X

2

+

X

3

), X

3

and X

2

flasks respectively and it was assumed to be due to sulphate reduction. The reported performances of both L. fusiformis and the mixture of both Lysinicibacillus sp. were similar to the sulphide removal efficiency reported by Chen et al., (2009). For this reason they were chosen for further studies and to also compare between single and mixed species.

4.3 Investigation of Optimal Conditions

4.3.1 Optimum pH Values

The pH of a solution is known to affect both bacterial activity and the type of sulphide species present. The effect of pH on sulphide oxidation was investigated using various pH

55

values ranging between 6 and 8 with the same sulphide concentration in the reactor. This range was chosen based on a report by Donfeng et al., (2011), stating that the range of pH (6 to 8) was the optimal pH range for the bacterial activity and the pH of the sulphide rich effluent from an acid mine drainage treatment always fall within the same range (Cao et al.,

2009 and Singh et al., 2011).

4.3.1.1 Effect of pH on

Lysinicibacillus fusiformis

on sulphide removal activity

The results of the tests are illustrated in Figures 4.5 and 4.6. Figure 4.5 illustrates the sulphide removal rate over time at different pH values, while Figure 4.6 shows the average sulphide removal rate at the end of the experiment. In Figure 4.5, it was observed that sulphide removal rate was above 90% at every pH value. The sulphide concentration fluctuated in each flask: at pH 6 the concentration was constantly changing, while at pH 7 the concentration remained constant, only after 80 minutes with an average of percentage decrease of 2%. At pH 8, the sulphide concentration only decreased of 1.5% at 100 minutes, this was considered not to be significant.

Figure 4.5: Sulphide Removal Rate at the different pH values.

At pH 6, H

2

S is the dominant sulphide species but as the pH increases towards 8, bisulphide

(S

2-

) ions tend to be dominant in the solution. S

2-

ions are soluble therefore they are considered to be undesirable species that should be removed. Mamashela (2002) reported

56

that the H

2

S species are not readily oxidized so the rate of sulphide oxidation tends to be lower. This is confirmed by the results obtained which show sulphide concentration variation throughout the experiment (Figure 4.5) and the lowest sulphide removal efficiency (Figure

4.6). As the pH increased, the oxidation rate increased with the highest sulphide removal efficiency recorded at pH 8 which was on agreement with findings reported by Chen et al.,

(2008) and Xu et al., (2012).

Figure 4.6: Box and Whisker plots showing the mean distribution of the sulphide removal rate by L. fusiformis

4.3.1.2 Effect of pH on Sulphide Removal Activity by Mixed Culture

At pH 8, the sulphide removal occurred slowly (Figures 4.7 and 4.8). While sulphide removal efficiencies were at its highest at pH values of 6.7 and 7. Mamashela (2002) suggested that S

2ion species remained in the solution, therefore they were not oxidized. It was also assumed that a competition between the Lysinicibacillus species over the substrate may have also affected the ability of the mixed bacteria to oxidize sulphide at pH 8. Figure 4.7 illustrates the change of sulphide concentration over time, while Figure 4.8 shows the sulphide removal rate at the end of the experimental test.

57

Figure 4.7: Sulphide Concentration at different pH values

Figure 4.8: Box and Whisker plots showing the mean distribution of the Sulphide removal rate by both L. fusiformis and L. sphaericus

4.3.2 Oxidation Reduction Potential (Eh)

The aim of the experimental test was to determine the optimum conditions in term of Redox potential for partial sulphide oxidation

58

4.3.2.1 Effect of Eh on sulphide removal activity by Mixed Culture

Steudel (1996) reported that at a redox potential (Eh) of -130 mV elemental sulphur was generated. For this reason, the mixed strains were tested to confirm elemental sulphur generation under this redox potential but at pH values of 6.7 and 7. Eh was increased from

≃-210 mV to -130 mV by adding oxygen gas into the reactor. Figure 4.9 shows the change in sulphide concentration, over time at different pH and Eh values. It was observed that as the

Eh values increased the sulphide removal rate increased further which was on agreement with Molwantwa (2008). The straight and dotted lines in Figures 4.13 and 4.14 represent the original and modified Eh respectively.

(A) (B)

Figure 4.9: Sulphide concentration over time using mixed culture at (A) pH 6.7 and (B) pH

7

4.3.2.2 Fate of Elemental Sulphur in the Reactor.

The quantitative determination of the elemental sulphur generated was done by the mass balance calculation as explained by Chen et al (2009):

[S in2-

] + [SO

42-in

] + [S

2

O

3in2-

] + [S out2-

] + [SO

42-out

] + [S

2

O

3out2-

] = [production of (biomass-S +

S

0

] (4.1)

59

Since a batch reactor was used in this study no input and output were catered for. Initial and final concentrations were measured and thiosulfate (S

2

O

32-

) concentration was not measured because the biological activity was low (van den Bosh et al., 2008), therefore (4.1) was modified to:

[S initial2-

] + [SO

42-initial

] + [S final2-

] + [SO

42-final

] = [production of (biomass-S + S

0

]

(4.2)

The results are presented in Tables 4.2 and 4.3:

Table 4.2: Reactor performance on sulphur containing compounds in the batch reactor during the experimental phase (EP 3) at pH 6.7

Time

(minutes)

Sulphide

(mg/L)

Sulphate

(mg/L)

S

2-

removal rate (%)

S

0 production

(mg/L)

S

0 recovery

(%)

Initial final S initial2-

S final2-

SO

42initial

0

15

15 50 0.11 0

SO

42final

4.62

30 0.11 0.11 4.62 17.34

30

45

45 0.11 0.009 17.34 19.64

60 0.009 0.37 19.64 25.42

99.78

0

91.82

-

45.27

-12.72

-2.19

-.6.14

90.54

-

-

-

Table 4.3: Reactor performance on sulphur containing compounds in the batch reactor during the experimental phase (EP 3) at pH 7

Time

(minutes)

S

2-

(mg/L) SO

42-

(mg/L) S

2-

removal rate (%)

S

0 production

(mg/L)

S

0 recovery

(%)

Initial final S initial2-

S final2-

SO

42initial

0

15

30

45

15 50 0.17 0

SO

42final

9.24

30 0.17 0.07 9.24 18.5

45 0.07 0.04 18.5 26.58

60 0.04 0.2 26.58 28.9

99.66

58.82

42.86

-

40.59

-9.16

-8.05

-2.48

81.18

-

-

-

From Tables 4.2 and 4.3, it can be deduced that 10 minutes after inception of the test, 99% of sulphide was removed in both reactors. Thereafter, a decrease in sulphide removal was noted with an average of 64% and 67% at pH 6.7 and 7. This confirmed previous

60

observation: as the pH increase, the rate also increases (Mamashela, 2002). At the end of the test, the sulphide concentration was observed to increase to over 100%.

In both reactors, maximal sulphur concentration was observed to occur 10 minutes within the beginning of the experiment afterwards, it gradually decreased while the sulphate concentration was observed to gradually increase. This could be explained by the oxidation of sulphur to sulphate after 10 min and the negative values of sulphur in Tables 4.2 and 4.3.

