Asian Institute of Technology, Thailand The Netherlands

Thesis No.

COMPARING PERFORMANCE OF ACTIVATED SLUDGE SYSTEMS IN

TROPICAL CLIMATES:

A case of the Crowborough Wastewater treatment plant in Harare (Zimbabwe) and the

Tung Kru Wastewater treatment plant in Bangkok (Thailand)

Happymore Mbiza

Comparing Performance of Activated Sludge Systems in Tropical Climates:

A case of the Crowborough Wastewater Treatment Plant in Harare (Zimbabwe) and the Tung

Kru Wastewater Treatment Plant in Bangkok (Thailand)

by

Happymore Mbiza

A thesis submitted in partial fulfillment of the requirements for the Degrees of Master of Science in Urban Water Engineering and Management at the Asian Institute of Technology

and

the degree of Master of Science at UNESCO – IHE

Examination Committee: Prof. Ajit Anachathre (Chairperson) Dr. Tineke Hooijmans (Co-chairperson) (IHE) Dr. Sangam Shrestha Dr. Thammarat Kottenkap Nationality: Zimbabwean Previous Degree: Bachelor of Science (Honors) in Environmental Science and Technology, Chinhoyi University of Technology Chinhoyi, Zimbabwe Scholarship Donor: BMG / UNESCO-IHE/AIT Fellowship

Asian Institute of Technology

School of Engineering and Technology and School of Environment, Resources and Development Thailand

UNESCO-IHE, the Netherlands May 2014

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I would like to appreciate my sponsors Bill and Melinda Gates Foundation especially the project coordinator Prof. Damir Brdjanovic and The Government of Zimbabwe especially the Office of the President and Cabinet for awarding me the opportunity to explore my potential in the academic dome. My heartfelt gratitude goes to my committee chairpersons Prof. Ajit Anachathre (Asian Institute of Technology) and Dr. Tineke Hooijmans (Unesco-IHE) for their inexorable guidance and support during this study. I am thankful to my committee members Dr. Sangam Shrestha and Dr. Thammarat Kottenkap for their support during the course of the research, their contribution cannot be overlooked. Special mention goes to Prof. Innocent Nhapi (Chinhoyi University of Technology) for his guidance on Zimbabwe’s wastewater situational analysis and encouragement since study inception. Engineer Simon Muserere helped in processing all necessary documents required to carry out my study with the Harare city council as well as resources for data collection and analysis. His contribution to this study was vivid. Mr. Teddy Mafuko and the Harare City Council lab team worked relentlessly in assisting with lab experiments and results analysis, special mention goes to Mr. Makaza and Mr. Mutero. I extend special gratitude also to the personnel at Crowborough and Firle Sewage treatment works in Harare (Zimbabwe) for assisting during the plant tours and sampling campaigns, special mention goes to Mr. Nyabonde and Mr. Mukusha. At Thungkru wastewater treatment plant, I would like to appreciate the staff for their kindness and assistance during data collection, special mention goes to Mr. Worawong for his follow up and concern after the data collection. My sincere gratitude also goes to Mr. Roel Noorman of Unesco-IHE Information and Technology desk for his patience with me and the porch of his busy hands to set up remote access to BioWin. Ms Phunsiri Salaya Senior Laboratory Supervisor at AIT worked persistently to facilitate data collection in Bangkok and I would also like to appreciate her efforts in making this study a success. I would like to thank my classmate Charles Gisima for his second eye on my work and assistance in polishing it. Above all, I thank God for allowing me to live through the days and provision of wisdom.

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This research summarizes work that was done towards making recommendations in rehabilitation of the CWWTP in Harare, Zimbabwe. The study focused on modeling activated sludge process in CWWTP (Harare) and TKWWTP (Bangkok) to evaluate treatment efficiencies. Modeling results were then compared and recommendations were made basing on this comparison. BioWin was used for system evaluation following the systematic activated sludge process modeling protocol developed by STOWA and the parameters of concern in the model were COD, TKN and TP. The research showed that the 5 stage Bardenpho at CWWTP has the potential to perform well under normal circumstances. In its non-functional state, the plant was also reasonably likely to contribute to disease outbreaks in Harare. CWWTP did not meet effluent discharge standards in December from 1994 to 2014, a period of (20 years). It was also found that CWWTP had a bigger loading rate than the TKWWTP and that they had different wastewater characteristics. There was evidence for system overdesign and or wrong treatment system selection with special emphasis on erection of a 5 stage Bardenpho plant to treat 2mg/l of phosphorus at CWWTP. Chemical precipitation was recommended for CWWTP just as it was found to be effective in removal of such a TP concentration at TKWWTP. Total system dilapidation at CWWTP as found to be a reason of lack of system monitoring and subsequent failure to react on time to fix identified challenges in the plant therefore automation was suggested. Land based treatment systems like ponds were also suggested. Industry was encouraged to take part in wastewater treatment and privatization of the water sector could reap good service provision. This study also suggested that both TKWWTP and CWWTP look into possibilities of removing personal care products and pharmaceuticals from their wastewater as none of them had put in place measures to remove these micro pollutants. CWWTP was recommended to reduce aeration from 4mg/l to 2mg/l in the BNR plant as concentration increase didn’t increase system efficiency above 2mg/l, also energy generation at the plants was suggested. A revamp was suggested on the national legislation in Thailand concerning a high allowable TKN concentration in effluent discharge to the environment. 100-200mg/l was regarded too high in comparison to EPA standard of 25mg/l. The standard is even lower in other countries like Zimbabwe at about 10mg/l. Finally a technology selection matrix should be created to aide in treatment system selection. Political and socio-economic factors should be included in wastewater treatment issues.

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CHAPTER TITLE PAGE

Acknowledgement ii Abstract iii Table of Contents iv List of Tables v List of Figures vi List of Abbreviations vii

1 INTRODUCTION 1

1.1 Background 1 1.2 Problem description 1 1.3 Present state of research 1 1.4 Open questions to be addressed by the research 2

1.5 Objectives 3 1.6 Scope of research 3

2 LITERATURE REVIEW 4

2.1 Introduction 4 2.2 Performance comparison 4 2.3 Tropical climates 4 2.4 The role of modeling in modern wastewater treatment 5 2.5 Activated sludge system 10 2.6 Wastewater characterisation 14

3 STUDY AREA

3.1 Background to the study area 194 STUDY METHODOLOGY 26

4.1 Assessment of plants 27 4.2 Sampling and analysis 33

4.3 Reasons CWWTP is not meeting standards 36 4.4 Modeling (STOWA protocol) 375 RESULTS AND DISCUSSIONS

5.1 Kinetic parameters used 50 5.2 BioWin Results 51 5.3 Results comparison 56 5.4 Lessons learnt from TKWWTP recommended for CWWTP 58 5.5 Further work and future applications 596 CONCLUSIONS AND RECOMMENDATIONS 60

6.1 Conclusion 60 6.2 Recommendations 60 References 61

Appendices 66

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TABLE TITLE PAGE

1 Matrix presentation of Model D 6

2 Stoichiometric matrix for activated sludge modeling 7

3 Unit sizes at CWWTP 22

4 Unit sizes at TKWWTP 25

5 Summarized sample analysis methods 34

6 Sampling results CWWTP 35

7 Sampling results for TKWWTP 36

8 CWWTP COD fractions as shown in BioWin 43

9 TKWWTP COD fractions as shown in BioWin 44

10 Summarized kinetic parameters used in the BioWin models. 50

11 CWWTP efficiency 51

12 TKWWTP efficiency 54

13 Modeling results comparison 56

14 Fractions adjusted during model calibration 57

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FIGURE TITLE PAGE

1 Five Stage Bardenpho Process/ Modified Bardenpho Process 12

2 Vertical Loop Reactor 133 Map showing location of the CWWTP 204 CWWTP process flow diagram 215 Schematic representation of the 5 stage Bardenpho process 226 Map showing location of the TKWWTP 237 Process Flow Diagram for TKWWTP 248 Schematic representation of the topic, objectives & methodology 269 CWWTP COD for 20 years 2710 CWWTP Ammonia for 20 years 2811 CWWTP TP for 20 years 2912 TKWWTP COD for 5 years 3013 TKWWTP Ammonia for 5 years 3114 TKWWTP TP for 5 years 3215 The STOWA protocol for modeling of activated sludge plants 3816 Process flow diagram fort CWWTP 3917 Process flow diagram for TKWWTP 3918 Model structure (Meijer et al., 2001) 4019 BioWin unit configuration CWWTP 4120 BioWin unit configuration TKWWTP 4221 Characterization of the main flows 4322 Relationship between influent COD load and sludge production 4623 Fitting model kinetics simulating diffusion limitation 4724 Validation procedure 4825 CWWTP COD BioWin chart 5126 CWWTP TP BioWin chart 5227 CWWTP TKN BioWin chart 5328 TKWWTP COD BioWin chart 5429 TKWWTP TKN BioWin chart 5530 TKWWTP TP BioWin chart 56

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LLIISSTT OOFF AABBBBRREEVVIIAATTIIOONNSS AANNDD AACCRROONNYYMMSS

ADM Anaerobib Digestion Model AS Activated Sludge ASM Activated Sludge Model BNR Biologival Nutrient Removal BOD Biochemical Oxygen Demand CBD Central Business District COD Chemical Oxygen Demand CODX Particulate Chemical Oxygen Demand CSE Correct Sampling Error CWWTP Crowborough Wastewater Treatment Plant DO Dissolved Oxygen EMA Environmental Management Agency EPA Environmental Protection Agency F/M Food to Mass Ratio IPIP Individual Performance Improvement Plan ISE Incorrect Sampling Error ISS Inorganic Suspended Solids IWA International Water Association LGAs Local Government Authorities MATLAB Matrix Laboratory MLSS Mixed Liquor Suspended Solids

NH3 Ammonia

PAOs Phosphorus Accummulating

PCC Pollution Control Committee

PE Population Equivalent

pH Potential Hydrogen

PL Person Load

Ppm Parts per million

PST Primary Settling Tank

RAS Return Activated Sludge

SCADA Supervisory Control And Data Acquisition

SI Soluble Inert

SS Suspended Solids

SRT Sludge Retention Time

STOWA Acronym for Foundation for Applied Water Research

TA Total Alkalinity

TN Total Nitrogen TKN Total Kjeldahl Nitrogen

TKWWTP ThungKru Wasten Water Treatment Plant

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TOS Theory of Sampling

TP Total Phosphate TPX Particulate Total Phosphate

TSS Total Suspended Solids TKWWTP Tung Kru Waste Water Treatment Plant

VFAs Volatile Fatty Acids VSS Volatile Suspended Solids WAS Waste Activated Sludge WWTP Wastewater Treatment Plant

X Particulate

XOCs Xenobiotic Organic Compounds

XI Particulate inert

XS Biodegradable Particulate

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1.1 BACKGROUND

1.1.1 General

In many cases the performance of activated sludge processes in regions with a warm climate has been less than satisfactory, especially when these are designed for secondary treatment only. This can be attributed partially to the lack of financial means for proper operation, but in many cases the problem is mainly due to the fact that inadequate design criteria are used. Often these criteria are adaptations from those developed in regions with a colder climate, where the vast majority of the activated sludge processes have been constructed. However, the difference in temperature has such an important influence on the activated sludge behavior, that some of the design criteria developed in regions with a temperate climate have only a limited applicability in the tropics and subtropics. A clear example concerns the process of nitrification. Many Biological Nitrogen Removal (BNR) activated sludge processes in warm climates are conservatively designed; because little systematic investigation has been carried out on the BNR activated sludge process in warm or tropical climates, although many studies under temperate climate conditions are available (Cao, Wah et al. 2008)

1.2 PROBLEM DESCRIPTION

Challenges faced by Harare in wastewater treatment are a myriad of unexplainable tribulations. Putting them together in a single report can be confusing or misleading. This study therefore focused on a single facet in the bigger predicament of wastewater treatment in Harare. The aspect of choice in this study was malfunctionality of the (CWWTP) Crowborough Waste Water Treatment Plant which is contributing to water pollution in Lake Chivero. The wastewater treatment plant crushed to a total halt and therefore discharges untreated wastewater into Lake Chivero which is the source for water supply in Harare. This problem can lead to more staid challenges in the whole urban water cycle and community health. An extreme example of such a challenge is the seasonal outbreak of cholera and other water related diseases. Furthermore, there is high proliferation of the water hyacinth in the receiving waters. All these are signs of a malfunctioning wastewater treatment system. If wastewater is not being treated, certainly the water supply is also grossly affected and costly; this has a direct negative impact on economic performance of water utilities and the health of the society.

1.3 PRESENT STATE OF RESEARCH

No research has been done in Zimbabwe or Thailand on comparing Activated Sludge systems performance in tropical climates. R. Mazhandu and Z. Hoko carried out a research on Characterization of wastewater at selected sewage plants in Harare. This study was carried out from March to May 2009 to assess the treatability of sewage at Crowborough and Marlborough wastewater plants in Harare which are more than 20 years old. Toxicity was

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found to be a potential problem for Crowborough. The researchers suggested that stringent effluent requirements should be made for industries that produce effluent with toxic elements (Mazhandu and Hoko 2009).

A research by P. Taru in 2005 focused on assessing the present and future contributions of wastewater discharges from Harare to the nitrogen and phosphorus loading of Lake Chivero and, based on this assessment formulated feasible sanitary engineering solutions to ensure sustainable use of the resources in the lake. This research was done at Crowborough wastewater treatment plant. They concluded that influent parameter ratios were within the ranges that allow effective phosphorus removal in activated sludge systems (Taru, Mlambo et al.).

Tanaka in 1997 looked at Effects of thermo chemical pretreatment on the anaerobic digestion of waste activated sludge in Thailand. (Tanaka, Kobayashi et al. 1997). Nhapi in 2009 looked at the water situation in Harare, Zimbabwe.(Nhapi 2009) The study focused more on the policy and management problems faced by the city. In this study, the researcher highlighted that the then current problems had been caused by rapid population growth after independence in 1980, inadequate rehabilitation and maintenance of water and wastewater treatment plants, expensive technologies (trickling filters and especially biological nutrient removal systems) and a poor institutional framework. The researcher mentioned that the last upgrade of sewage treatment plants was in 1996 and since then the major plants were treating 100,000m3/d for Crowborough (design capacity 54,000m3/d) and Firle 180,000m3/d (design capacity 144,000m3/d). Overally he mentioned that wastewater problems in Harare were because political patronage was taking precedence over technical competence (Nhapi 2009). Nhapi 2004 looked at options for wastewater management in Harare. In this research, he highlighted the severe pollution of the receiving waters and suggested that source control by users (pollution prevention/reduction and reuse), better management of wastewater treatment, and efforts to control direct and indirect river discharges upstream and downstream of wastewater treatment plants. He also suggested onsite, decentralized, and centralized management of wastewater with appropriate regulations and institutions for each case (Nhapi 2004)

1.4 OPEN QUESTIONS TO BE ADDRESSED BY THE RESEARCH

i. What are the conditions of the CWWTP and the TKWWTP wastewater treatment plants?

ii. Which sources contribute to the influent streams of the two wastewater treatment plants?

iii. What are the differences in operation between the CWWTP and the TKWWTP? iv. Are the performances of the 2 activated sludge systems different, in what manner

and how are the differences being justified? v. What can be learnt from the TKWWTP to improve performance of the CWWTP

and Vise versa? vi. What options are available for rehabilitation and performance improvement of both

activated sludge systems?

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1.5 OBJECTIVES

1.5.1 General Objective

To make recommendations for improving system performance at CWWTP by comparing it to the TKWWTP through evaluating the activated sludge system performance in both the plants by mathematical modeling, comparing evaluation results of the two systems, identification of gaps and associated problems and recommending appropriate solutions for improving plant performance.

1.5.2 Specific Objectives

i. To assess performance of the CWWTP and the TKWWTP and identify existing problems in operation.

ii. To evaluate performance of the activated sludge systems in both plants using steady state mathematical models. Evaluation will be based on Carbon, nitrogen, and phosphorus mass balances.

iii. To compare modeling results of performance of the activated sludge system at the CWWTP against the system performance at the TKWWPT.

iv. To recommend appropriate solutions for improving the performance of the activated sludge systems and sustainability measures that can be adopted to maintain acceptable performance with reference to national standards in both Thailand and Zimbabwe.

1.6 SCOPE OF RESEARCH

The research was done in Harare, the capital city of Zimbabwe and Bangkok the capital city of Thailand. These two capital cities are located in tropical climates with Harare falling under the tropical savanna grasslands and Bangkok falling under tropical monsoon climate. The research focused on evaluating activated sludge systems performance in the two selected wastewater treatment plants in Asia and Africa with the aim of making recommendations towards rehabilitation the CWWTP at the same time improving the performance of the TKWWTP. Data was collected from the Crowborough Wastewater Treatment Plant which is an activated sludge wastewater treatment plant located near the high-density suburb of Mufakose in Harare, Zimbabwe and the Tung Kru Waste Water Treatment Plant which is an activated sludge wastewater treatment plant located in Bangkok, Thailand. Evaluation of the performances of the two activated sludge systems for decision making was based on steady state mathematical modeling by use of BioWin modeling Software. The modeling process followed the STOWA protocol and both primary and secondary data were used. Modeling focused on activated sludge not plant wide modeling. Sampling was done in the influent and effluent streams of the CWWTP and the TKWWTP. The sampling was done through the composite method and the analysis of samples was done according to standard procedures. Recommendations were made according to modeling results.

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2.1 Introduction

Wastewater generation and its subsequent treatment is a major problem for every industry and for the society as well. Prior to treatment, the wastewaters need to be monitored so as to permit their discharge into the local water resources. (Rastgi, Rathee et al. 2003). Wastewater treatment today probably is more focused on removing phosphorus and nitrogen than pathogens since these elements contribute to eutrophication and deterioration of our natural water ecosystems. (Pell and Wörman 2008). In Harare wastewater is collected to semi-centralized wastewater treatment plants using conventional sewerage infrastructure. Combined sewers are not permitted in Zimbabwe (Nhapi, Siebel et al. 2006) meaning that storm water drains directly into streams, rivers, and reservoirs in the proximity of the city or town. Some industries discharge partially treated or untreated wastewater into storm drains leading to the direct pollution of streams and reservoirs with industrial effluent. With increasing centralization of wastewater treatment facilities and continued growth of population centers, even the effluent from well-performing facilities causes environmental risks (Siebel and Gijzen 2002).

2.2 Performance comparison

System performance comparison has become a key tool in the water industry to promote and achieve performance targets for utilities. The use of this tool for performance improvement through systematic search and adaptation of leading practices has expanded globally during the past decade. (Cabrera Jr 2010). Benchmarking can be defined as a measurement of the quality of an organization's policies, products, programs, strategies, etc., and their comparison with standard measurements, or similar measurements of its peers. The objectives of benchmarking are to determine what and where improvements are called for, to analyze how other organizations achieve their high performance levels, and to use this information to improve performance. Construction and rehabilitation of wastewater treatment plants has been so far a challenge for most developing and transitional countries. Socio-economic resources, political stability and will, institutional strength and capacity as well as cultural background are important elements defining the trajectory of pollution control in many countries. Technological aspects are sometimes mentioned as being one of the reasons hindering further developments. However, there is a wide list of technological options for the treatment of wastewater even in the so called developing countries. (Von Sperling, de Lemos Chernicharo et al. 2005)

2.3 Tropical Climates

Most developing and under-developed countries are located in tropical climates. These countries struggle with their economies in most cases and yet they have established (AS) Activated Sludge systems which are treatment systems that call for more stable economies. This wastewater treatment system requires high investment in capital and skill as well as in Operation and Maintenance. Due to lack of investment in research and development, most

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lifetime projects like wastewater treatment plants in these developing countries were established without adequate baseline studies to determine proper sustainable system selection. Most projects were established out of adoption of successful systems established elsewhere especially in developed countries without customization to fit local conditions. Most projects have crumbled or increased in cost over time due to unsuitability pertaining to treatment system selection.

The term tropical has a rather specific meaning when applied to the scientific sense of the word. An area with tropical climate is one with an average temperature of above 18 degrees Celsius (64 degrees Fahrenheit) and considerable precipitation during at least part of the year. These areas are none arid and are generally consistent with equatorial climate conditions around the world. Three very large areas conform to the definition of a tropical climate. These are the Amazon Basin in Brazil, the Congo Basin in West Africa and much to all of Indonesia. Other, less commonly known areas that are actually tropical include the savannas of Africa and semiarid areas throughout the world. Southeast Asia and Central America are two of the most well-known tropical areas, by comparison.

Temperature has a great impact on the process of metabolism and thereby affects the rate of metabolism for COD and nitrogenous compounds. Generally, the rate of reaction should increase with an increase in temperature and the rule of thumb in most chemical reactions is that for every 10 degrees Celsius increase in temperature, the rate of reaction doubles. In biological reactions however this will be defiled by the presence of enzymes. Temperature increase can increase directly the rate of reaction only up to a certain level of temperature as the enzymes will start to undergo the process of denaturing, this will then reduce the rate of reaction.{de Lemos Chernicharo, 2005}

2.4 THE ROLE OF MODELING IN MORDEN WASTEWATER TREATMENT

2.4.1 Why modeling?

The most important advantages of the use of models in wastewater treatment are:

i. Getting insight into plant performance ii. Evaluating possible scenarios for upgrading

iii. Evaluating new plant design iv. Supporting management decisions v. Developing new control schemes

vi. Providing operator training

2.4.2 What is a model?

A model can be defined as a purposeful representation or description (often simplified) of a system of interest {Ekama, 1999}. This consequently means that the model will never exactly reflect the reality. So, the question ‘does this model describe a wastewater treatment plant?’ is meaningless, unless it defines what part(s) of the treatment plant the model should describe in the first place. One never develops a model that describes every single organism, every

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molecule of water or every detail of the process. Models are used as a simplification of reality in such a way that they describe that part of reality that is relevant to understand and to deal with. It is also important to note that a mathematical model can only be successful if it fulfils people’s expectations. There are two aspects that are extremely relevant in modeling: the aspect of time and that of scale. In general, processes can be separated into three groups from the perspective of time. Processes can be in a so-called frozen state, dynamic state, steady state or equilibrium. Models are usually made to describe the dynamic state, the state in which variations occur as a function of time. When a process is in a frozen state it means that the process will change over time, but not in the time interval that one is interested in.

Table 1: Matrix presentation of Model D

 

Source: (Henze et al., 1987)

2.4.3 Stoichiometry

From the system definition one takes only those compounds of the system that are considered important and/or make a significant part of the total system mass (being at least a small percentage of it). For example, in the case of nitrification the nitrite concentration will remain very low or close to the detection limit in most plants, so from the perspective of mass balance there is no need to take nitrite into account. In anaerobic digestion, similarly, there is no need to take hydrogen into account, as the hydrogen content of gas is very low, as almost everything ends up as methane. Such intermediates will only be specified if considered important, for example, when there is nitrite or hydrogen accumulation. Nitrite is not included in the nitrification process in ASM1, while in the Anaerobic Digestion Model (ADM1, Batstone et al., 2000) hydrogen is included as it plays an important role in the stability of the anaerobic system. ASM models are specifically designed for applications at lower temperatures (5 to 20°C), under which no significant accumulation of nitrite is expected to take place. Nitrite will only accumulate at higher temperatures or in the case of unusual toxic events. Thus nitrite is left out of the model. {Batstone, 2001} For each conserved balance the number of atoms of a compound entering the plant is equal to those leaving. Examples of conserved balances are nitrogen, phosphorus, COD or alkalinity conversion. Using balance equations, unknown stoichiometry coefficients can be calculated.

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This substantially reduces the information required for modeling as the approach allows a number of unknown values to be calculated. The use of BOD measurement as the characteristic of wastewater is declining and, instead, modern approaches rely on COD. BOD-based design is associated with a black-box approach, which cannot be used for balancing as it is not conserved, and depends on many factors (e.g. reaction time, temperature).{Batstone, 2001} In reality, it is still mostly used to link the output of ASM regarding the effluent impact on the receiving waters (where BOD is still a relevant indicator of water quality). In contrast, the COD balance is conserved because COD is by definition the amount of electrons which are transferred to oxygen in order to oxidize all the organic matter in the system to CO2 and water. That is why the modeling is based on COD rather than on BOD. Stoichiometry can be determined based on relevant compounds involved in the reaction and use of balances to calculate those relevant coefficients. For example, in the reaction for heterotrophic growth, the relevant compounds are organic matter, oxygen, ammonia, alkalinity, biomass, carbon dioxide and water.  Table 2: Stoichiometric matrix for activated sludge modeling

 Source: (Henze et al., 1987)

2.4.4 Kinetics

Each reaction has its own rate equation. The rate equation specifies the rate of conversion of the compound with the stoichiometric yield coefficient of 1. The conversion rate of the other compounds follows from multiplying each yield coefficient with the rate equation. The model can be based either on substrate-based kinetics (the substrate stoichiometric coefficient is equal to 1) or growth-based kinetics (the biomass stoichiometric coefficient is equal to 1). In ASM1 the rates are described based on the growth rate; the biomass coefficient is therefore set as 1. In ASM, a saturation equation is used as the standard rate equation. Saturation (Monod) kinetics includes two main parameters, the maximum rate parameter and affinity or

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saturation constant (K value, defined as the concentration at half the maximum rate). The saturation term S/(K+S) can have a value between 0 and 1, and can have a different function in the model. Several affinity terms reflect a real value, e.g. the oxygen affinity term is an observed parameter. However, in some cases the saturation term is only a switching term. For example, a switching function is used in the model to stop the growth process when there is no ammonia present. The affinity constant for ammonia is effectively very low and hardly measurable, so the sole purpose of the coefficient placed in the equation is to guarantee there is no growth anymore if the ammonia is fully consumed. This consequently means that one does not need to calibrate this value. How to distinguish between real measurable parameters and switching functions is a bit vague and inexplicit in activated sludge modeling. Therefore it is important to realize whether the K values are there as real model parameters or as a switching function to stop the process when the relevant compound is not present.

To describe inhibition kinetics, a similar approach is applied, but now the affinity constant is called the inhibition constant, and consequently it is possible to define an inhibition term which again has a value between 0 and 1. The inhibition constant is equal to the substrate concentration at which a 50% decrease in the rate is observed. There are also much more complex inhibition terms, but in ASM this is the usually applied term, especially for the substrate inhibition.

It is important to note that multiplying so many factors causes deviation because these factors are never exactly 1. If one multiplies the two factors with the value of 0.9 with the value of the third factor that is 0.5, the result will be 0.4, while the real value should be 0.5 because this is the limiting factor. This consequently means a 20% lower rate value. Therefore it is better to use a logical operator in the model and choose the minimum factor among the terms instead of multiplying these factors as it seems that it gives a better approximation of the reality.

The reason that this equation is used is partly an inherited habit (at the time of early model development in the 1970s, computing logical operators by integral differential equations was difficult and extremely time-consuming and thus not applied). It does not matter so much which equation is used for activated sludge modeling; the point is to understand the reasons for the choices at different stages of the model development.