Elemental sulphur formation was observed to be higher at pH 6.7 with a sulphur recovery of

90%. For this reason, any batches were subsequently incubated at pH 6.7 to observe the presence of sulphur deposits. An example of sulphur deposition of cells at pH 6.7 is shown in the Figure 4.10 (below).

Figure 4.10: (A) Scanning electron micrographs and (B) Transmission electron micrographs of elemental sulphur

In Figure 4.10 (a) extracellular sulphur globules were observed with the scanning electron microscope. The Transmission electron micrograph (Figure 4.10 (b)) showed an inclusion

61

that was observed to be an intra-cellular sulphur globule. INCA analysis was used to confirm the presence of sulphur within the cells. Low sulphur concentration was observed due to the use of ethanol which removes sulphur during the preparation of the cells for the SEM and

TEM (Bruser et al., 2009).

4.3.2.3 Molar Mass Balance on Sulphur species

This exercise was conducted because mass can only be conserved. All sulphur in the system must then be accounted for. Figure 4.11 illustrates the cumulative mass balance among the sulphur-containing compounds at pH 6.7 and Eh 130 mV.

Figure 4.11: Mass balance analysis of the sulphur containing compound at pH 6.7 and Eh=-

130mV.

From the above Figure (Figure 4.11 A), it was observed that the oxidative reaction of sulphide to elemental sulphur respect a polynomial curve at the second degree. The results obtained were on agreement with earlier studies (Liu et al., 2004, Romano, 2012 and Nwoye et al., 2013) which concluded that the effect of oxygen on the sulphide oxidation rate to

62

elemental sulphur was not a linear trend but a polynomial one. This also explained that several other reactions may have taken place in the solution just like Romano (2012) stated.

During the biooxidation of sulphide, Thurston et al (2010) proposed the oxidation of sulphide to sulphate occurs via an intermediate sulphur species which was then found to be elemental sulphur by Heidel and Tichomirowa (2011). A linear regression line fits the model proposed by Heidel and Tichomirowa (2011) stipulating the oxidation of sulphate via sulphur. Heidel and Tichomirowa findings (2011) were in accordance with above results so confirming that sulphur oxidation to sulphate fits a linear trend therefore describing the process to be stable and confirming that every moles of sulphur were directly converted to moles of sulphate.

4.3.2.4 Effect of Eh on sulphide Removal Activity by

Lysinicibacillus fusiformis

It was observed earlier that the pH values do not intensely affect the rate of sulphide removal by Lysinicibacillus fusiformis, however the best removal rates were observed at pH 8. It was also observed from the studies carried out with the mixed culture that at Eh -130 mV, sulphur was produced confirming results presented by Steudel, 1996. On the other hand,

Molwantwa, (2008) observed that Eh could get around -100 mV for sulphur generation.

Figure 4.12 shows the changes in sulphide concentration over time at pH 8 and Eh -80 mV.

It can be observed that as the Eh increases, the sulphide oxidation rate also increases.

Figure 4.12: Sulphide concentrations at pH 8 and Eh -80mV

63

Table 4.4 shows the mass balance analysis for sulphide removal under the conditions of pH

8 and Eh = -80mV. Figure 4.13 illustrates the cumulative mass balance among the sulphur containing compounds under these conditions.

Table 4.4- Reactor performance on sulphur containing compounds in the batch reactor at pH 8 and Eh=-80mV

Time

(minutes)

S

2-

(mg/L) SO

42-

(mg/L) S

2-

removal rate (%)

S

0 production

(mg/L)

S

0 recovery

(%)

initial final S initial2-

S final2-

SO

42initial

0

10

10 50 0.153 0

SO

42final

2.3

20 0.153 0.096 2.3 3.59

60

70

80

20

30

40

50

30

40

50

60

70

80

90

0.096

0.11

0.159 0.079

0.079 0.062

0.062 0.057

0.057 0.043 13.21 15.33

0.43

0.11

0.159

0.57

3.59

5.08

6.66

8.11

5.08

6.66

8.11

9.26

9.26 13.21

15.33 20.31

99.69

37.25

-

-

50.31

21.52

8.06

2.46

-

47.55

-1.23

-1.50

-1.63

-1.37

-1.13

-4

-2.11

-5.12

95%

-

-

-

-

-

-

-

-

64

Figure 4.13: Mass balance analysis of the sulphur containing compound at pH 8 and Eh=-80 mV: Conversion of (A) sulphide to sulphur and (B) sulphur to sulphate

From Table 4.4 it can be observed that 10 minutes into the experiment, elemental sulphur concentration reached the highest, but afterwards sulphur was being oxidized to sulphate due to observed increase in sulphate concentration. In spite of sulphur oxidation, sulphide was oxidised. However, greater sulphur recovery was recorded (95%) at this condition compared to the previous ones (90% at pH 6.7 and Eh -130mV). Those results implied that the redox potential is the main tool in sulphur recovery thus agreeing with Xu et al finding (2012).

Likewise in the previous conditions (Eh = -130 mV and pH 6.7), sulphide oxidation to elemental sulphur also fits on a polynomial curve of second degree and sulphur oxidation to sulphate has a linear trend. From Figure 4.13(A), it was observed that sulphide oxidation was unstable while Figure 4.13 (B) shows stable sulphur oxidation with a linear trend: a large amount of sulphur produced was directly converted to sulphate whereas, sulphide was oxidized but some of it was not converted to elemental sulphur.

65

4.4 Biological Treatment of Acid Mine Drainage in Batch Reactor

Sulphate reducing bacteria (SRB) are known to utilize carbon sources such as lactate, ethanol, acetate, lignocellulose and sawdust as electron donors during the reduction of sulphate to produce sulphide (Tang et al., 2010). Studies have shown that organic matter for SRBs can be expressed in the form of a COD/sulphate ratio (Mc Cartney & Oleszkiewicz., 1993; Li et al.,

1996). Rinzema et al., (1988) reported that a COD/sulphate ratio of 0.67 did not provide sufficient sulphate for SRBs to completely utilize organic matter. At a ratio below 0.67, the amount of organic matter was insufficient for complete sulphate reduction.

The majority of integrated sulphate removal experiments consist of two reactors: one for sulphate reducing phase and the second one for sulphide oxidizing phase. It would be an ideal to have SRB and SOB working in the same reactor. Xu et al., (2012) demonstrated the technical feasibility of a single reactor (81.5% of sulphate was removed and up to 72% of sulphur was recovered. There have not been more successful studies reported regarding a single reactor. The objective of these set of experiments was to investigate the feasibility of mixed

Lysinicibacillus sp. to recover elemental sulphur from a sulphate rich solution in a single batch reactor. Preliminary studies were first carried out to determine the suitable

COD/Sulphate ratio for biogenic sulphide production.

4.4.1 Influence of COD/Sulphate Ratio on pH

pH was considered as an indicator and controller of the performance of the bacteria strains in the different batch reactors. Figure 4.14 illustrates the changes in pH values in each sample during the incubation period. It was observed that the pH uniformly increased before decreasing. The initial increase in pH can be explained by the production of alkaline species which are generated during the metabolic reaction of the bacteria (Equation 4.3). The increase in pH was thought to be beneficial for bacteria growth.