2.4.5 Simulator environments

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A wastewater treatment simulator can be described as software that allows the modeler to simulate a wastewater treatment plant configuration. General purpose simulators can be distinguished from specific wastewater treatment simulators. General purpose simulators normally have a high flexibility in their application, but the modeller has to supply (by programming) the models that are to be used to model a specific plant configuration. The latter task can be very time consuming although necessary, as it is needed to debug the implemented model, to avoid running lots of simulations with a model that afterwards is shown to be erroneous for the specific task. As a consequence, general purpose simulators require a skilled user (with programming skills) who fully understands the implications of each line of code in the models. A popular example of a general purpose simulator is MATLAB/SIMULINK (developed by www.mathworks.com). In contrast to general purpose simulators, specific wastewater treatment simulators usually contain an extended library of predefined process unit models, for example a perfectly mixed ASM1 or ASM2d bioreactor, and a 1-dimensional 10-layer settler model. The process configuration to be simulated can easily be constructed by connecting process unit blocks. Pop-up windows allow the modification of the model parameters. Examples of specific commercial wastewater treatment simulators:

i. AQUASIM (www.aquasim.eawag.ch) ii. BioWin (www.envirosim.com)

iii. EFOR (www.dhisoftware.com/efor) iv. GPS-X (www.hydromantis.com) v. SIMBA (www.ifak-system.com)

vi. STOAT (www.wrcplc.co.uk/software) vii. WEST (www.hemmis.com)

2.4.6 BioWin WWTP simulation software

This software is a commercial package developed by Envirosim Ltd. (at www.envirosim.com). BioWin is state-of-the-art and an especially user-friendly (intuitively operated), MS Windows-based simulation software package founded on generally accepted modeling principles according to internationally-accepted IAWQ model standards. The BioWin calculation model integrates the activated sludge models ASM1, ASM2d and ASM3 and combines these with an anaerobic digestion model (ADM) and several other (specialized) models. Moreover, BioWin includes several sub-models for different process units including primary sedimentation, sludge dewatering, biofilm systems, membrane systems, pH calculation, lime dosage and (metal) precipitation reactions (and more). {Gernaey, 2004}

Of all the previously presented dedicated wastewater treatment software packages, BioWin is the only software that has been developed specifically to be used by engineers rather than by scientists. Therefore, the BioWin software uses a unique interface which translates the activated sludge model results into practical operational measurements and vice versa. This means the modeller (in this case the wastewater treatment engineer) can use actual measured data directly as input parameters (e.g. TSS, COD. BOD5, TN, TP) and BioWin will accept this data and translate it to ASM model parameters (e.g. XI, XS, SS, SI, SF, etc.).

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In this process, the modeller will not be experiencing the calculations in the background of the activated sludge model. Therefore, BioWin makes it possible to use an activated sludge model without having extensive knowledge of activated sludge modeling. For this reason, the BioWin simulator, made by Envirosim Company, is the most widely used software package by operators and process engineers (while other packages may be used more often by scientists or control engineers). The BioWin simulator software has been selected because it is widely used by engineers, designers but also by academics and researchers. It has all the features necessary for modeling endeavor in this research. The BioWin simulator includes an exceptionally quick and stable (user friendly) steady-state model solver and thereby provides all the required options for both steady-state and dynamic simulations. BioWin arguably has the most user-friendly interface of all the commercial packages and has been developed for relatively inexperienced users and under practical conditions. {Gernaey, 2004} The BioWin simulator suite presently includes two modules:

i. A steady state module for analyzing systems based on constant influent loading and/or flow weighted averages of time-varying inputs. This unit is also very useful for mass balancing over complex plant configurations. (Used in this research).

ii. An interactive dynamic simulator where the user can operate and manipulate the treatment system "on the fly". This module is ideal for training and for analyzing system response when subjected to time-varying inputs or changes in operating strategy.

2.5 ACTIVATED SLUDGE SYSTEM

Activated Sludge is a multi-chamber reactor unit that makes use of (mostly) aerobic microorganisms to degrade organics in wastewater and to produce a high-quality effluent. In the biological process of sewage purification, one takes advantage of the property of bacteria to reduce dissolved or colloidal organic compounds (Roediger 1983).To maintain aerobic conditions and to the keep the active biomass suspended, a constant and well-timed supply of oxygen is required. Different configurations of the activated sludge process can be employed to ensure that the wastewater is mixed and aerated (with either air or pure oxygen) in an aeration tank. The microorganisms oxidize the organic carbon in the wastewater to produce new cells, carbon dioxide and water. Although aerobic bacteria are the most common organisms, aerobic, anaerobic, and/or nitrifying bacteria along with higher organisms can be present. The exact composition depends on the reactor design, environment, and wastewater characteristics. The effectiveness of an activated sludge process is subject to a good solid–liquid separation, which is strongly determined by the activated sludge settling properties. In turn, the settling properties depend mainly on the flocs’ structural properties and on the activated sludge microbial population. (Govoreanu, Seghers et al. 2003)

During aeration and mixing, the bacteria form small clusters, or flocs. When the aeration stops, the mixture is transferred to a secondary clarifier where the flocs are allowed to settle out and the effluent moves on for further treatment or discharge. The sludge is then recycled back to the aeration tank, where the process is repeated. With more stringent standards imposed regarding nutrient removal, treatment processes have been developed to remove

11

compounds containing nitrogen and phosphorous. The result of removing greater concentrations of phosphorus nitrogen and magnesium from the wastewater is struvite formation. (Doyle and Parsons 2002).

To achieve specific effluent goals for BOD, nitrogen and phosphorus, different adaptations and modifications have been made to the basic activated sludge design. Aerobic conditions, nutrient-specific organisms (especially for phosphorus), recycle design and carbon dosing, among others; have successfully allowed activated sludge processes to achieve high treatment efficiencies. Before discharging wastewater into water bodies, removing phosphate is usually obligatory, even though in many cases it is not performed, and leads to major contamination on a worldwide level. (de-Bashan and Bashan 2004). Biological phosphorus removal is a complex process if compared to Nitrogen and COD removal, much different interference with the other processes might occur.(Brdjanovic, van Loosdrecht et al. 2000). Today, the main commercial processes for removing phosphorus from wastewater effluents are chemical precipitation with iron, alum, or lime (Donnert and Salecker 1999) and to a lesser extent, biological removal.(de-Bashan and Bashan 2004).

Nitrogen removal from domestic wastewater is widely performed by nitrification coupled to heterotrophic denitrification. However, the organic carbon to Nitrogen ratio (C/N) in the wastewater is frequently too low to achieve proper Nitrogen removal efficiencies and external organic Carbon sources are often necessary to support the process and meet the discharge standards for total Nitrogen (TN), increasing the process cost. The main cost, however, remains the high energy demand to provide the aeration necessary for nitrification. (Ruscalleda Beylier, Balaguer et al. 2011). With more stringent standards imposed regarding nutrient removal, treatment processes have been developed to remove compounds containing nitrogen and phosphorous. The result of removing greater concentrations of phosphorus nitrogen and magnesium from the wastewater is struvite formation.

2.5.1 Five-Stage Bardenpho Process/ Modified Bardenpho Process

The Bardenpho Process of Wastewater Treatment was developed by James Barnard of South Africa in the 1970’s. The Modified Bardenpho Process, sometimes referred to as a “5-Stage Bardenpho Process”, is different from the basic Bardenpho process for nitrogen removal because an anaerobic zone is added at the influent end of the process. This anaerobic zone, in conjunction with the anoxic and aerobic zones that are part of the basic Bardenpho process, allows the process to achieve phosphorus removal. {Cooper, 1994}

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Figure 1: Five Stage Bardenpho Process/ Modified Bardenpho Process

This is a biological nutrient removal process which goes through five stages. Anaerobic digestion of wastewater occurs in the anaerobic treatment tank of the plant. Anaerobic sludge is used to obtain broth for fermentation. The fermentation broth is then sent through alternating anaerobic-aerobic-anoxic cycles in batch reactors. When under anaerobic conditions, phosphorous is secreted from the microbes that accumulate phosphorous (PAO). When the fermentation broth is then subjected to aerobic conditions, the phosphorous is taken up by the accumulating microbes. Nitrifying bacteria oxidize the ammonia nitrogen in this stage. When the final anoxic tank is filled with this broth, the oxidized nitrogen is converted to nitrogen gas by the bacteria. {Nicholls, 1987}

i. Nitrogen Removal

Nitrogen in the original wastewater is mainly in the form of ammonia and this ammonia passes through the first two zones without any change. It is only in the third aerobic zone that the sludge has aged sufficiently for complete nitrification to take place and that the ammonia nitrogen gets converted to nitrates and nitrites. When this reaches the anoxic zone, because of the absence of dissolved oxygen, the nitrates are converted by the bacteria to nitrogen gas by using the organic carbon compounds as donors for hydrogen. This nitrogen escapes to the atmosphere. The effluent is then subjected to aeration in the final zone which raises the dissolved oxygen levels and prevents further denitrification.

ii. Phosphorous Removal

This is achieved through a step feed process in which wastewater influent is treated in at least one aerobic zone. This is again processed through at least one anoxic zone. A portion of the effluent from the anoxic zone is then sent to an anaerobic zone along with raw water. Influent from one anaerobic zone is sent to an anoxic zone and then sent to a downstream aerobic zone.

iii. Advantages of the Bardenpho Process

As no chemicals are used, operating costs are lower and there is also no problem with removal of sludge that can come from sludge containing chemicals. Bardenpho Process

13

plants are simple to operate and do not require any retraining of personnel. The sludge that is obtained in the final stages does not require any further treatment and can be easily disposed of.

iv. Disadvantages of the Bardenpho Process

One of the main disadvantages of the Bardenpho process is the number of tanks required, which greatly increases capital cost. Detention times also need to be very strictly monitored and constant evaluation made of the BOD and COD values.

2.5.2 The Vertical Loop Reactor

i. Background

The Vertical Loop Reactor is an alternative for the conventional oxidation ditch. The Vertical Loop Reactor (VLR) is a series of reactors in rectangular tanks, with disc aerators primarily for mixing and coarse bubble diffusers for the supply of oxygen. The under-over flow pattern allows an oxidation ditch system to be installed in deeper tankage, reducing land area requirements significantly. {Smith, 2003}

ii. Description and working principle

The Vertical Loop Reactor (VLR) is an oxidation ditch flipped over on its side, using a horizontal baffle to divide the tank into upper and lower compartments.

Figure 2:Vertical Loop Reactor

Source: (http://www.stowa selectedtechnologies.nl/Sheets/Sheets/Vertical.Loop.Reactor.htm); Retrieved on 8 April 2014.

The difference between the VLR and the conventional oxidation ditch is that the loop is made in the VLR in the vertical plane instead of the horizontal. From the top, the basin looks like conventional rectangular plug flow tanks. But from the side, the VLR looks like an oxidation

14

ditch, complete with rounded end walls and a center partition divider. In the case of the VLR this divider is a horizontal baffle extending across the full width of the tank and most of the length. The baffle divides the tank into upper and lower aeration compartments: disc aerators are situated on top while coarse bubble diffusers are placed in the lower compartment for additional oxygen delivery. The air delivered in the lower compartment does not follow the same pathway of conventional aeration tanks. The diffusers are normally located in the first third of the lower compartment; the released bubbles are guided by the channel flow, the horizontal baffle not allowing any to escape to the surface until the end of the tank has been reached. An air release section allows air exiting from the lower compartment to be divided over the width of the upper section before reaching the surface.

iii. Design guidelines / Technical data

Aeration and mixing of the basin mixed liquor is achieved by discs mounted at the surface of the water level of the upper compartment. These discs are made on non-corroding plastic with a diameter of 54” and a thickness of 0.5”. The discs are made in halves with features for mounting on the horizontal shaft. The discs support hundred of half-pyramid shaped projections on either side of the disc which allow oxygen to be varied. The disc operates optimally between 12” and 21” of immersion and speed of 40 to 60 rpm. The coarse bubble diffusers located in the lower compartment of the VLR provide supplemental oxygen delivery. Air bubbles are in contact with the liquid several times longer in the VLR than in the conventional aeration tank. Since the typical VLR has a tank length longer than 100’ retention time of the air bubbles is likely to be between 50 to 100 seconds. Most VLRs are designed for liquid depths greater than 6 metres. The horizontal baffle is located about mid-depth so that both upper and lower compartments are about 3 metres deep.

2.6 WASTEWATER CHARACTERISATION

Because of changing wastewater characteristics and the imposition of stricter limits on wastewater discharges and biosolids that are used beneficially, greater emphasis is being placed on wastewater characterization. Because process modeling is widely used in the design and optimization of biological treatment processes (e.g., activated sludge), thorough characterization of wastewater, particularly wastewaters containing industrial waste, is increasingly important. Process modeling for activated sludge as it is currently conceived requires experimental assessment of kinetic and stoichiometric constants. Fractionization of organic nitrogen, chemical oxygen demand (COD), and total organic carbon into soluble and particulate constituents is now used to optimize the performance of both existing and proposed new biological treatment plants designed to achieve nutrient removal. (Metcalf&Eddy 2002)

2.6.1 The origins of wastewater

The safe disposal of both solid and liquid waste is of ever increasing concern due to the growing scientific and public awareness about the impact of untreated wastes on both human and environmental health. (Jones and Freeman 2003). The production of waste from human activities is unavoidable. A significant part of this will end up as wastewater. The quantity and quality of wastewater is determined by many factors; not all humans or industries produce the

15

same kind and amount of waste. The wastewater produced in households is influenced by behavior, lifestyle, and standard of living of the inhabitants as well as the technical and juridical framework by which people are surrounded. In households most waste will end up as solid and liquid waste, and there are significant possibilities for changing the amounts and composition of the two waste streams generated. For industry, similar considerations apply. The design of the sewer system affects the wastewater composition significantly. Most developing countries use separate sewer systems. Old urban areas might have combined systems. (M. Henze 2008)

2.6.3 BOD and COD

Natural organic detritus and organic waste from waste water treatment plants, failing septic systems, and agricultural and urban runoff, acts as a food source for water-borne bacteria. Bacteria decompose these organic materials using dissolved oxygen, thus reducing the DO present for fish. Biochemical oxygen demand (Saul, DJORDJEVIC et al.) is a measure of the amount of oxygen that bacteria will consume while decomposing organic matter under aerobic conditions. Biochemical oxygen demand (Saul, DJORDJEVIC et al.) is one of the most widely used and important parameters in the measurement of biodegradable organic compounds and pollutants in water. (Kwok, Dong et al. 2005). Biochemical oxygen demand is determined by incubating a sealed sample of water for five days and measuring the loss of oxygen from the beginning to the end of the test. Samples often must be diluted prior to incubation or the bacteria will deplete all of the oxygen in the bottle before the test is complete. The BOD test has its widest application in measuring waste loading to treatment plants and in evaluating the BOD removal efficiency of such treatment systems. (Liu and Mattiasson 2002). The main focus of wastewater treatment plants is to reduce the BOD in the effluent discharged to natural waters. Wastewater treatment plants are designed to function as bacteria farms, where bacteria are fed oxygen and organic waste. The excess bacteria grown in the system are removed as sludge, and this “solid” waste is then disposed of on land (Brdjanovic 2008).

Chemical oxygen demand (COD) does not differentiate between biologically available and inert organic matter, and it is a measure of the total quantity of oxygen required to oxidize all organic material into carbon dioxide and water. COD values are always greater than BOD values, but COD measurements can be made in a few hours while BOD measurements take five days. If effluent with high BOD levels is discharged into a stream or river, it will accelerate bacterial growth in the river and consume the oxygen levels in the river. The oxygen may diminish to levels that are lethal for most fish and many aquatic insects. As the river re-aerates due to atmospheric mixing and as algal photosynthesis adds oxygen to the water, the oxygen levels will slowly increase downstream. The drop and rise in DO levels downstream from a source of BOD is called the DO sag curve (Selin and Chevez 1995).

2.6.4 Load

The wastewater from inhabitants is always expressed in the unit population equivalent (PE). PE can be expressed in water volume or BOD. The two definitions used worldwide are: 1PE=0.2m3/d and 1 PE=60gBOD/d. These two definitions are based on fixed non-changeable

16

values. The actual contribution of a person living in a catchment, so called Person Load (Rughoonundun, Mohee et al.) can vary considerably. The reasons for variation can be working place outside a catchment, socio economic factors, lifestyle or household installations etc. Person Equivalent PE and Person Load PL are both based on average contributions and used to give an impression of the loading of wastewater treatment processes. They should not be calculated from data based on short time intervals i.e. hours or days. They also vary from region to region or country to country. (Henze 2011)

2.6.5 Microorganisms

Understanding the pathway of pathogens in the environment is very important for the protection of human health. One of the sources of pathogens is wastewater that is discharged without or with treatment or recycled for many purposes. (Meric and Fatta Kassinos 2009). The microorganisms in wastewater come mainly from human excreta as well as from the food industry. The incidence of infection in the community is important, the more people that are infected the more pathogens are released into the sewage. Thus in developing countries with greater rates of enteric diseases, greater concentrations of pathogens would be expected in sewage. Other factors can be the socio economic status of the population, time of the year and per capita water consumption. For all these reasons the concentrations of enteric pathogens are much greater in sewage in developing world than in developed world (Brdjanovic 2008).

2.6.6 Special wastewater and internal plant recycle streams

The bigger the plant, the more internal wastewater recycles and external inputs have to be handled. If the catchment area has decentralized wastewater handling, septic tank sludge will be loaded into the plant by trucks. This is common practice in most developing countries. Often septic tank sludge creates problems in biological treatment due to sudden overloading of the plant from the truck. For treatment plants of over 100,000 person equivalent the unloading from a few trucks into the plant will not cause a problem. A small quantity can already cause problems in smaller treatment plants, the load from the truck can be unloaded into a storage tank at the plant from which it can be loaded into the plant during periods of low plant loading like at night.(Henze 2008). (Asolekar and Gopichandran 2005) Another external load to the plant can be leachate from a landfill. This leachate can be transported or pumped to a central treatment plant. However it is normally just dumped into the sewer next to a landfill. Internal loading at a treatment plant is caused by thickening and digester supernatant, reject water from sludge dewatering and filter washwater. Digester supernatant is often a significant internal load especially concerning ammonia. This can lead to overload of nitrogen in the case of biological nitrogen removal. Reject water from sludge dewatering can have higher concentrations of soluble material, both organics and nitrogen. Filter wash can cause problems due to high hydraulic overload of the settling tanks in treatment plants. In some cases it can result in overload with suspended solids. Filter wash water in smaller treatment plants should be recycled slowly. (Henze 2011)

2.6.7 Ratios

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For all microbial processes, the right balance of nutrients is essential. (Rughoonundun, Mohee et al. 2012).The ratio between various components in wastewater has significant influence on the selection and functioning of wastewater treatment processes. Wastewater with a low carbon to nitrogen ration might require external carbon source addition in order that biological denitrification functions fast and efficiently. Wastewater with high nitrate concentration or low volatile fatty acids (VFAs) will not be suitable for biological phosphorus removal. Wastewater with high COD to BOD ratio indicates that a substantial part of organic matter will be difficult to degrade biologically. When suspended solids in wastewater have a high volatile component (VSS to SS ratio) these can be successfully digested under anaerobic conditions. Even in subject to dilution, these ratios are not affected. Ratios can be used in detection of anomalies in analysis which can be discharges into the sewer system, often from industries or due to analytical errors (Henze 2011).

2.6.8 Variations

The concentrations of substances in wastewater vary with time. In many cases daily variations are observed, in some weekly and others are very likely a function of industrial production matters. The variations are important for design operation and control of the treatment plant. Sampling of wastewater is challenging due to the variations in flow and component concentrations. Analytical results will vary considerably with the chosen sampling method. Floating materials such as oils and grease are difficult to sample and so are comparatively heavier components such as sand and grit. A number of sampling techniques are applied to wastewater and they are as follows: grab samples, time proportional samples, flow proportional sampling, 24 hour variations and weekly sampling.

2.6.9 Wastewater flows

Wastewater flows vary in time and space. This makes them difficult to measure. The forecasting of wastewater flowrates can help to reduce overflows and the operational costs of wastewater pumping stations and treatment plants. (Tan, Berger et al. 1991). The basic unit for flow is volume of wastewater per unit of time (m3/d). The design flow for different units in a plant varies. For units with a short hydraulic residence time like screens and grit chambers, the design flow is given in m3/sec. Settling tanks design flow is normally given in m3/h. Average daily flow is calculated as wastewater flow per year divided by 365. Average hourly flow is daily flow divided by 24. Watch out for flows of return water (such as supernatant) which are often mixed into the raw wastewater before reaching bar screen and grit chamber, hence making correct measurement on the raw wastewater difficult. (Henze 2002).

2.6.10 Traditional wastewater from households

Professionals working in the field of urban wastewater are often confronted with the disappointing fact that carefully researched measures do not lead to the desired results. The reason for this can often be attributed to unforeseeable social, economical and legal changes. (Dominguez and Gujer 2006). These vary from country to country and variations are induced by climate, socio economic factors, households and other factors. The composition of grey

18

wastewater depends on sources and installations from where the water is drawn, e.g. kitchen, bathroom or laundry. The chemical compounds present originate from household chemicals, cooking, washing and the piping. In general grey wastewater contains lower levels of organic matter and nutrients compared to ordinary wastewater, since urine, faeces and toilet paper are not included. The levels of heavy metals are however in the same concentration range. The information regarding the content of Xenobiotic organic compounds (XOCs) is limited. (Eriksson, Auffarth et al. 2002). Urine is the main contributor to nutrients in household wastewater thus separating it will greatly reduce nitrogen content in the wastewater stream to a level where nitrogen removal is not needed. Kitchen waste contains a significant amount of organic matter which traditionally ends up in the wastewater. Wastewater from laundry and bath carries a minor pollution load as only part of it comes from household chemicals, the use of which can affect the composition of the load of this wastewater fraction. This water can be used together with the waste from the kitchen for irrigation. After a considerable treatment, it can be used for toilet flushing. (Brdjanovic 2008).

2.6.11 Wastewater fingerprint

Wastewater from a particular person gives a detailed picture of that person and the lifestyle. All human activities are registered and reflected in the wastewater, from the food we eat to materials we use in our houses and the materials and the production applied in industry. Through wastewater, one can get information on sex, illness, drugs, pregnancy, personal hygiene, diet, environmental consciousness, alcoholism etc. The fingerprint we reflect with the wastewater affects the environment. It is not the wastewater that spoils the environment; it is people that pollute the water.(Comeau 2011).

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CCHHAAPPTTEERR 33

SSTTUUDDYY AARREEAA

3.1 Background to the study area

This study was carried out in two tropical climatic regions in Africa and South East Asia. The first was the tropical savanna grasslands climate of Africa and secondly the tropical monsoon climate of South East Asia. Two major cities in these climates were explored. Harare (Zimbabwe) falls under the tropical savanna grasslands climate and Bangkok (Thailand) falls under the tropical monsoon climate. The study compared the efficiency of activated sludge systems in these two climatic regions with the aim of improving the activated sludge system in Harare. The CWWTP in Harare was compared to the TKWWTP in Bangkok.

3.1.1 Harare (Zimbabwe)

Harare is the capital city of Zimbabwe. It is an urban agglomeration with a population of about 2.3 million people (Tsiko and Togarepi 2012). It is located on a high plateau (1400 m): some parts are hilly, some flatter. The climate is subtropical highland. There is extensive industrial activity including steel, chemicals and textiles. There is also significant urban agricultural activity and notably a widespread use of flood-prone peri-urban sites for maize and horticultural crops.

A large proportion of the population lives in low-income settlements, including informal settlements. Sanitation provision in Harare is grossly deficient, as in most cities in sub-Saharan Africa: most people do not have access to a hygienic toilet; large amounts of fecal waste are discharged to the environment without adequate treatment; this is likely to have major impacts on infectious disease burden and quality of life (Njoh and Akiwumi 2011).There are seasonal cholera outbreaks that ravage the city mostly after the rain season. The major wastewater treatment system in Harare is activated sludge (Nhapi, Siebel et al. 2006) and it relies on power availability for functionality. With a crippled economy, Harare struggles to continually provide the required power to the plants and neither can it afford operation and maintenance of these activated sludge plants.

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Figure 3: Map showing location of the CWWTP

3.1.2 CWWTP

The CWWTP was not working at the time of the study and this study aimed at making recommendations for rehabilitation of the plant based on evaluation of the treatment system installed at the plant. The city of Harare has 6 sewage treatment works including Crowborough Wastewater Treatment Plant (CWWTP) (Nhapi et al., 2001). This plant is located near the high-density suburb of Mufakose. Effluent from this sewage treatment plant is disposed into Marimba River that eventually drains into Lake Chivero the main source of drinking water for the city. Below is a Process Flow Diagram (PFD) for the 5 Stage BNR plant.

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Figure 4: CWWTP process flow diagram

CWWTP consists of a set of trickling filters that have a combined design capacity of 36 000 m3/day and a 5-stage BNR plant that has a design capacity of 18 000 m3/day. This study focused on the 5 stage BNR plant. The CWWTP receives sewage from the surrounding high-density suburbs of Budiriro, Kuwadzana, Dzivarasekwa and Kambuzuma. Industrial effluent from the Workington and Willowvale industrial areas is also treated at the CWWTP. The average daily influent to the whole plant is 110 000m3/day. The BNR plant is fed to a maximum of its design capacity of 18 000 m3/day to avoid overloading it. The rest of the influent is directed to the set of trickling filters.

i. Operations of the Activated Sludge Plant at CWWTP

The settled sewage from the set of primary settling tanks (PSTs) is directed to the activated sludge plant at an average flow of 10 000m3/day to 18000m3/day depending on the volume of influent to the plant. The design sludge retention time of the plant is 15 to 25 days with a mixed liquor suspended solids (MLSS) concentration of between 4000 and 6000 mg/l in the bioreactor. The MLSS concentration is maintained at this range by sludge recycle from the clarifiers as well as sludge wastage. The three sludge recycle pumps are centrifugal pumps, which use a float switch system as a control to recycle the activated sludge from the clarifiers. The number of clarifiers working governs the volume of influent that can be directed to the activated sludge plant to avoid overloading the clarifiers. Three mixed liquor return pumps ensure internal recirculation of the MLSS. Sludge wasting also depends on the concentration of the MLSS in the reactor. If the concentration is below the 4000 mg/l wasting is totally suspended to allow build up of solids. The plant uses tapered aeration in which 14 aerators of different power rating are used to ensure the appropriate dissolved oxygen is supplied to the different zones of the basin. These aerators are switched on and off depending on the desired oxygen levels. Below is a schematic representation of the BNR plant at the CWWTP

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Figure 5: Schematic representation of the 5 stage Bardenpho process at the plant

The arrows in Fig 3.2 show the flow direction of wastewater in the bioreactor. The influent to the 5-stage BNR plant is settled sewage from the PSTs. Return sludge comes from the underflow of the clarifiers to maintain active sludge in the bioreactor by recycling mixed liquor suspended solids. From the aeration basin part of the flow is directed to the anoxic2 basin through the stages of aeration that are tapered towards the anoxic zones. The other part of effluent from the aeration zone is recycled to the anoxic 1 basin where nitrified effluent is mixed with phosphorus rich effluent from the anaerobic zone as well as effluent rich in the carbon source and denitrification occurs in this zone. Effluent from the anoxic zone 2 passes through the reaeration basin to the clarifiers where solid-liquid separations mainly occur. Below is a representation of the basin volumes of the plant.