2CH

3

CHOHCOO

-

+ SO

42-

→ 2CH

3

COO

-

+ 2HCO

3-

+ HS

-

+ H

+

(4.3)

The decrease in pH values is most likely due to the production of H

2

S in the solution. At the end of the experiment, the final pH was ranging between 6.3-6.95 which was close to neutral.

66

Figure 4.14: Change of pH during anaerobic culture with COD/Sulphate Ratio

4.4.2 Influence of COD/Sulphate ratio on sulphate removal rate

A high removal efficiency of sulphate indicates high bacteria activity (SRB) thus higher biogenic sulphide production, while low sulphate removal efficiency implied a decrease in

SRB activity. The changes in sulphate concentration during the incubation period of the experiment are illustrated in Figure 4.15. Maximum sulphate removal rate is shown in Figure

4.16.

67

Figure 4.15: Change of Sulphate Concentrations during the batch culture with

COD/Sulphate ratio

Similar to changes in pH, sulphate concentration started to decrease at 12hours and reached the lowest concentration at the end of the incubation period. At the ratio of 0.67 the concentration reached its lowest at 48 hours before attaining an increase again of 18%.

Figure 4.22 shows the sulphate removal rates at the different ratios. As the ratio increased from 0.6 to 2.25, the sulphate removal also increased. It was evident that COD/Sulphate ratio of 2.25 was the optimum for the sulphate removal and growth of SRB.

68

Figure 4.16: Sulphate removal rate at the different COD/Sulphate ratios

4.4.3 Influence of COD/Sulphate ratio on Biogenic Sulphide production

Figure 4.17 shows the changes in sulphide concentration throughout the incubation period with respect to COD/Sulphate ratio. Similar to the removal of sulphate, sulphide concentration increased and at the end of the experiment, sulphide concentrations were 31,

67, 119, 99 and 86 mg/L for COD/Sulphate ratio of 0.67, 1.7, 2.25, 3 and 5 respectively.

According to a study by Choi and Rim (1991) COD/Sulphate ratios exceeding 2.7 could have negative effects on SRB activity. El Bahoumy et al., (1999) suggested that

COD/Sulphate values ranging from 1.5 to 2.5 was more than enough for sulphide production to reach maximum values when lactate is used as the carbon source. This explains the high sulphate reduction rate and sulphide production at a ratio of 2.25.

It was reported that the activity of SRB is inhibited when sulphide concentration reaches

900mg/L of total sulphide concentration of which 450mg/L are in the un-ionized form

(Renze et al., 1997). Therefore the sulphide produced at all samples could not have a huge effect on bacterial activity.

69

Figure 4.17: Variation of sulphide concentration over time during batch culture with

COD/Sulphate ratio

4.4.4 Partial Sulphide Oxidation and Elemental Sulphur Recovery

Since the COD/Sulphate ratio of 2.25 showed the best results, the biogenic sulphide produced was used for elemental sulphur formation. At the end of the sulphate reducing phase, the mixed

Lysinicibacillus species were inoculated into the reactor and glucose nutrient broth was also added as organic matter for the sulphide oxidizing phase to occur. Figure 4.18 shows the concentrations of sulphur species in the reactor throughout the incubation period.

70

Figure 4.18: Changes of Sulphide (A), Sulphur and Sulphate (B) concentrations during the incubation period

71

Table 4.5: Single batch reactor performance on sulphur containing compounds

Time

(minutes)

S

2-

(mg/L) SO

42-

(mg/L) S

2-

removal rate (%)

S

0 production

(mg/L)

44

48

52

56

28

32

36

40

60

64

68 initial final S initial2-

S final2-

SO

42initial

0

4

4

8

119

98

98 83

SO

42final

85

92.7 83 85.9

8

12

16

20

24

12

16

20

24

28

92.7

98.2

79.7

72.5

68.1

98.2

79.7

72.5

68.1

65

85.9

86.7

88

90.4

90.7

86.7

88

90.4

90.7

92.5

32

36

68

65

54

64 43.1

45

54 92.5 93.4

40.9 93.4 94.9

40 40.9 45.2 94.9 95.6

44 45.2 31.9 95.6 96.7

48 31.9 26.1 96.7 98.1

52 26.1 30.5 98.1 98.5

56 30.5 38.8 98.5 100.5

60 38.8 43.1 100.5 102.3

45 102.3 100.9

50.8 100.9 105.4

72 50.8 57.3 105.4 108.2

17.6

5.41

-

18.8

8.78

6.06

4.55

17

24.26

-

29.42

18.18

-

-

-

-

-

-

10.1

11.6

-5

12.2

4.4

-4.8

-10.3

-6.1

-0.5

-10.3

9.3

18.1

2.4

-6.3

16.1

4.8

4.1

1.3

S

0 recovery

(mg/L)

148

235

26

516

24

14

16

72

Figure 4.18 (A) shows the production of sulphide and its removal over time, during the sulphate reducing (SR) phase and the sulphide oxidizing (SO) phase. Table 4.5 shows the concentration of the sulphide, sulphate and sulphur measured in the single batch reactor. It was observed that, the sulphide oxidizing phase was unsuccessful. An average of 3% sulphide removal rate with a maximum rate of 18% was recorded. It was assumed that the

SOB bacterial activity was affected by the presence of SRB, as well as the low dissolved oxygen concentration (Okabe et al., 2005). The presence of lactate most likely stimulated the growth of SRB (Kaksonen, 2004 and Oyekola, 2008), thereby making the SO process less efficient. This was in accord with Celis- Garcia et al (2008) findings which also demonstrated low sulphur recovery.

Figure 4.19: Variation of pH and Eh throughout the experiment

Figure 4.19 illustrates the changes in pH and Eh values in the integrated reactor. The highest pH reading throughout the entire experiment was 8.5. The subsequent decrease in pH can be attributed to the presence of hydrogen ions, produced from the oxidation of elemental sulphur to sulphate as followed:

73

2S

0

+2OH

-

+ 3O

2

→ 2SO

42-

+ H

+

(4.4)

Redox potential was also affected during the production of S

0

. The redox potential increased from -320mV to -156mV in the SR phase and subsequently decreased from -130mV to -

312mV. This represents an inverse relationship between pH and Eh during the course of the treatment process

4.5 Chapter Summary

In the integrated reactor, 87% of sulphate was reduced to sulphide and 18% of the sulphide was measured to be left as residual but a low amount of sulphur was formed. It was concluded that the lactate present in the reactor could have stimulated the growth of SRB which affected the sulphide oxidizing reactor.

74

CHAPTER 5

KINETIC MODELLING

5.1 Viable Biomass of

Lysinicibacillus fusiformis

Incubated on sulphide (50-100 mg/L) under optimum conditions

Figure 5.1: Biomass of L.fusiformis at sulphide concentration of: ( ) 50 mg/L and ( )

100 mg/L

At an initial concentration of 50 mg/L, the biomass reached a maximum value 12000 after 6 h of incubation, while an initial concentration of 100 mg/L showed maximal biomass of

11800 after 12 h of incubation. A decline in biomass value was observed subsequently for the separate concentrations Buisman et al. (1991) stated that microbial growth could not be inhibited by a sulphide concentration of 300 mg/L thus the reduction in biomass could be the result of reduction in concentration of organic matter.