Table 3: Unit sizes at CWWTP

Unit Process Units # Dimensions (m) Volume (m3)

Length Width Height Diameter Fermentation 39.6 28.5 3.25 1062

Anoxic 1 1 45.1 14.3 3.05 1967

Anoxic 2 1 45.1 18.7 2.95 2488

Aeration basin 1 69.3 52 2.85 10270

Reaeration Basin 1 45.1 17.6 2.85 2262

(total basin Volume) 1 114.4 52 3.25 19334

PST 6 13 13

Clarifier 3 8 26 2123

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3.1.3 Bangkok (Thailand)

Wastewater management services are currently provided by the local government authorities (LGAs), although agencies of the Central Government play the dominant role in sector planning and funding. Under-investment in environmental infrastructure is clearly evident in the municipal wastewater management sector. Only a small proportion of total urban wastewater generation presently receives any treatment. To date there are 95 municipal wastewater treatment plants (WWTPs) with a combined treatment capacity of about 3.0 million m3/day, equivalent to about 21% of the total wastewater generated by the population throughout the country (Simachaya 2009).

Figure 6: Map showing location of the TKWWTP

Bangkok is the capital and the most populous city of Thailand. It has a tropical wet and dry climate under the Koppen climate classification and is under the influence of the South Asian monsoon system. It experiences hot, rainy and cool seasons, although temperatures are fairly hot year-round, ranging from an average low of 20.8 °C in December to an average high of 34.9 °C in April. Bangkok is situated in the Chao Phraya River delta in Thailand's central plains. The river meanders through the city in a southward direction, emptying into the Gulf of Thailand approximately 25 kilometres south of the city centre. The area is flat and low-lying, with an average elevation of 1.5 metres above sea level. Most of the area was originally swampland, which was gradually drained and irrigated for agriculture via the construction of canals which took place throughout the sixteenth to nineteenth centuries. The course of the

24

river as it flows through Bangkok has been modified by the construction of several shortcut canals.

3.1.4 TKWWTP

The TKWWTP was working at the time of the study and this study aimed at making recommendations for rehabilitation of the CWWTP using the TKWWTP as a reference point based on evaluation of the treatment systems installed at the plants. Both systems are activated sludge operating in two tropical climates. The plant is located in the greater metropolitan area of the Thungkru district of Bangkok. The TKWWTP saves a total population of about 1,080,000 inhabitants in the greater metropolitan area. Treatment Capacity of the plant is 65,000(m3/d) and has a network of pipes covering a distance of about 26(km). The treatment technology used at the plant is Activated Sludge with Vertical Loop Reactor.

Figure 7: Process Flow Diagram for TKWWTP

i. Operation of the vertical loop aeration at TKWWTP

The wastewater treatment system is a closed indoor system with a lot of control and automation. This automation is controlled by the Scada system. Wastewater treatment happens at an average daily temperature of about 28 degrees Celsius. Wastewater enters the treatment plant via an installed flow equalization tank of 16m in height. The maximum design capacity of the plant is 65 000m3/d. A daily average flow of 58 000m3/d goes through to the screening where it is also tested by an automatic sampler. This sampling determines the concentrations of parameters in the wastewater. In the case of a low concentration in such parameters like COD, this would impede system functionality in MLSS concentration so the sampler allows for such monitoring and thus calls for corrective measures to be taken. There

25

is addition of COD or TS for controlling concentration in MLSS. There is an elevated sludge hopper installed to collect the screenings and grit on the first floor of the building. There is polymerization of primary sludge from the grit chamber. Sluice gates are used in the batch transfer of wastewater from one unit to the other.

There are 4 aeration tanks in the system arranged systematically. Arrangement is as follows, Anoxic, aeration, aeration and aeration. After the grit chamber, there is chemical precipitation of phosphorus by use of iron chloride. Two tanks containing iron chloride are installed before the aeration tank. The first tank is anoxic and unaerated, followed by an aerated tank with minimal oxygen concentration of 1 mg/l these two first tanks target removal of nitrogen. The third tank has an aeration of 1mg/l as well. Oxygen concentration increases to 2mg/l in the fourth and last aeration tank and these last two tanks target removal of COD. In all the aeration tanks, oxygen concentration is measured at a depth of 2m by installed DO measuring meters. The TKWWTP vertical loop system has a WAS of 1120m3/d as well as RAS of 43550m3/d. There are six rectangular clarifiers installed and a post aeration tank that increases oxygen concentration in the treated wastewater with an aeration of 5mg/l in the post aeration tank. After the post aeration tank, the oxygen concentration in the effluent wastewater reaches up to 7mg/l and discharged into the Bangchak canal.

Table 4: Unit sizes at TKWWTP

Unit Depth (m) Width (m) Length (m) T1 8.500 9.2 34 T2 8.500 9.2 34 T3 8.500 9.2 34 T4 8.500 9.2 34

Post aeration 3 2 80

Clarifier 1 5 13 36 Clarifier 2 5 13 36 Clarifier 3 5 13 36 Clarifier 4 5 13 36 Clarifier 5 5 13 36 Clarifier 6 5 13 36

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CCHHAAPPTTEERR 44

MMEETTHHOODDOOLLOOGGYY

Figure 8: Schematic representation of the topic, objectives and expected activities to achieve the objectives

27

4.1 Assessment of plants

4.1.1 Crowborough

i. History of plant operation

The history for operation of the CWWTP was collected for a period of 20 consecutive years starting 1994 to 2014. Plant performance assessment was done in the month of December during this study therefore also the data collected for history was for the performance of the plant in the month of December for each year. Plant records were used as the source of information for history evaluation. Parameters considered in this study were COD, NH3 and TP in both the influent and effluent streams of the wastewater treatment plant.

a. COD

Figure 9: CWWTP COD for 20 years

For the period 1994 to 2004, CWWTP reached a maximum COD concentration of 1800mg/l in 1997 in the influent. The least concentration was about 280mg/l in 1998. For the 20year period considered in this research, the plant never met the COD discharge standard of 60mg/l in the month of December.

1800

285

480

98,6600

100200300400500600700800900

10001100120013001400150016001700180019002000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

mg/l

Time

CWWTP COD for 20 years

Influent Effluent standard

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b. Ammonia

Figure 10: CWWTP Ammonia for 20 years

Like COD, the plant did not meet the standard for ammonia discharge into the environment in the 20 year study duration period. A highest concentration of ammonia was 50mg/l in the year 1994 and lowest was 8mg/l in the year 1998 for the influent stream. The effluent had as high as 26mg/l of ammonia in the year 1994 and also a minimum of about 3mg/l in the year 1998.

49,5

7,5

26

2,80,50

2468

101214161820222426283032343638404244464850

1994

1995

1996

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1998

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2009

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mg/l

Time

CWWTP ammonia for 20years

Influent Effluent standard

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c. Total phosphorus

Figure 11: CWWTP TP for 20 years

A maximum concentration of TP in the plant was reached in 1997 with a record of 7mg/l and a minimum of about 2mg/l in the year 2013. Effluent was highest in 1995 at 5mg/l and lowest in 1997 at 1mg/l. There were times when the influent concentration was lower than the effluent concentration like in the year 2007. Influent was 3mg/l and effluent was almost 4mg/l. This could be because during that time there was a high TP concentration in the RAS yet there was poor aeration to allow for phosphate uptake by PAOs. This could result in influent concentration being lower than the effluent as concentration is increased in the fermentation tank by both recycle and release by PAOs. This could be a sign of extreme poor aeration in the system as the system cannot allow the released phosphate to be absorbed by the microbes.

4.1.2 Thungkru

i. History of plant operation

For the TKWWTP history of plant operation considered was for 5 years for the period 2010 to 2014. The TKWWTP had a more reliable system of sampling and analysis as the plant is automated. Monitoring and operation is done by the SCADA system and sampling is automatic for both influent and Effluent.

7,3

2

4,9

10,5

00,51

1,52

2,53

3,54

4,55

5,56

6,57

7,58

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

mg/l

Time

CWWTP TP for 20 years

Influent Effluent standard

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a. COD

Figure 12: TKWWTP COD for 5 years

Generally, the wastewater going to the TKWWTP was weak. There was a maximum COD concentration of about 134mg/l and a minimum of about 69mg/l in the influent stream. Effluent was maximum in 2012 at 31mg/l and lowest in 2011 at 13mg/l. The plant met its standard of 60mg/l all the time considered in this study.

68,45

134,23

13.16

30.68

60

0102030405060708090

100110120130140

2010 2011 2012 2013 2014

mg/l

Time

COD TKWWTP

COD inf COD eff standard

31

b. Ammonia

Figure 13: TKWWTP Ammonia for 5 years

Influent ammonia had a slight fluctuation in the both the effluent and influent during this period. 7mg/l was the average influent concentration and about 0.3mg/l for the effluent stream. The study period, the plant met the national standard of discharge of ammonia which is 1mg/l.

7,21

6,59

0,430,21

1

0

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1

1,5

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7,5

8

2010 2010,5 2011 2011,5 2012 2012,5 2013 2013,5 2014

mg/l

Time

NH3 TKWWTP

NH3 inf NH3 eff standard

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c. Total Phosphorus

Figure 14: TKWWTP TP for 5 years

Phosphorus had an interesting observation also for TKWWTP just as in the CWWTP. In this case, the plant received concentration less than 2mg/l, the standard for discharge of TP into the environment. The plant received a concentration higher than 2mg/l only from 2010 to 2012. Generally the observation is that there was a low P concentration in the wastewater generated and conveyed to the plant for the period 2010 to 2014. The lowest effluent concentration was 0.3mg/l in the year 2013 and a maximum of 1mg/l in the year 2012

2,24

1,23

1,02

0,32

2

00,20,40,60,81

1,21,41,61,82

2,22,42,62,83

2010 2010,5 2011 2011,5 2012 2012,5 2013 2013,5 2014

mg/l

Time

TP TKWWTP

TP inf TP eff standard

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4.2 Sampling and analysis

Sampling and analysis was done to get an insight into plants performance and thus secure data for use in modeling. A sampling and analysis campaign was carried out at the CWWTP. From the TKWWTP secondary data was collected in form of concentrations of parameters of concern like COD, TKN and TP in both the influent and the effluent. A plant inventory was taken at the TKWWTP even if sampling was not done. The sampling and analysis methodology described herein therefore only refers to the CWWTP. Any reliable approach to practical, representative sampling must be based on the Theory of Sampling (TOS), as concerns both stationary lots as well as process sampling. (Minkkinen and Esbensen 2009). Sampling was based on the requirements of the STOWA protocol which was used again in modeling both the activated sludge systems in these two plants. After taking a plant inventory at CWWTP, constructing the process flow diagrams (PDF) and defining the area of the plant to be studied, the measurement campaign was developed. In general this campaign focused on acquiring the following information:

i. Flows and operational data ii. Influent characterization measurements

iii. Mass balance measurements iv. Activated sludge characterization measurements v. Effluent characterization

A tabulated data capture sheet as presented in appendix six was used for sampling responsibility and lab analysis. 24-hour composite samples were collected from the influent of the CWWTP; 1920ml of wastewater were collected per day during the 24hr period. Each hour 80ml were extracted into a 2000ml holding plastic container storing the daily sample, each time a sample was extracted, the withdrawing apparatus were rinsed 3 times with the wastewater withdrawn from the extraction point. Samples were kept under ice in a cooler box before being transported to the Harare City council laboratories for analysis. Unfiltered (total analysis) was done at the lab. Before the campaign was launched, all information was communicated to the treatment plant and lab staff responsible for acquiring and analyzing the samples and they were inducted on how to log into the responsibility form provided in the appendices. A process flow diagram was developed indicating the precise measurement point related to the plan. During the first two days, the collection of samples was done together with the responsible staff at the plant so as to monitor the process and assist accordingly. Occasional checking was also carried out throughout the sampling period. While designing the sampling campaign the following practical issues were addressed as a minimum:

i. The purpose of taking the samples ii. The parameters of concern in the study (COD, TKN and TP)

iii. Sampling location iv. Time to sample v. Frequency for sampling

vi. How will the sampling be executed? vii. Who will take the samples and who will analyze them?

viii. What will be used for collecting, storing and analyzing the samples?

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For TKWWTP, sampling was done by automatic samplers installed at the plant. The samplers collect automatically with 24-hour continuous (or quasi-continuous) flow proportional composite measurement.

4.2.1 Duration of Campaign period

The duration of the measurement campaign necessary for the development of a properly working model depends on the accuracy of the required results (linked to a purpose for modeling. In general 1 to 3 days of sampling is considered sufficient for standard situations, whereas for the development of models used in optimization studies and for control strategies, a longer measuring campaign is advised (3 to 7 days and at least 7 days, respectively) {Henze, 1999}. A period of 14 days was considered sufficient in this study. Sampling was done from 5 to 18 December 2013 for the CWWTP.

4.2.2 Sample analysis

Samples collected from the CWWTP were analysed according to the methods tabulated below at the Harare City council laboratories. These analytical methods are further described in the appendices.

Table 5: Summarized sample analysis methods

Parameter Method of analysis

COD Closed reflux Standard Methods 5220.

Ammonia Ammonia- Selective Electrode Method (Standard Method 4500-NH3 F).

TP Vanadate molybdate- phosphoric acid method (Standard Methods 4500)

TKN Micro–Kjeldahl method (Standard Methods 4500-Nitrogen (organic))

Total Alkalinity Titration method

DO Analysed on site using a probe meter (Oximeter 340-A/SET –WTW)

PH Analysed online using the pH meter inoLab 7110.