75

5.2 Kinetic Modelling Theory

The quantitative determination of elemental sulphur formation was done by mass balance calculation (Chen et al., 2009) or via sulphite method (Jiang et al., 2009). Xu et al (2013) stated that kinetic models could also assist during the optimization process of elemental sulphur generation.

At low and high oxygen concentration, sulphide is oxidized to elemental sulphur (Equation

5.1) and sulphate (Equation 5.2) respectively.

2HS

-

+ O

2

→ 2S o

+2OH

-

(5.1)

2HS

-

+4O

2

→ 2SO

42-

+ 2H

+

(5.2)

According to Eq. (5.1), sulphur production depends on sulphide ion (HS

-)

and dissolved oxygen. Dutta (2008) stated that when two substances limit the biological reaction rate, the rate of the product adopted is the following: 𝑟 =

𝐶ₑ × 𝑘₁ × [𝐻𝑆⁻] 𝑘₂ + [𝐻𝑆⁻] ×

𝑂₂ 𝑘₃ + 𝑂₂ (5.3) where:

C e

= Optical density of the bacteria,

k

1

= reaction rate constant (mg L

-1 h

-1

),

k

2

= reaction rate constant (mg L

-1

),

k

3

= reaction rate constant (mg L

-1

),

[HS

-

]= concentration of sulphide (mg/L) and

O

2

= concentration of the dissolved oxygen (mg/L).

However, redox potential was used as the oxygen control, therefore the dissolved oxygen concentration was ignored (see Eq. 5.4). 𝑟 =

𝑋 × 𝑘₁ × [𝐻𝑆⁻] 𝑘₂ + [𝐻𝑆⁻] (5.4)

Analogous to the Monod kinetics,

k

1

is analogous to the maximum conversion rate (

k m

) divided by the yield (Y),

C e

is analogous to the biomass concentration (X) and k

2

is analogous to K half saturation constant.

76

𝑟 = 𝑘𝑚

𝑌 ×

[𝐻𝑆

𝐾 + [𝐻𝑆

]

] 𝑋 (5.5)

Monod kinetics has been used by several researchers (Alcantara

et al., 2004; Gadekar et al.,

2006; Ni et al., 2012 and Xu et al., 2013). It was proposed as an appropriate expression to highlight substrate utilisation during elemental sulphur formation. The model also highlights that the rate and the extent to which sulphide oxidation in a bacterial system is affected by the biomass concentration and endogenous decay rate represented by the term

k d

. This indicates that the substrate consumed by the bacteria provides the energy required for growth, reproduction and metabolic function. Therefore, the active biomass concentration is assumed to increase with the decreasing concentration of substrate. Therefore at t=0, the substrate concentration is high and the biomass concentration is low, the rate of decay of the bacteria is then written as Equation 5.6 and when the substrate is exhausted, the rate of decay is expressed as Equation (5.7).

𝑋 = 𝑋₀𝑒

(𝑘𝑚−𝑘𝑑)𝑡

(5.6)

𝑋 = 𝑋₀ 𝑒

−𝑘𝑑𝑡

(5.7)

Where:

k d

= endogenous decay rate (time

-1

),

X o

= initial biomass concentration (mg/L),

k m

= maximum specific sulphide oxidation rate (time

-1

).

However during the optimal condition investigations, the bacterial cells were harvested thus their concentration was kept constant throughout the experimental phase. So Equation 5.5 was written as: 𝑟 = 𝑘𝑚

𝑌 ×

[𝐻𝑆

𝐾 + [𝐻𝑆

]

] (5.8)

Sulphide Oxidation involves two main processes which were illustrated in Equations 5.1 and

5.2. Both processes were incorporated into the above model (Equation 5.8).

Process 1: Sulphide conversion to elemental sulphur

77

𝑟 = 𝑘𝑚ᵅ

𝑌ᵅ ×

[𝐻𝑆

]

𝐾ᵅ + [𝐻𝑆

] (5.9)

Process 2: Sulphur conversion to sulphate 𝑟 = 𝑘𝑚ᵇ

𝑌ᵇ

×

[𝑆⁰]

𝐾ᵇ + [𝑆

]

(5.10)

5.3 Parameter Evaluation

The unknown parameters defined based on the equations, and the type of systems used to produce elemental sulphur, were

k m

, K c

and Y in each process. These paramters were determined by performing a nonlinear regression analysis using Aquasim (Riechert, 1998).

For each parameter, a search was performed through a range of values. Trial values of the unknown parameters were initially investigated. It was also required to impose some constraints, in order to set upper and lower limits for each parameter so that invalid values were not omitted. Whenever optimization converged at/or very close to a constraint, the latter was relaxed until the constraint no longer forced the model. The processes were repeated till unique values for each parameter, lying away from the constraints but between the set of limits were determined. The best fit values were found through repetition of parameter estimation of the unknown. The objective function for parameter optimization was defined as the least sum of the squares between the observed and the modelled concentrations as follows: 𝜎² =

1 𝑛 − 𝑞 𝑖=𝑛

�(𝑦ᵢ − 𝑦) 𝑖=1

2

(5.11)

Where: σ= average deviation of model from the measured values; y i

= observed variables; y= simulated variables; n= number of observations and q= degrees of freedom representing the number of parameters being evaluated.

78

The batch reactor used was a volumetric flask of 250mL and a working volume of 100mL.

The initial concentration of sulphide ions (S

2-

) was of 50 or 100mg/L. Oxygen was purged into the reactor to give an initial ratio of 2:1 (sulphide:oxygen). The temperature was kept constant throughout the test at 30±1°C and the initial pH was 8.

5.3.1 Kinetic Parameter Estimation

Experimental data with sulphide concentration of 50 mg/L at pH 6.7 and 8 and Eh = -130 and -80 mV respectively were used to estimate the kinetic parameters

k m

, K c

and Y in each process . The validation of both models was performed and Figure 5.2 confirms that the kinetic parameters values were obtained under the different conditions (See Table 5.1).

Table 5.1: Optimum Kinetic Parameter in Biological Sulphide Oxidation

Sulphide Oxidation Sulphur Oxidation

k ma

10

K a

0.010

Y a

3.00

χ

2

14.74

k mb

746

K b

Y b

χ

2

8401 6.85 13.04 pH 6.7 and

Eh= -

130mV pH 8 and

Eh= -

80mV

126 564.73 0.377 37.72 682 9598 10.3 21.63

As the Oxidation reduction potential increased, the half saturation ( K) constants were observed to increase whereas the yield coefficients ( Y) were decreasing but the maximum conversion rate (

k m

) seemed to decrease during the sulphur oxidation stage. χ

2 also increased as Eh increased but the parameters were more accurate during the sulphur oxidation stage since χ

2

were smaller. Those results implied that ORP was the main parameter affecting sulphur formation process thus confirming earlier findings.

During the sulphide oxidation stage, at pH 6.7 and Eh= -130 mV,

k m

was observed to be similar and higher to those reported in Gadekar

et al., (2006) and Xu et al. (2013) respectively.

The half saturation constant was lower to values reported by Alcantara et al., (2004); Gadekar

79

et al.,( 2006) and Xu et al., (2013) but higher under optimal conditions and the Yield coefficient was higher than Xu et al., (2013).