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Table 6: Sampling results CWWTP

4.2.3 View of sampling results CWWTP

i. 457mg/l is generally medium COD concentration ii. High fraction of biodegradable COD as shown by ration of BOD/COD

iii. 221/457mg/l shows that there could be more Domestic than industrial WW iv. The wastewater has a pH of 7 v. NH3 is low at 25mg/l needing less aeration for nitrification & denitrification

vi. There is also a low TP concentration at 2mg/l which can be precipitated vii. The 5 stage Bardenpho could be an overdesign for 2mg/l phosphate wastewater

viii. VSS is higher than ISS signifying a rich biomass in the wastewater ix. Day 11 when ISS > VSS this could show a poisonous industrial discharge x. C to N is high meaning there would be no need for external source of C

xi. Average concentration wastewater that can be treated by A S

Sample pH TA (ppm)

NH3 (Mg/L)

TP (Mg/L)

BOD (Mg/L)

COD (Mg/L)

TSS (Mg/L)

ISS (mg/L)

VSS (Mg/L)

1 7.0 138 25 5.0 390 572 321 122 199 2 7.0 134 23 1.0 60 959 176 50 126 3 7.0 140 25 2.0 310 320 166 62 104 4 7.0 136 24 3.0 110 310 93 49 44 5 7.0 156 27 2.0 130 687 96 60 36 6 7.0 136 26 2.0 190 422 104 50 54 7 7.0 138 30 1.0 100 480 89 46 43 8 7.0 134 24 0.3 130 475 125 51 74 9 7.0 148 24 0.4 170 126 314 121 193 10 7.0 142 25 2.0 310 359 219 71 148 11 7.0 140 24 0.2 370 349 100 166 34 12 7.0 138 26 2.0 200 586 206 108 98 13 7.0 134 24 3.0 350 326 291 127 164 14 7.0 128 21 0.4 270 435 182 65 117

Average 7.0 139 25 2.0 221 457 177 82 102 Stdv 0.2 7 2.0 1.0 111 201 84 39 58

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Table 7: Sampling results for TKWWTP

Description Influent Effluent Flow (m3/d) 55175 COD(mg/l) 120 26 Ammonia(mg/l) 6.9 0.3 TKN(mg/l) 10 0.7 TP(mg/l) 1.7 0.7 TSS(mg/l) 74 5.8 BOD(mg/l) 38 4.2 pH 7.4 7.5 Temperature (Deg Celsius) 28 26 Dissolved Oxygen(mg/l) 0 7.2 Alkalinity (ppm) 126 129

i. 120mg/l is a low COD concentration ii. Low fraction of biodegradable COD as shown by ration of BOD/COD

iii. 38/120mg/l shows that there could higher non biodegradables iv. The wastewater has a pH of an average 7 which is medium v. TKN is low at 10mg/l needing less aeration for nitrification & denitrification

vi. There is also a low TP concentration at 2mg/l which is precipitated vii. The iron chloride precipitation of P is a noble decision

viii. 5.8 TSS in effluent might need an improvement maybe more clarification ix. 7.2 mg/l oxygen effluent concentration is a good performance indicator x. C to N is high meaning there would be no need for external source of C

xi. Generally weak wastewater which can be treated by A. S

4.3 Reasons CWWTP is not meeting standards

i. There was failure to carry out a comprehensive analysis of the area as well as wastewater characterisation before establishment of the wastewater treatment system installed. This led to installation of activated sludge system in a community with constrains in economy and inadequate training for operation and maintenance. The plant was over designed e.g. a concentration of 2mg/l of phosphorus does not require a 5 stage Bardenpho process. This consumes a lot of energy which the country does not have so there were times of intermittent power cuts which affected the aeration and mixing in the biological unit.

ii. Energy shortages also affected the biological removal of impurities like phosphorus,

nitrogen and carbon as there was no aeration. Failure to aerate led to inability of PAOs to uptake phosphorus in the aeration tanks meaning that the phosphorus had to be released into the marimba river through the effluent stream from the clarifiers. There was no nitrification taking place in the absence of oxygen so ammonia accumulated in the stream as well. Failure to aerate also led to complications in the

37

biological unit like development of anaerobic conditions in the whole plant. Land based treatment systems were supposed to be installed instead of activated sludge.

iii. There is lack of investment in wastewater treatment in the whole country and the

region. Attention is given to wastewater in a form of reaction to problems. There is no or less proactive approach focusing on avoidance. Industry has not shown interest in investing in wastewater as there are other competing needs like food provision and health care. Detection of problems in operation of the plant was difficult and corrective action slow until total system failure.

iv. Gross incapacitation has ruined many sectors of the country, water sector included.

Zimbabwe has been in an economic catastrophe for more than a decade and this led to an exodus of both capable people and capable companies. Skilled and knowledgeable people have left the country leading to compromises in recruitment of unskilled labour as the economy is not appreciating education by better remuneration. Most of the available labour is not skilled and or educated enough to understand complicated systems like a 5 stage Bardenpho.

4.4 Modeling (STOWA protocol)

The STOWA protocol is a systematic guide for modeling wastewater treatment plants that was developed based on the experience of a large and diverse group of wastewater and modeling practitioners in the Netherlands. It proposes a uniform protocol for static and dynamic modeling of activated sludge systems. This protocol is a guideline for improving the quality and controllability of the simulation studies for activated sludge processes. This approach has been tested on various occasions and has become standard in good modeling practice. {Hulsbeek, 2002}In this research, modeling was used to evaluate the performance of activated sludge systems in the CWWTP and the TKWWTP for comparison purposes with the aim of improving performance of the CWWTP. The STOWA methodology was chosen for use in the systematic process of modeling. The methodology presented here does not cover all aspects of plant-wide modeling. In this research only the activated sludge part of the wastewater treatment plants was covered as it suited the objectives of this study. Modeling aimed at explaining and evaluating operation of the activated sludge systems.

In general, plant-wide modeling is more difficult because in the plant-wide model the error of one model (e.g. primary settling) is passed on to the other models (e.g. the activated sludge model and to the primary sludge). This can make the (precise) calibration of plant-wide models very difficult. If the design questions are purely related to the activated sludge plant like in the case with this study where focus was on only evaluating the activated sludge part of the whole plant, it is therefore not advisable to use the plant-wide model. In that case, it was scientifically sound to focus on description of the activated sludge system and measure all the incoming and outgoing flows as well as parameters of concern in these flows. The STOWA protocol consists of a series of diagrams, some of which are in the form of decision charts (algorithms). The charts have a distinct order of approach; the topics are handled stepwise according to the diagram below.

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Figure 15: The main structure of the STOWA protocol for modeling of activated sludge plants

4.4.1 Modeling objective

In this study, the modeling objective was to describe and evaluate the performance of the activated sludge systems in CWWTP and the TKWWTP. The models were meant to evaluate the concentrations of carbon, nitrogen and phosphorous entering and leaving the activated sludge systems in these two plants in form of COD, TKN and TP. The modeling therefore was mainly focused on the steps leading to a calibrated model that could be used to reliably describe the plant performance specifically by quantifying concentrations entering and leaving the activated sludge systems in these two treatment plants

4.4.2 Process description

From collected data during plants tours a definition of the relevant process components and their sizes and properties specific to each treatment plant was made. Process flow diagrams were constructed outside of the modeling environment as observed at the plants and their manuals. They were paper sketched to understand the process configurations in each respective treatment plant. As it was not required to model the complete WWTP, only those parts that fit within the described process were useful for consideration in the model as described in the PFDs below.

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Figure 16: Process flow diagram fort CWWTP

Figure 17: Process flow diagram for TKWWTP

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The modeling process described in this study focused on the water line. A thorough insight into the water line during data collection was done at each treatment plant. The other part addressed was the splitting and redistribution of return activated sludge coming from the settlers to the beginning of the aeration tanks as this has a direct impact to the water line.

4.4.3 Model structure

The model structure for the hydraulics of each WWTP was based on the process description as outlined in each specific (PFD) Process Flow Diagram above. The definition of the two model structures was an essential phase of the modeling study. There was no point carrying out calibration if the models were not properly defined in the first place. Errors in e.g. flow rates of recirculation pumps are more sensitive than errors in model parameters {Hulsbeek, 2002}. The model definition in this research described the different process components for each of the two treatment plants. Aspects such as the number of compartments, aeration configuration, settling and control were taken into account during the definition of the model structure. During this phase it was essential to evaluate the oxygen gradients in the aerated tanks in a horizontal and vertical direction in all the aeration tanks for both the CWWTP and the TKWWTP. Certainly for surface aeration at the CWWTP, this process was important as it allowed the set up of a proper compartmentation in the aeration basins. The WWTP was not working so a theoretical gradient was developed based on literature e.g. oxygen concentration was expected to decrease as distance from the surface aerator increases both vertically and horizontally. For diffuse aeration at the TKWWTP visual inspection of the operation of the aeration was done and DO reading meters also aided in giving specific values of aeration in each of the units. Figure 18 below describes the model structure.

Figure 18: Model structure (Meijer et al., 2001)

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There were no controllers introduced in any of the two models developed in this study. In general it is required that the effects of the control incorporated in the model are in accordance with the operations of the process control in practice. In this research DO flow rates and concentrations were defined based on the actual measured values for each tank of the activated sludge system instead of implementing the controller. In theory, for steady-state simulations, introducing controllers in the model is not necessary. All average conditions can be reached by setting up the required flows and oxygen set points. However, in the process of calibrating the model, it can be convenient to have certain parameters controlled against their measured value (e.g. TSS in the aeration tank). This avoids the situation where when calibrating one aspect the other aspect has to be changed and so forth; a controller could do this automatically. These changes in untargeted parameters during calibration were accepted during the calibration of models.

In building the activated sludge models for these two plants the aeration tanks were the primary focus. As the norm in activated sludge modeling, compartmentation was used to distinguish between different processes of the activated sludge (e.g. anaerobic, and anoxic or anaerobic) as all the plants are plug flow based. In the case of plug-flow systems, the actual tank should be described as a number of smaller tanks in series so as to allow also for the profiling of such parameters like DO which might have different concentrations per different tank. Dissolved oxygen (DO) profiles were used to evaluate whether more than one completely mixed reactor was needed (over length and depth of the reactor). In the case that the concentration is near substrate affinity constant (Cs≈Ks), the rate becomes concentration dependent and the need for compartmentation needs to be evaluated.

Aeration was built into the models relatively easily by use the exact air input based on kgO2/kWh as measured in each unit of the AS system. In the case of tanks that have not been well mixed, oxygen affinity is normally used to calibrate the aerobic (and anoxic) conversions but this was not done in this study as all tanks were assumed to be well mixed.

i. Unit configuration in BioWin

Figure 19: BioWin unit configuration CWWTP

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Figure 20: BioWin unit configuration TKWWTP

4.4.4 Characterization of the main flows

Characterisation aimed at defining and determining the quality and quantity of wastewater in the treatment plants in these two similar climatic regions by determining the degree of bio-degradability of the wastewater. Wastewater characterization is a decisive activity as it largely determines the model input and thereby the model results. Comprehensive characterisation was carried out to best describe the wastewater in both the CWWTP and the TKWWTP. Besides the influent data, some effluent data also should be measured but this was not possible for CWWTP as there was no effluent because the plant was not working. The history of performance for the CWWTP however had the effluent parameters collected during periods when the plant was still functional. Both influent and effluent information was available for the TKWWTP. The data collection was structured based on a general protocol as required by the STOWA method and specified in detail for each of two studied plants. Primary data was collected from the CWWTP but the data from the TKWWTP in terms of influent and effluent parameters for both history and current performance was secondary data collected by the staff at the plant.

By using historical data and or specific measurements, the important process flows (influent, effluent and the activated sludge) were characterized. Fractionation was done by the BioWin model for each plant and the tables are given below in tables 8 and 9. However, it is often not necessary to perform the specific influent and effluent and activated sludge measurements needed for the characterization as most of this information is normally available at the plant. Influent characterization is the basis for the flow characterization (Roeleveld, 2001). According to (Roeleveld, 2001) When the model is used for a system choice or system evaluation, daily average concentrations of influent and effluent are sufficient. If the model is used for optimization or the development of control strategies, specific data (2 or 4-hour composite samples) is required. In this case other flows in addition to the influent and effluent (e.g. recirculation flows) should also be sampled but this study did not cover such because the objective was to evaluate the system performance and most of the required data was available at the treatment plants. Figure 21 gives the procedure for the characterization of the main flows.