Figure 5.2: Sulphur species oxidation in batch cultures at (A) pH 6.7 and Eh -130mV and

(B) pH 8 and Eh -80mV.

Strigul et al., (2009) indicated that in a Monod model, a stationary phase is produced and it is infinite, this was observed in Figure 5.2, as the sulphur and sulphate concentrations reached a stationary phase. Strigul et al (2009) also revealed that the microbial concentration in the stationary phase determines the yield coefficient, since the microbial concentration was

80

unknown but the yield was estimated at each process it was deduced that microbial concentration was higher in the sulphur oxidation but it was at its lowest at the sulphide oxidation stage under optimal conditions meaning the biomass concentration was increasing.

From Figure 5.2, It was noted that sulphide will be eliminated from the system but in Figure

5.2 (A) only a few amount (around 5 mg/L) of elemental sulphur will be left in the flask whereas the entire sulphur species will be in the form of sulphate and in Figure 5.2 (B) around 20mg/L of elemental sulphur will be left as a residual while sulphate will be the main sulphur species formed.

5.3.2 Sensitivity Analysis

The sensitivity analysis combines the tasks of identifiability analysis and uncertainty analysis.

The latter will not be discussed in this study. Identifiability analysis aims on verifying if the parameters can be determined using the measured data during the experimental phase and it also focuses on estimating the uncertainty of the parameters estimates. Additional information of the identifiability analysis is the sensitivity functions which are written below: 𝛿 𝑎,𝑎 𝑦,𝑝

=

𝜕𝑦

𝜕𝑝 (5.12) 𝛿 𝑟,𝑎 𝑦,𝑝

=

1 𝑦

𝜕𝑦

𝜕𝑝 (5.13) 𝛿 𝑎,𝑟 𝑦,𝑝

= 𝑝

𝜕𝑦

𝜕𝑝 (5.14) 𝛿 𝑟,𝑟 𝑦,𝑝

= 𝑝 𝑦

𝜕𝑦

𝜕𝑝 (5.15)

With y is an arbitrary variable calculated by Aquasim (2.0) and p is the model parameter represented by a constant variable or by a real list variable. The absolute-absolute sensitivity function (Equation 5.9) measures the absolute change in y per unit of change in p. The relative-absolute sensitivity function (Equation 5.10) measures the relative changes in y per unit of change in p. the Absolute-relative sensitivity function (Equation 5.11) measures the

81

absolute change in y for 100% change in

p and the relative-relative sensitivity function

(Equation 5.12) measures the change in y for a 100% change in p. all the changes are calculated in linear approximation only. However, the most useful sensitivity functions are the absolute-relative and relative-relative sensitivity functions because their unit do not depend on the unit of parameter but the absolute-relative sensitivity function is preferred over the relative-relative one because this latter does not give useful results when the value of y becomes small during simulation. For this reason the absolute-relative sensitivity function was used for simulation.

Figure 5.3 illustrates the sensitivity function of sulphur species concentration under the optimized condition with respect to

k m

, K and Y at each process was analysed to compare the effect of each parameter on the oxidation process. It was observed that

k m

, Y and K of the sulphur oxidation step do not affect the sulphide oxidation stage process, it was not shown in the Figure 5.3 (A) but for the first 15 min the model was highly sensitive to

k ma

, Y a

and K

a

before no more sensitivity was recorded. In Figure 5.3 (B) each parameters, no matter the stage, affects the model: for the first 15 min,

Y a

, k ma

and K

a

affect the model whereas the sulphur concentration was highly sensitive to

k mb

, Y b

and K

b

the entire process. In Figure 5.3

(C) sulphate concentration was highly sensitive to

k mb

, Y b

and K

b

but was not sensitive to k

ma

,

Y a

and K

a

. This confirmed the above findings that sulphate is directly converted from sulphur. The above results were on agreement with the one reported by Xu et al., (2013)

82

Figure 5.3 Sensitivity test for the sulphide oxidation and sulphur oxidation processes under optimal conditions (A) sulphide concentration, (B) sulphur concentration and (C) sulphate concentration.

5.4 Chapter Summary

The kinetics of sulphide oxidation in batch culture by L. fusiformis species can be described using the Monod model. The model indicates that the sulphur oxidation stage it is a stable process and yield is produced at a higher concentration at this stage compared to the sulphide oxidation to sulphur phase. Aquasim 2.0 was able to simulate the removal of

83

sulphide under optimal conditions in a batch reactor and it was adequate in modelling the system. These results would allow for the development of a more predictive model and allow for accurate prediction of the overall performance of the reactor.

The Monod model was observed to be an appropriate fit for the data obtained. The obtained parameters were comparable to parameters found in earlier literature. They can be incorporated into reactive transport models used for the design and operation of sulphide remediation systems (as well as sulphide recovery process).

84

CHAPTER 6

CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusion

Six microorganisms,

Micrococcus luteus, Lysinicibacillus fusiformis, Lysinicibacillus sphaericus,

Pseudomonas putida, Fusarium oxysporum and Penicillium simplicissimum were isolated a mining environment to evaluate their effectiveness in removing sulphide and to select the most efficient cultures. The criterion for selection was based on: the culture with the highest sulphide removal rate was selected as the most suitable strain for the rest of the experiment.

Lysinicibacillus fusiformis was regarded as the most efficient not only due to its high rate but the bacterium was observed to be able to remove sulphide in the presence and the absence of carbon source.

Partial oxidation was reported to occur within strict conditions. The effects of pH, redox potential and presence of a carbon source on the oxidation of sulphide to produce elemental sulphur was investigated. It was observed that the pH of the solution did not have an effect on the bacterial ability to remove sulphide. At a pH range of 6-8; the same sulphide removal rate was observed. The redox potential was considered as the main key parameter in this study. It was observed at Eh of around -80mV partial sulphide oxidation does occur and sulphur is produced.

The results obtained from this study highlight the feasibility of elemental sulphur production through partial sulphide oxidation and serve as a foundation for further studies towards the development of cost-effective sulphur recovery procedure from AMD. Batch modelling results showed that partial sulphide oxidation fitted well the Monod kinetic model rate for biological process. The kinetics parameters developed could be used to design a bioreactor for sulphide removal system.

6.2 Recommendations

In order to achieve optimum application for this particular technology it will be needed to find a cheaper and efficient organic matter or evaluate the feasibility and efficiency of the organic matter left during biological sulphate reducing process in order to reduce the cost.

85

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J-S. (2013) Sulphate-reduction, sulphide-oxidation and elemental sulphur bioreduction process: Modelling and experimental validation. Bioresource technology. 147:202-2011

Yao, W. and Millero, F. J. (1996). Oxidation of hydrogen sulphide by hydrous Fe (III) oxides in seawater. Marine Chemistry. 52: 1-16.

Zhao F.J., Lehman J., Solomon D., Fox M.A. and McGrath S.P. (2006) Sulphur speciation and turnover in soils: evidence from sulphur XANES spectroscopy and isotope dilution studies. Soil Biol. Biochem. 38:1000-1007.