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Figure 21: Procedure for the characterization of the main flows (Meijer et al., 2001)

Table 8: CWWTP COD fractions as shown in BioWin

Element name Default Used Fbs - Readily biodegradable (including Acetate) [gCOD/g of total COD]

0.1600 0.1600

Fac - Acetate [gCOD/g of readily biodegradable COD] 0.1500 0.1500 Fxsp - Non-colloidal slowly biodegradable [gCOD/g of slowly degradable COD]

0.7500 0.7500

Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.0500 0.0900 Fup - Unbiodegradable particulate [gCOD/g of total COD] 0.1300 0.1300 Fna - Ammonia [gNH3-N/gTKN] 0.6600 0.6600 Fnox - Particulate organic nitrogen [gN/g Organic N] 0.5000 0.5000 Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.0200 0.0300 FupN - N:COD ratio for unbiodegradable part. COD [gN/gCOD]

0.0350 0.0350

Fpo4 - Phosphate [gPO4-P/gTP] 0.5000 0.5000 FupP - P:COD ratio for unbiodegradable part. COD [gP/gCOD] 0.0110 0.0110 FZbh - Non-poly-P heterotrophs [gCOD/g of total COD] 0.0001 0.0001 FZbm - Anoxic methanol utilizers [gCOD/g of total COD] 0.0001 0.0001

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Element name Default Used FZaob - Ammonia oxidizers [gCOD/g of total COD] 0.0001 0.0001 FZnob - Nitrite oxidizers [gCOD/g of total COD] 0.0001 0.0001 FZamob - Anaerobic ammonia oxidizers [gCOD/g of total COD]

0.0001 0.0001

FZbp - PAOs [gCOD/g of total COD] 0.0001 0.0001 FZbpa - Propionic acetogens [gCOD/g of total COD] 0.0001 0.0001 FZbam - Acetoclastic methanogens [gCOD/g of total COD] 0.0001 0.0001 FZbhm - H2-utilizing methanogens [gCOD/g of total COD] 0.0001 0.0001 Table 9: TKWWTP COD fractions as shown in BioWin

Element name Default Used Fbs - Readily biodegradable (including Acetate) [gCOD/g of total COD]

0.1600 0.1600

Fac - Acetate [gCOD/g of readily biodegradable COD] 0.1500 0.1500 Fxsp - Non-colloidal slowly biodegradable [gCOD/g of slowly degradable COD]

0.7500 0.7500

Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.0500 0.0800 Fup - Unbiodegradable particulate [gCOD/g of total COD] 0.1300 0.1300 Fna - Ammonia [gNH3-N/gTKN] 0.6600 0.6600 Fnox - Particulate organic nitrogen [gN/g Organic N] 0.5000 0.5000 Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.0200 0.0400 FupN - N:COD ratio for unbiodegradable part. COD [gN/gCOD]

0.0350 0.0350

Fpo4 - Phosphate [gPO4-P/gTP] 0.5000 0.5000 FupP - P:COD ratio for unbiodegradable part. COD [gP/gCOD] 0.0110 0.0110 FZbh - Non-poly-P heterotrophs [gCOD/g of total COD] 0.0001 0.0001 FZbm - Anoxic methanol utilizers [gCOD/g of total COD] 0.0001 0.0001 FZaob - Ammonia oxidizers [gCOD/g of total COD] 0.0001 0.0001 FZnob - Nitrite oxidizers [gCOD/g of total COD] 0.0001 0.0001 FZamob - Anaerobic ammonia oxidizers [gCOD/g of total COD]

0.0001 0.0001

FZbp - PAOs [gCOD/g of total COD] 0.0001 0.0001 FZbpa - Propionic acetogens [gCOD/g of total COD] 0.0001 0.0001 FZbam - Acetoclastic methanogens [gCOD/g of total COD] 0.0001 0.0001 FZbhm - H2-utilizing methanogens [gCOD/g of total COD] 0.0001 0.0001

4.4.5 Calibration

The calibration procedure was an iterative process that was terminated when the required results were more or less obtained. A model can never completely reproduce the measured

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data. There was no need to aim at unnecessarily high accuracy as this would increase both the efforts and costs substantially and would not bring added value to the research. After the model set up was properly defined and the required model input data had been implemented for both plants, the first simulation was done with the aim of checking to see if the model had been fitted to the measured data for each specific treatment plant. Data for calibration was from the year 1994 for CWWTP and 2010 for TKWWTP. The results of this initial test simulation indicated that a major adjustment of the model parameters was not necessary for both models; literature confirms that it is most likely that a structural error (gross error) is present if simulation failed. For the two models constructed, there was no failure to run the initial simulation for checking for the gross error.

i. Fitting the TP balance

In BioWin the activated sludge system needs to be balanced for total phosphorus according to the measurements. Therefore, the incoming and outgoing flows and mass loads need to be reproduced based on the total phosphorus balance. Total phosphorus in the effluent was calibrated by adjusting the loss of solids from the clarifier. This was done through iteration and checking concentration of phosphorus in the effluent stream each time a change was made in the wasting rate. For CWWTP the underflow from the clarifier balanced phosphorus at 10800m3/d whilst for TKWWTP it balanced at 43870m3/d. By regulating the waste flow, total phosphorus in the system was introduced into the mass balances, thereby fitting the models. By doing this, the SRT was also fixed in the model (waste activated sludge – WAS, was also fixed in the process). By fitting the total phosphorus balance and setting the SRT for the models, the first step of the calibration was completed. CWWTP plant was fitted at an SRT of 15days whilst TKWWTP balanced at 12days.

ii. Fitting the solids CODX balance

Because the CODX balance is a non-conserved balance, an incorrect influent COD load will generally be compensated for by the oxygen consumption of the activated sludge process. In the previous step the SRT was fixed according to the TP balance. Hence, the total amount of MLSS in the WWTP is (mainly) determined by the influent XI/X ratio as inert COD (XI) accumulates in the WWTP. By adjusting the influent XI/X ratio the model can be fitted to the measured TPX/CODX ratio coming from the activated sludge characterization. Hereby, all model uncertainties related to the production of XI and the influent characterisation are lumped together in the influent XI/X ratio. This is justified because the amount of inert COD in the influent cannot be analytically measured and therefore is based on estimation. None of the two models was adjusted for this parameter.

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Figure 22: Schematic relation between the influent COD load (XS and XI) and sludge production (VSS or COD).

Source: (Meijer et al., 2001)

Inert particulate material is the main contributor to waste sludge production and therefore fitting the fraction XI/XS in the model immediately affects WAS.

iii. Fitting soluble CODS in the effluent

The soluble non biodegradable COD (SI) for both the CWWTP and TKWWTP were adjusted form the default values during the calibration process. The default value was 0.05 and for CWWTP it was increased to 0.09, TKWWTP was increased to 0.08 from 0.05. Increasing SI in the influent directly affects the effluent concentration, as SI is not converted. In doing so, SA in the influent is not changed, as this model component is directly measured from the influent VFA.

iv. Fitting the TKN balance

Soluble unbiodegradable TKN for CWWTP was adjusted through iteration from 0.02 to 0.03 during fitting TKN for TKWWTP it was also adjusted from 0.02 to 0.04. Like COD, TKN is a non-conserved compound. An incorrect (influent) TKN load is generally compensated for by the oxygen consumption of the activated sludge process via the nitrification process, and nitrogen gas production via denitrification.

v. Calibrating the net nitrified load

To simulate ammonium in the effluent, DO set points in the aeration tank are usually adjusted. Thus the original aerated volume is not changed. Otherwise it is permissible to adjust the

47

growth rates of nitrifying organisms. Usually calibrating nitrification is straightforward but it was not done for any of the two models in this study.

vi. Calibrating the net denitrified load

Though none of the two models was calibrated on the net denitrified load, it is important to mention that net denitrification in the activated sludge process is fitted by increasing the parameter KO2. This model parameter limits the anoxic process in the presence of oxygen (according to a Monod equation). By increasing this value the anoxic process does not detect that oxygen is present in the aeration tank and will continue to denitrify and therefore simultaneous nitrification and denitrification is stimulated in the activated sludge process. A practical reason that anoxic bacteria in a anaerobic tank do not detect oxygen can be explained by the fact that oxygen can be depleted inside sludge flocks as the result of an oxygen gradient towards the inside of a flock. The same effect can also occur on a larger scale, when an aeration tank, often aerated from the surface, is not properly mixed. Under these conditions sludge will settle to the bottom of the tank where anoxic and even anaerobic conditions can occur. To a lesser extent this also happens in carousel-type reactors; by artificially introducing an oxygen gradient in one tank, zones that include and exclude oxygen are created, thereby simultaneously nitrifying and denitrifying. The diagram below shows how the typical Monod equation responds when increasing or decreasing the KO2 value; when the value is increased the relative reaction rate (0-1) increases speeding up the anoxic process while the oxygen concentration remains the same. The same principle also accounts for anaerobic processes which are suppressed by increasing KO2.

Figure 23: Fitting model kinetics simulating diffusion limitation in activated sludge

Source: (Meijer et al., 2001)

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The model can be used beforehand to evaluate which internal process concentrations are sensitive. These can then be measured. Also, if necessary for the calibration of the internal flows, the effluent calibration has to be checked again in an iterative loop.

4.4.6 Model validation

After successful calibration, the models were supposed to be validated prior to the actual model-based investigations. Therefore, sufficient data from different monitoring periods was available in form of history of performance of the plants. Typically one set of data is used for the calibration and another is used for the validation of the calibrated model results. It is e.g. possible to use plant data from a different period for validation, or use data from a completely different set of conditions (e.g. when primary settlers are out because of maintenance). In this research, validation was done using data obtained in the year 2013 for TKWWTP and for CWWTP for the year 2004 as the plant worked with little breakdowns during that period.

Figure 24: Validation procedure

Source: (Meijer et al., 2001)

Validation is carried out to evaluate whether the model applicability can be extended to a reasonable range of dynamic operating conditions within the regularly experience, such as wet weather conditions and/or seasonal climate changes. Dynamic validation only is used for plants that have been selected to be optimized in considerable detail (e.g. designing dynamic process control). It often happens that the initial calibrated model does not satisfactorily match the tested dynamic operating conditions. In this case a second calibration would be necessary. Usually one additional iterative fine-tuning procedure is sufficient to make a model satisfactorily describing the selected conditions. It should be noted that models are not designed to describe extreme conditions that may occur at the treatment plant and such conditions (if any) were not be considered in this research. As this research focused on steady state modeling, validation of the models was not a major priority also considering the fact that the CWWTP was not working it would be justifiable not to validate it for current performance but rather double calibrate it.

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4.4.7 Scenario investigation

Based on the conclusions reached from the mass balance data analysis and the calibrated model results, the operational assessment of the plants were carried out by changing concentrations of such parameters like TP, COD, TKN and Flow in the influent.

Scenario investigation is a remarkable achievement for the modeling technology as it makes it possible to describe the (measured) operation of a wastewater treatment plant with a high level of accuracy. However, the true power of the application of models lies in the extrapolation of the designed to non-existing operational conditions. {Gernaey, 2004} Scenario studies should not exceed a maximum of 5 scenarios as above this number it will be difficult to distinguish between the simulations and make well informed choices about the design.

In this research, 2 scenario studies were done for CWWTP in trying to establish a noble range of concentration of phosphorus that would see the plant running without phosphorus limited conditions in the aeration tanks and the actual aeration needed to effectively oxidize the organic matter at optimum costs. Phosphorus concentration in the influent was therefore adjusted by iteration from 2mg/l to 2.5mg/l and there were still limited conditions in the aeration tanks. After a second increase from 2.5 to 3mg/l there were no limited conditions. This led to an understanding that there is need for an external source of phosphorus in order to balance the system otherwise a redesign would be necessary for the CWWTP if not opting for chemical precipitation.

The second scenario investigation involved the iterative manipulation of the concentration of oxygen in the aeration tanks from 4mg/l to 3mg/l then to 2mg/l. There was no significant change in each time an adjustment was done to the oxygen concentration in the aeration tanks from 4mg/l to 2mg/l. This led to a conclusion that the CWWTP could run effectively at an oxygen concentration of 2mg/l. This could reduce the energy consumption in the plant by almost half.

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CCHHAAPPTTEERR 55

RREESSUULLTTSS

5.1 Kinetic parameters used Table 10: Summarized kinetic parameters used in the BioWin models.

i. Ordinary Heterotrophic Organisms Name Default Value Max. spec. growth rate [1/d] 3.20000 3.20000 Substrate half sat. [mgCOD/L] 5.00000 5.00000 Anoxic growth factor [-] 0.50000 0.50000 Aerobic decay [1/d] 0.62000 0.62000 Anoxic/anaerobic decay [1/d] 0.30000 0.30000 Hydrolysis rate (AS) [1/d] 2.10000 2.10000 Hydrolysis half sat. (AS) [-] 0.06000 0.06000 Anoxic hydrolysis factor [-] 0.28000 0.28000 Anaerobic hydrolysis factor [-] 0.50000 0.50000 Adsorption rate of colloids [L/(mgCOD d)] 0.80000 0.80000 Ammonification rate [L/(mgN d)] 0.04000 0.04000 Assimilative nitrate/nitrite reduction rate [1/d] 0.50000 0.50000 Fermentation rate [1/d] 3.20000 3.20000 Fermentation half sat. [mgCOD/L] 5.00000 5.00000 Anaerobic growth factor (AS) [-] 0.12500 0.12500 Hydrolysis rate (AD) [1/d] 0.10000 0.10000 Hydrolysis half sat. (AD) [mgCOD/L] 0.15000 0.15000

ii. Phosphorus Accumulating Organisms

Name Default Value Max. spec. growth rate [1/d] 0.95000 0.95000 Max. spec. growth rate, P-limited [1/d] 0.42000 0.42000 Substrate half sat. [mgCOD(PHB)/mgCOD(Zbp)] 0.10000 0.10000 Substrate half sat., P-limited [mgCOD(PHB)/mgCOD(Zbp)] 0.05000 0.05000

Magnesium half sat. [mgMg/L] 0.10000 0.10000 Cation half sat. [mmol/L] 0.10000 0.10000 Calcium half sat. [mgCa/L] 0.10000 0.10000 Aerobic decay rate [1/d] 0.10000 0.10000 Anaerobic decay rate [1/d] 0.04000 0.04000 Sequestration rate [1/d] 6.00000 6.00000 Anoxic growth factor NO3 [-] 0.33000 0.33000 Anoxic growth factor NO2 [-] 0.33000 0.33000

iii. Nitrogen Oxidizing Bacteria

Name Default Value Yield [mgCOD/mgN] 0.09000 0.09000 N in biomass [mgN/mgCOD] 0.07000 0.07000 P in biomass [mgP/mgCOD] 0.02200 0.02200 Fraction to endogenous residue [-] 0.08000 0.08000 COD:VSS ratio [mgCOD/mgVSS] 1.42000 1.42000

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5.2 BioWin Results Plant efficiency was calculated using the following formulae: 5.2.1 Efficiency

Where E - Efficiency So - Influent concentration S - Effluent concentration

5.2.2 Crowborough

Table 11: CWWTP efficiency

Parameter Influent Effluent Standard Efficiency %

COD (mg/l) 457 39 60 92

TKN(mg/l) 25 5 10 81

TP(mg/l) 2 0.3 0.5 85

Figure 25: CWWTP COD BioWin chart

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COD for CWWTP was 457mg/l in the influent and 39mg/l in the effluent. The model predicted a possibility of reducing the COD concentration by 92%. The standard for effluent discharge into natural water in Zimbabwe is 60mg/l. From this prediction, it is palpable that the plant has the potential to meet the national standard for COD discharge. Such a reduction in the concentration of COD would see an increase in the level of Oxygen concentration in the Marimba River which receives this effluent and discharges it to Lake Chivero.