Zhao W., Chu W.S., Yang F.F., Yu M.J., Chen D.L. Guo X.Y., Zhou D.W., Shi N., Marcelli

A., Niu L.W., Teng M.K., Gong W.M., Bentaffo M. and Wu Z.Y. (2007) Quantitative investigation of two metallohydrolases by X-ray absorption spectroscopy near-edge spectroscopy. Nucl. Instrum. Meth. Phys. Res. 580:451-456.

103

Appendix A

APPENDIXES

Figure 1: (A) Gram positive bacterial species and (B) Gram negative bacterial species.

(A):

104

(B):

(C):

Figure 2: Phylogenetic tree diagram showing: (A)sample X1 identified as a homology of

Micrococcus yunnanensis and Micrococcus luteus, (B) sample X2 and X3 identified as a homologs of

Lysinicibacillus fusiformis AF169537 and Lysinicibacillus sphaericus AF169465 respectively and (C) sample X4 identified as a homology of Pseudomonas plecoglossicida.

105

APPENDIX B

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Parameter Estimation File

***********************************************************************

Number of parameters = 4

Number of data points = 5

Estimation method = secant

Parameters:

Name Unit Start Minimum Maximum

C_Aini1 mg/L 50 0 1000

K_A mg/L 0.0101684 0.01 1000

u_A 1/minutes 10 0.01 1000

Y_A 3.00279 0.01 10

Calculations:

C_Aini1 K_A u_A Y_A Chi^2

[mg/L] [mg/L] [1/minutes] []

50 0.0101684 10 3.00279 14.7403

60 0.0101684 10 3.00279 9957.72

50 10.0101 10 3.00279 17400.7

50 0.0101684 19.9999 3.00279 15.9488

50 0.0101684 10 3.10269 263.915

50 2.34889 2.98189 2.80299 165819

50 0.170698 29.9998 3.00275 15.9488

55 0.0904333 19.9999 3.00277 27.0108

50 0.01 25.48 3.00177 15.9488

52.5 0.0502166 22.7399 3.00227 18.7143

50 0.01 34.8212 3.00003 15.9488

51.25 0.0301083 28.7805 3.00115 16.6402

50 0.0101614 39.6584 2.99727 15.9488

50.625 0.0201349 34.2195 2.99921 16.1217

50 0.0111273 9.96316 2.99175 14.7423

50.3125 0.0156311 22.0913 2.99548 15.9921

50 0.0120854 9.92632 2.98072 14.7508

50.1563 0.0138582 16.0088 2.9881 15.9596

50 0.0137385 9.85263 2.95864 14.7804

50.0781 0.0137984 12.9307 2.97337 15.9515

50 0.0101684 10 3.00279 14.7403

106

Parameter estimation successfully finished (convergence criterion met)

C_Aini1 K_A u_A Y_A

[mg/L] [mg/L] [1/minutes] []

Estimated values of the parameters:

50 0.0101684 10 3.00279

Standard errors could not be estimated

Contribution of data series to Chi^2:

Calculation: Data Series: Chi^2 ini: Chi^2 end:

fit1 Cmeas_A1 14.7403 14.7403

---------- ----------

14.7403 14.7403

Number of steps performed = 8

Number of simulations performed = 21

107

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Sensitivity Analysis File

***********************************************************************

Ranking of mean absolute sensitivities and error contributions:

Calculation Number: 1

Compartment: Reactor

Zone: Bulk Volume

Variable: C_A

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_Aini1 7.562 C_Aini1 0.7562

2 u_A 3.753 Y_A 0.2499

3 Y_A 3.751 u_A 0.07507

4 K_A 0.001521 K_A 0.02992

5 K_B 1.825e-006 Y_B 4.646e-008

6 Y_B 1.705e-006 u_B 4.849e-010

7 u_B 1.701e-006 K_B 4.913e-011

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

Variable: C_B

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_Aini1 25.91 C_Aini1 2.591

2 u_B 12.96 Y_A 0.3895

3 Y_B 12.94 Y_B 0.3526

4 K_B 12.91 u_A 0.117

5 u_A 5.85 K_A 0.04748

6 Y_A 5.848 u_B 0.003695

7 K_A 0.002414 K_B 0.0003475

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

Variable: C_C

Parameter: Sens AR: Parameter: Error Contr.:

108

[mg/L] [mg/L]

1 C_Aini1 16.53 C_Aini1 1.653

2 u_B 12.96 Y_B 0.3526

3 Y_B 12.94 Y_A 0.1707

4 K_B 12.91 u_A 0.05114

5 Y_A 2.563 K_A 0.0204

6 u_A 2.557 u_B 0.003695

7 K_A 0.001037 K_B 0.0003475

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

109

APPENDIX C

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Parameter Estimation File

***********************************************************************

Number of parameters = 4

Number of data points = 5

Estimation method = secant

Parameters:

Name Unit Start Minimum Maximum

C_Aini1 mg/L 47.429 0 1000

K_B mg/L 8401.28 0.01 100000

u_B 1/minutes 746.107 0.01 10000

Y_B 6.85016 0.01 10

Calculations:

C_Aini1 K_B u_B Y_B Chi^2

[mg/L] [mg/L] [1/minutes] []

47.429 8401.28 746.107 6.85016 13.0447

57.429 8401.28 746.107 6.85016 221.719

47.429 9401.28 746.107 6.85016 22.6472

47.429 8401.28 846.107 6.85016 27.9003

47.429 8401.28 746.107 6.95006 13.1467

47.0539 7868.37 710.173 7.04996 13.1431

45.9859 7521.02 583.533 6.45056 14.0653

47.5337 10401.3 900.187 6.64306 13.0567

47.4294 8220.21 658.934 6.05096 13.5554

47.4275 12401.3 1058.44 6.55146 13.1037

47.4262 16401.3 1369.03 6.25275 14.046

47.4247 24326.6 1991.95 5.66093 24.8564

47.4792 17363.9 1446.07 6.15199 14.8697

47.5294 10344.5 951.222 6.65248 16.5604

47.6298 12459.6 1271.13 6.44545 48.3005

47.8305 8208.94 1911.13 6.67785 1023.32

48.2319 15455.6 1072.62 5.87746 26.7188

48.3751 22509.9 1470.2 5.2667 19.3807

49.3211 33585.8 2030.01 3.90612 24.9269

50.1235 43523.3 3313.91 2.75422 509.266

51.7706 68826.3 5409.65 0.01 4372.51

51.1899 73106 5729.1 0.01 4372.48

38.746 52358.6 4421.22 8.36174 85.4183

110

30.0629 96122.7 7874.8 9.86667 383.731

29.2217 100000 7652.15 10 399.99

36.2479 44585.2 0.01 10 369.14

46.8464 0.01 0.01 7.75457 1481.15

47.3481 0.01 0.01 7.51117 1559.44

47.2004 100000 0.01 0.988563 1546.5

47.1516 0.01 791.352 0.01 4393.01

46.2638 15880 726.23 7.10733 239.908

49.7596 67991.5 138.157 1.27652 1446.72

48.5538 33995.7 69.0835 4.39385 1613.2

51.5147 0.01 0.01 4.38086 2283.95

45.9371 100000 740.451 1.91173 520.042

46.5444 50000 765.902 0.960866 70.4856

46.6408 100000 628.321 0.01 4258.14

50.572 78252.6 0.01 0.01 2123.94

51.3442 70270.9 0.01 0.01 2267.46

46.6288 19620.5 0.01 6.36888 1457.58

42.2983 100000 563.27 4.31954 628.536

44.4696 100000 595.795 2.16477 575.144

37.1675 6285.83 866.95 2.81525 2316.37

51.1362 0.01 494.633 1.68786 4393

34.3258 100000 1091.97 3.86878 126.807

26.8665 78942.5 1437.83 5.01335 343.561

8.29616 78281.8 2129.56 8.02877 2607.52

49.3242 100000 269.323 10 1806.58

50.2302 50000 381.978 5.84393 1605.15

47.2631 100000 644.563 10 1359.76

47.251 0.01 418.382 10 4393.01

43.5436 0.01 0.01 5.94103 1019.2

55.1999 51255 166.829 9.29573 2893.35

37.8436 100000 1280.01 8.49514 295.334

28.2582 74665.6 1702.89 5.20603 297.812

42.4204 76266 1011.27 10 635.717

43.8978 100000 1215.83 10 800.122

47.1477 100000 0.01 7.23539 1538.19

47.6589 54517.3 58.9623 0.55047 1062

47.7319 2052.22 0.01 10 1631.75

47.9459 100000 371.145 10 1545.92

47.1373 0.01 686.323 10 4393.01

47.4576 0.01 806.166 10 4393.01

46.3953 3858.16 342.644 6.86932 15.1691

47.0636 2955.19 171.327 8.43466 245.743

46.9355 25203.8 609.246 7.14761 687.073

46.9996 14079.5 390.286 7.79114 662.604

49.4965 14962.1 1503 7.10779 15.3756

47.7198 0.01 401.958 10 4393.01

47.5887 0.01 604.062 10 4393.01

51.5639 20944.2 584.85 7.53095 1113.64

39.1594 14989.3 441.364 5.73854 97.2955

111

40.3707 26402.3 2259.9 5.37186 262.404

30.8898 8314.6 386.387 3.55227 622.866

37.2399 0.01 449.613 10 4393.01

50.9048 43800.5 3773.68 8.06744 123.423

56.6228 0.01 390.058 10 4392.96

48.4968 13201.2 1324.98 7.68593 14.8691

27.0508 4853.32 425.541 6.38706 966.12

88.1855 6214.63 551.916 7.65105 4260.18

66.2777 52224.9 4669.66 10 1559.16

64.3869 34625.6 3249.54 10 1228.06

43.4079 5675.52 968.904 10 196.925

35.2294 5264.42 697.223 8.19353 539.104

42.2558 10445.6 58.9623 0.55047 27.929

43.1274 96048.5 6965.95 0.305076 4011.92

77.8698 97936.4 8361.67 10 3572.26

71.1283 66281 5805.6 10 2294.73

50.1143 0.01 482.06 10 4392.97

38.9059 10504.3 0.01 0.01 538.132

38.9933 5089.45 0.01 3.2027 559.865

43.3445 10667.6 0.01 0.01 983.223

60.6071 54302 4180.84 5.005 188.744

45.8616 10766.3 0.01 0.01 1316.34

47.2184 15024.9 760.655 7.07793 222.631

48.5554 0.01 200.245 0.01 4393.01

39.26 5630.08 500.003 4.84861 446.285

31.091 13182.3 503.483 5.93865 238.661

38.4763 11974.3 251.747 2.97433 55.1304

14.753 16106.1 617.951 9.02023 1607.31

0 20212.9 528.214 10 4393.01

50.3509 663.83 259.453 0.55047 4175.32

71.4816 0.01 397.229 0.01 4393.01

49.9468 0.01 489.241 2.87745 4393

10.9115 0.01 827.555 10 4393.01

0 8700.33 651.519 10 4393.01

34.626 25203.8 544.705 7.70043 128.853

0 32658.6 406.873 10 4393.01

4.29916 41180.2 67.6393 6.86091 3289.64

47.3393 19481.4 58.9623 0.55047 400.476

49.7077 73959.1 281.041 9.10452 1820.25

49.2701 100000 90.1246 10 1861.07

47.5866 100000 89.0652 10 1579.33

25.9429 70590.1 78.3522 8.43045 277.821

47.6036 0.01 0.01 4.44365 1593.01

47.6326 0.01 0.01 4.48339 1597.84

47.2225 0.01 729.958 10 4393.01

47.8422 6962.25 750.482 7.38969 22.5093

47.7144 53481.1 419.773 8.69484 1350.42

48.2553 6172.74 651.886 7.68569 13.8999

47.9849 29826.9 535.83 8.19027 1047.13

112

47.429 8401.28 746.107 6.85016 13.0447

100 steps performed (not yet converged)

Contribution of data series to Chi^2:

Calculation: Data Series: Chi^2 ini: Chi^2 end:

fit2 Cmeas_B1 13.0447 13.0447

---------- ----------

13.0447 13.0447

Number of steps performed = 100

Number of simulations performed = 122

113

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Sensitivity Analysis File

***********************************************************************

Ranking of mean absolute sensitivities and error contributions:

Calculation Number: 1

Compartment: Reactor

Zone: Bulk Volume

Variable: C_A

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_Aini1 6.807 C_Aini1 0.7176

2 Y_A 3.376 Y_A 0.2248

3 u_A 3.369 u_A 0.06738

4 K_A 0.001441 K_A 0.02834

5 K_B 8.003e-007 Y_B 2.255e-008

6 Y_B 7.724e-007 u_B 2.016e-010

7 u_B 7.52e-007 K_B 1.905e-011

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

Variable: C_B

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_Aini1 24.61 C_Aini1 2.594

2 u_B 12.39 Y_B 0.3611

3 Y_B 12.37 Y_A 0.3528

4 K_B 12.34 u_A 0.1059

5 Y_A 5.298 K_A 0.04519

6 u_A 5.296 u_B 0.003321

7 K_A 0.002297 K_B 0.0002938

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

Variable: C_C

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

114

1 C_Aini1 16.02 C_Aini1 1.688

2 u_B 12.39 Y_B 0.3611

3 Y_B 12.37 Y_A 0.1547

4 K_B 12.34 u_A 0.04645

5 Y_A 2.323 K_A 0.01942

6 u_A 2.322 u_B 0.003321

7 K_A 0.0009875 K_B 0.0002938

8 C_Aini2 0 C_Aini2 0

9 Cmeas_A2 0 Cmeas_A2 0

10 Cmeas_B1 0 Cmeas_B1 0

11 Cmeas_B2 0 Cmeas_B2 0

12 Cmeas_C1 0 Cmeas_C1 0

13 Cmeas_C2 0 Cmeas_C2 0

14 Cmeas_A1 0 Cmeas_A1 0

115

APPENDIX D

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Parameter Estimation File

***********************************************************************

Number of parameters = 4

Number of data points = 10

Estimation method = secant

Parameters:

Name Unit Start Minimum Maximum

C_A1ini mg/L 50.0003 0 1000

K_A mg/L 564.73 0.01 1000

u_A 1/minutes 125.673 0.01 1000

Y_A 0.37786 0.01 10

Calculations:

C_A1ini K_A u_A Y_A Chi^2

[mg/L] [mg/L] [1/minutes] []