Figure 26: CWWTP TP BioWin chart

TP for CWWTP was 2mg/l in the influent and 0.3mg/l in the effluent. The model predicted a possibility of reducing the TP concentration by 85%. The standard for effluent discharge into natural water in Zimbabwe is 0.5mg/l. It can be concluded that the plant has the potential to meet the national standard for TP discharge. Such a reduction in the concentration of TP would reduce greatly the rate of eutrophication in the receiving water bodies which are already almost dead waters as there is an overcast of water hyacinth in both the Marimba River and Lake Chivero. Water hyacinth overgrowth has disturbed many water related activities like fishing and boating. It should be noted that most families in the greater Manyame catchment make a survival out of fishing co-operatives.

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Figure 27: CWWTP TKN BioWin chart

TKN for CWWTP was 25mg/l in the influent and 5mg/l in the effluent. The model predicted a possibility of reducing the TKN concentration by 81%. The standard for effluent discharge into natural water in Zimbabwe is 10mg/l. This model prediction evidences that the plant has the potential to meet the national standard for TKN discharge. The reduction of nitrogen in water is of paramount importance as nitrogen depletes oxygen like COD but it also causes a disease in fish ( Brown blood), in mammals, it causes methemoglobin by reacting with Hemoglobin and Causes blue baby disease in babies. Marimba River which receives this effluent and discharges it to Lake Chivero is currently overloaded and this reduction could save people and the whole ecosystem.

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5.2.3 Thungkru

Table 12: TKWWTP efficiency

Parameter Influent Effluent Standard Efficiency %

COD 120 22 60 82

TKN 10 2 100 80

TP 2 0.4 2 77

Figure 28: TKWWTP COD BioWin chart

An influent COD concentration of 120mg/l and an effluent of 22mg/l were simulated at TKWWTP. There was an efficiency of 82% COD removal. The national standard for COD discharge into natural water in Thailand is 60mg/l. The plant therefore was meeting the standard for discharge.

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Figure 29: TKWWTP TKN BioWin chart

TKN Influent concentration was 10mg/l for TKWWTP and this was reduced to 2mg/l against a national standard of 100mg/l. There was 80% removal of TKN from the wastewater. It is of paramount importance to mention that allowable TKN concentration in wastewater in Thailand is higher than would be expected. The (PCC) Pollution Control Committee set these standards after certain considerations in native Thai industries which produce wastewater with high TKN values. There are some industries which can discharge up to 200mg/l TKN like for the following factories: - food furnishing factories (category 13 (2) - animal food factories (category 15(1) according to their national classification. The set national allowable standard is 100mg/l. The standards were summarized from the Notification of the Ministry of Science Technology and Environment No.3 B.E. 2539 (1996) issued under the Enhancement and Conservation of the National Environmental Quality Act B.E. 2535 (1992). The Notification was published in the Royal Government Gazette, Vol.113, Part 13 D, dated 13 February B.E. 2539 (1996).

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Figure 30: TKWWTP TP BioWin chart

The TKWWTP received a TP influent of 2mg/l and an effluent of 0.4mg/l. This summed up to a TP removal efficiency of 77%. The national standard for Phosphorus removal is 2mg/l. This showed that the plant met all its national standard requirements in terms of effluent discharge into the environment. The plant received a low concentration of phosphorus in the influent stream and this influent concentration was equal to the discharge standard of 2mg/l. This means that there was no need to concentrate more on P removal in the design of the plant as generally the community generates low phosphorous concentrations. This was not the case with CWWTP. The treatment system at CWWTP was designed primarily to remove phosphorus as the 5 stage modified Bardenpho is for removal of phosphorus.

5.3 Results comparison

Table 1: Modeling results comparison

i. Crowborough wastewater treatment plant

Parameter Influent Effluent Standard Efficiency % COD (mg/l) 457 39 60 92 TKN(mg/l) 25 5 10 81 TP(mg/l) 2 0.3 0.5 85

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ii. Thungkru wastewater treatment plant

COD (mg/l) 120 22 60 82 TKN(mg/l) 10 2 100 80 TP(mg/l) 2 0.4 2 77

a. CWWTP has the potential to work even more efficiently than the TKWWTP in removal of all the parameters of concern in this study. (COD, TKN and TP).

b. COD concentration into the plant was higher in CWWTP than the TKWWTP. CWWTP had an influent COD of 457mg/l and effluent of 39mg/l whilst TKWWTP had an influent COD of 120mg/l and effluent COD of 22mg/l. CWWTP had 92% COD removal efficiency while TKWWTP had 82% COD removal efficiency. This indicates that the loading of the plants were different in these two locations thereby calling for a strict characterization of wastewater during baseline studies before erection of these activated sludge systems.

c. Proper COD fractionation also is a necessity so as to establish the real fractions influencing plant behavior.

Table 14: Fractions adjusted during model calibration

Crowborough wastewater treatment plant

Fractions Standard Actual

Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.0500 0.0900 Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.0200 0.0300

Tungkru wastewater treatment plant

Fus - Unbiodegradable soluble [gCOD/g of total COD] 0.0500 0.0800 Fnus - Soluble unbiodegradable TKN [gN/gTKN] 0.0200 0.0400

Unbiodegradable soluble was higher in CWWTP at 0.09g COD/g of total COD than in the TKWWTP at 0.8g COD/g of total COD. This evidences why CWWTP had an effluent COD of 39mg/l higher than the TKWWTP at 22mg/l. A higher degree of non biodegradable COD in wastewater will directly increase the effluent COD concentration as the biomass cannot assimilate the inorganic components of wastewater. Likewise the soluble non-biodegradable TKN was higher in TKWWTP at 0.04 gN/g-TKN than in CWWTP where it was 0.03 gN/gTKN. This can explain the unusual TKN standard of discharge (100mg/l) in Thailand.

i. Like COD, Influent TKN was higher in CWWTP than in TKWWTP irrespective of the fractions. CWWTP had an influent of 25mg/l and effluent of 5mg/l whilst TKWWTP had an influent of 10mg/l and effluent of 2mg/l. This gave CWWTP a TKN removal efficiency of 81% and 80% for TKWWTP. These plants almost had

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the same performance in terms of TKN removal. The standard for Zimbabwe was 10mg/l and 100mg/l for Thailand. This could reflect the differences in issues considered by these countries in setting up discharge standards for TKN. In some instances industrial wastewater treatment has allowable discharge of 200mg/l in Thailand. In this research, a lower TKN concentration of 10mg/l was established in wastewater thereby failing to justify a 100mg/l allowable discharge. There is need for consideration of a lower standard of TKN discharge for domestic wastewater treatment.

ii. The influent TP for both plants was at 2mg/l. The plants differed in removal efficiency and the methods of removal. CWWTP had 85% removal of TP whilst TKWWTP had 77% removal efficiency. CWWTP had biological removal through 5 stage Bardenpho which is costly as compared to chemical precipitation by ferric chloride being done at TKWWTP. Because of economic implications, it would be better for CWWTP to precipitate TP as the utility is already failing to run the BNR plant.

iii. The CWWTP system operated at a temperature of 25 Degrees Celsius whilst TKWWTP operated at 28 Degrees Celsius. These temperatures both fall into the range for tropical climatic conditions which are suitable for biological activity during microbial metabolism. There were differences in SRT as CWWTP had an SRT of 15days and TKWWTP had 12days. This has a direct implication on system efficiency. This can explain why CWWTP turned out to have a higher efficiency in removal of all parameters in this study. The rule of thumb is that a higher sludge age gives better efficiency.

iv. CWWTP was not automated whilst TKWWTP was automated. The detection of problems or system changes is a challenge in CWWTP unless they are severe. System ability to detect small changes is important as it allows for early corrective action before total system failure which might turn to be more expensive to handle than correcting problems in their early stages.

5.4 Lessons learnt from TKWWTP recommended for CWWTP

i. An influent TP concentration of 2mg/l should be precipitated instead of using a 5 stage Bardenpho plant. This can reduce plant footprint as well as reduce energy consumption by the treatment process.

ii. Plant automation is helpful for detection and identification of small changes in the treatment system. Inability to detect small perturbations in the treatment system can lead to compounding of problems and a total breakdown of the whole system. If detected early, corrective measures can be taken to maintain the treatment system.

iii. Wastewater characterisation is crucial before selecting a system for wastewater treatment. The inability of the CWWTP to meet standards of effluent disposal into the environment for a period of 20 years is a sign of an improper system for treating the wastewater in that community even if there are other issues related to socio-economic and political disturbances.

iv. Covering a wastewater treatment plant (in-house wastewater treatment) has benefits to the society as this allows for gas collection and reduction of nuisance. Presence of

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a WWTP in an area can devalue the land if it is not covered. Covering it maintains the integrity and value of the land in its vicinity.

v. TKWWTP can hold up to 5 times its design capacity during storm events. CWWTP receives infiltration inflow during the rainy season especially the month of December but this disturbs the treatment efficiency of the plant. There is need to retrofit the plant as well to sustain upsets during storm events. In general, measures should be put in place to cater for known possible natural or anthropogenic disturbances that can interfere with the treatment of wastewater. This includes such issues as possible poisoning of microbes in the plant as this has a direct negative effect on the ability of the biomass to stabilize the wastewater.

5.5 Further work and future applications

i. A further investigation on the loading and possible removal mechanisms for PCPs and pharmaceuticals from both the TKWWTP and CWWTP should be considered. None of these plants has an intentional removal mechanism for micro-pollutants.

ii. Studies should be done on politico-socio-economic factors affecting wastewater treatment in tropical or developing and under developed countries with special emphasis on wastewater profiling and sanitation in the political, social and economic arenas of these societies.

iii. A comprehensive insight is needed on setting effluent standards in Thailand, especially the discharge standard for TKN which has a range of 100-200mg/l depending on the type of industry discharging the wastewater.

iv. There is need to develop a scientifically sound technology selection matrix with flexible engineering principles comprehensive enough to allow proper selection of wastewater treatment systems customizable to suite local conditions and indigenous knowledge systems for all communities.

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CCHHAAPPTTEERR SSIIXX:: CCOONNCCLLUUSSIIOONNSS AANNDD RREECCOOMMMMEENNDDAATTIIOONNSS

6.1 Conclusions

CWWTP did not meet any discharge standard in December from 1994 to date 2014 which can be a sign of a mistake in design and or operation of the plant. Highest effluent concentration was in 1997 at 480mg/l and lowest was in 2009 at 99mg/l.

CWWTP receives high COD concentration than TKWWTP 457mg/l and 120mg/l respectively which can be attributed to the fact that Harare has a combined sewer system and Bangkok uses a separate system. The presence of industrial effluent in Harare can explain the presence of a higher soluble non-biodegradable fraction of COD in CWWTP than TKWWTP 0.09mg/l and 0.08mg/l respectively.

Activated sludge systems can work efficiently in tropical climates as long as the system was designed with consideration of local conditions and wastewater characteristics like the case with TKWWTP.

In Harare activated sludge is a possible system but it is a wrong system as evidenced by the city’s failure to maintain the system. There was no evidence for preliminary investigation or baseline study in erecting the CWWTP. The design was possibly adopted from elsewhere or the baseline study was not comprehensive enough to avail information for designing such a treatment plant.

CWWTP has the potential to cause health hazards to the community considering the untreated wastewater released into the city’s source for water supply. A relationship can be established between wastewater treatment failure and seasonal outbreak of water related diseases like cholera and dysentery as there is no form of control of environmental contamination by pathogens.

The two plants were performing differently and other than operational differences, they had different loading as well as influent qualities. This can be attributed to spatial differences as different societies have different wastewater footprints.

6.2 Recommendations

CWWTP should reduce Oxygen concentration form 4mg/l to 2mg/l in the aeration basins as an increase of aeration above 2mg/l does not make any significant difference to the treatment system in place and there is need to generate and use energy at the CWWTP to sustain its energy needs as well as the needs of the community. Production of energy at the TKWWTP can as well reduce costs on running the automated system.

A phosphorus influent concentration of 2mg/l in CWWTP should be precipitated by Iron Chloride just like in TKWWTP as the influent concentrations were the same. This can aide in saving the energy needed to run a 5 stage BNR plant by reducing it to a 3 stage BNR plant.

The need for investment in rehabilitation and maintenance of municipal water infrastructure in Harare is unquestionable. Privatization with nominal control from government should be considered for improvement in service provision.

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Stakeholders like industry should play an active role in wastewater treatment in Harare by formation of clusters to assist both financially and technically in Water asset management and sustainable urban water management.

Harare like other cities south of the Sahara should explore the possibility for use of land based wastewater treatment systems like ponds as there is a lot of open space available and the temperatures are conducive for biological treatment of wastewater. There is need for thorough wastewater characterization before treatment system selection and plant automation should be considered so as to be able to detect system changes in time and take corrective action before total system breakdown.

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AAPPPPEENNDDIICCEESS

Appendix 1: History of CWWTP performance Appendix 2: CWWTP sampling results Appendix 3: History of TKWWTP performance Appendix 4: Methods of analysis and preservation of sample Appendix 5: Nomenclature Appendix 6: CWWTP sampling and analysis responsibility sheet