50.0003 564.73 125.673 0.37786 37.7288

60.0003 564.73 125.673 0.37786 82.0774

50.0003 574.73 125.673 0.37786 37.747

50.0003 564.73 135.673 0.37786 38.0731

50.0003 564.73 125.673 0.47776 50.9204

50.0003 551.812 145.673 0.414361 38.0853

50.0003 550.015 148.455 0.450861 37.7537

55.0003 557.373 137.064 0.414361 48.7974

50.0003 535.301 112.096 0.338116 37.8988

52.5003 546.337 124.58 0.376239 40.5297

50.0002 623.588 125.644 0.328117 37.8636

51.2503 584.962 125.112 0.352178 38.4806

50.0003 564.73 125.673 0.37786 37.7288

Parameter estimation successfully finished (convergence criterion met)

C_A1ini K_A u_A Y_A

[mg/L] [mg/L] [1/minutes] []

116

Estimated values of the parameters:

50.0003 564.73 125.673 0.37786

Standard errors could not be estimated

Contribution of data series to Chi^2:

Calculation: Data Series: Chi^2 ini: Chi^2 end:

fit1 Cmeas_A1 37.7288 37.7288

---------- ----------

37.7288 37.7288

Number of steps performed = 4

Number of simulations performed = 13

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Sensitivity Analysis File

***********************************************************************

Ranking of mean absolute sensitivities and error contributions:

Calculation Number: 1

Compartment: Reactor

Zone: Bulk Volume

Variable: C_A

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 0.9484 Y_A 0.4687

2 Y_A 0.8855 C_A1ini 0.009484

3 u_A 0.8853 u_A 0.001409

4 K_A 0.8479 K_A 0.0003003

5 Cmeas_A1 0 Cmeas_A1 0

6 C_A2ini 0 C_A2ini 0

7 Cmeas_A2 0 Cmeas_A2 0

8 K_B 0 K_B 0

9 Cmeas_B 0 Cmeas_B 0

10 u_B 0 u_B 0

11 Cmeas_C 0 Cmeas_C 0

12 Y_B 0 Y_B 0

Variable: C_B

117

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 26.59 Y_A 0.7141

2 u_B 14.31 Y_B 0.5609

3 Y_B 14.28 C_A1ini 0.2659

4 K_B 14.24 u_B 0.00581

5 u_A 1.353 u_A 0.002153

6 Y_A 1.349 K_A 0.0004587

7 K_A 1.295 K_B 0.000418

8 Cmeas_B 0 Cmeas_B 0

9 Cmeas_C 0 Cmeas_C 0

10 Cmeas_A1 0 Cmeas_A1 0

11 C_A2ini 0 C_A2ini 0

12 Cmeas_A2 0 Cmeas_A2 0

Variable: C_C

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 22.47 Y_B 0.5609

2 u_B 14.31 Y_A 0.3478

3 Y_B 14.28 C_A1ini 0.2246

4 K_B 14.24 u_B 0.00581

5 u_A 0.6574 u_A 0.001046

6 Y_A 0.6572 K_B 0.000418

7 K_A 0.6295 K_A 0.0002229

8 Cmeas_B 0 Cmeas_B 0

9 Cmeas_C 0 Cmeas_C 0

10 Cmeas_A1 0 Cmeas_A1 0

11 C_A2ini 0 C_A2ini 0

12 Cmeas_A2 0 Cmeas_A2 0

118

APPENDIX E

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Parameter Estimation File

***********************************************************************

Number of parameters = 4

Number of data points = 10

Estimation method = secant

Parameters:

Name Unit Start Minimum Maximum

C_A1ini mg/L 55.5708 0 1000

K_B mg/L 9797.96 0.01 10000

u_B 1/minutes 675.216 0.01 1000

Y_B 10 0.01 100

Calculations:

C_A1ini K_B u_B Y_B Chi^2

[mg/L] [mg/L] [1/minutes] []

55.5708 9797.96 675.216 10 21.6283

65.5708 9797.96 675.216 10 343.806

55.5708 9897.96 675.216 10 21.7612

55.5708 9797.96 685.216 10 21.8888

55.5708 9797.96 675.216 10.9999 32.3837

55.565 9780.18 655.217 9.70409 21.6321

55.5695 9597.96 681.846 10.3009 21.6282

60.5701 9697.96 678.531 10.1505 102.13

55.569 9197.96 695.105 10.9164 21.6481

58.0696 9447.96 686.818 10.5334 41.4486

55.5695 9597.96 681.846 10.3009 21.6282

Parameter estimation successfully finished (convergence criterion met)

C_A1ini K_B u_B Y_B

[mg/L] [mg/L] [1/minutes] []

119

Estimated values of the parameters:

55.5695 9597.96 681.846 10.3009

Standard errors could not be estimated

Contribution of data series to Chi^2:

Calculation: Data Series: Chi^2 ini: Chi^2 end:

fit2 Cmeas_B 21.6283 21.6282

---------- ----------

21.6283 21.6282

Number of steps performed = 3

Number of simulations performed = 11

120

***********************************************************************

AQUASIM Version 2.0 (win/mfc) - Sensitivity Analysis File

***********************************************************************

Ranking of mean absolute sensitivities and error contributions:

Calculation Number: 1

Compartment: Reactor

Zone: Bulk Volume

Variable: C_A

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 3.127 Y_A 0.6943

2 Y_A 3.018 C_A1ini 0.02814

3 u_A 3.018 u_A 0.003672

4 K_A 2.936 K_A 0.0005873

5 Cmeas_A1 0 Cmeas_A1 0

6 C_A2ini 0 C_A2ini 0

7 Cmeas_A2 0 Cmeas_A2 0

8 K_B 0 K_B 0

9 Cmeas_B 0 Cmeas_B 0

10 u_B 0 u_B 0

11 Cmeas_C 0 Cmeas_C 0

12 Y_B 0 Y_B 0

Variable: C_B

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 38.6 Y_A 0.8095

2 u_B 11.22 C_A1ini 0.3474

3 Y_B 11.2 Y_B 0.2175

4 K_B 11.17 u_A 0.004292

5 u_A 3.527 u_B 0.003291

6 Y_A 3.519 K_A 0.0006859

7 K_A 3.429 K_B 0.0002327

8 Cmeas_B 0 Cmeas_B 0

9 Cmeas_C 0 Cmeas_C 0

10 Cmeas_A1 0 Cmeas_A1 0

11 C_A2ini 0 C_A2ini 0

12 Cmeas_A2 0 Cmeas_A2 0

Variable: C_C

Parameter: Sens AR: Parameter: Error Contr.:

[mg/L] [mg/L]

1 C_A1ini 13.84 Y_A 0.3167

2 u_B 11.22 Y_B 0.2175

3 Y_B 11.2 C_A1ini 0.1245

121

4 K_B 11.17 u_B 0.003291

5 u_A 1.378 u_A 0.001677

6 Y_A 1.377 K_A 0.000268

7 K_A 1.34 K_B 0.0002327

8 Cmeas_B 0 Cmeas_B 0

9 Cmeas_C 0 Cmeas_C 0

10 Cmeas_A1 0 Cmeas_A1 0

11 C_A2ini 0 C_A2ini 0

12 Cmeas_A2 0 Cmeas_A2 0

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