upflow anaerobic sludge blanket reactor modelling

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KUNGLIGA TEKNISKA HÖGSKOLAN (KTH) KTH Chemical Science and Engineering UPFLOW ANAEROBIC SLUDGE BLANKET REACTOR: MODELLING RAÚL RODRÍGUEZ GÓMEZ LICENTIATE THESIS IN CHEMICAL ENGINEERING SCHOOL OF CHEMICAL SCIENCE AND ENGINEERING DEPARTMENT OF CHEMICAL ENGINEERING AND TECHNOLOGY ROYAL INSTITUTE OF TECHNOLOGY STOCKHOLM, SWEDEN 2011

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Page 1: Upflow Anaerobic Sludge Blanket Reactor Modelling

KUNGLIGA TEKNISKA HÖGSKOLAN

(KTH)

KTH Chemical Science

and Engineering

UPFLOW ANAEROBIC SLUDGE BLANKET REACTOR: MODELLING

RAÚL RODRÍGUEZ GÓMEZ

LICENTIATE THESIS IN CHEMICAL ENGINEERING

SCHOOL OF CHEMICAL SCIENCE AND ENGINEERING DEPARTMENT OF CHEMICAL ENGINEERING AND TECHNOLOGY

ROYAL INSTITUTE OF TECHNOLOGY STOCKHOLM, SWEDEN 2011

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UPFLOW ANAEROBIC SLUDGE BLANKET REACTOR: MODELLING Raúl Rodríguez Gómez Licentiate Thesis in Chemical Engineering Department of Chemical Engineering and Technology School of Chemical Science and Engineering Royal Institute of Technology Stockholm, Sweden TRITA-CHE Report 2011:4 ISSN 1654-1081 ISBN 978-91-7415-849-6 Copyright © 2011 by Raúl Rodríguez Gómez

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ABSTRACT

Anaerobic treatment is widely used around the world as a biological stage in both domestic and industrial wastewater-treatment plants. The two principal advantages of anaerobic over aerobic treatment are the production of biogas, which can be used as fuel, and the lower rate of biomass production, which results in lower maintenance costs for the plant. The upflow anaerobic sludge blanket (UASB) reactor is an attractive alternative for regions in hot climates since it works better under mesophilic conditions and it does not need any supporting structure for the development of microorganisms, which grow in the form of granules. In this thesis, a model describing the UASB reactor behaviour with respect to substrate degradation, microorganism growth and granule formation was developed. The model is transient and is based on mass balances for the substrate and microorganisms in the reactor. For the substrate, the processes included in the model are dispersion, advection and degradation of the organic matter in the substrate. The reaction rate for the microorganisms includes the growth and decay of the microorganisms. The decay takes into account the microorganism dying and the fraction of biomass that may be dragged into the effluent. The microorganism development is described by a Monod type equation including the death constant; the use of the Contois equation for describing the microorganism growth was also addressed.

An equation considering the substrate degradation in the granule was required, since in the UASB reactor the microorganisms form granules. For this, a stationary mass balance within the granule was carried out and an expression for the reaction kinetics was then developed. The model for the granule takes into account the mass transport through the stagnant film around the granule, the intra-particle diffusion, and the specific degradation rate. The model was solved using commercial software (COMSOL Multiphysics). The model was validated using results reported in the literature from experiments carried out at pilot scale.

A simplified model was also developed considering the case in which the microorganisms are dispersed in the reactor and granules are not formed. The UASB reactor is then described as formed by many well-stirred reactors in series. The model was tested using experimental results from the literature and the sensitivity of the processes to model parameters was also addressed.

The models describe satisfactorily the degradation of substrate along the height in the reactor; the major part of the substrate is degraded at the bottom of the reactor due to the high density of biomass present in that region. This type of model is a useful tool to optimize the operation of the reactor and to predict its performance.

Keywords: Wastewater, anaerobic treatment, modelling, UASB reactor.

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LIST OF PAPERS This thesis is based on the followings papers, referred to by Roman numerals I to III:

I- Rodriguez R., L. Moreno (2009), Modeling of Substrate Degradation and Microorganism Growth in an UASB Reactor, In Proceedings of the International Conference on Chemical, Biological & Environmental Engineering (CBEE 2009), Singapore, October 9-11, 2009, Singapore; Kai Ed., pp 76-80.

II- Rodriguez R., L. Moreno (2010), Modelling of a Upflow Anaerobic Sludge Blanket reactor, In Proceedings of the Tenth International Conference on Modelling, Monitoring and Management of Water Pollution, Bucharest, June 9-11 2010, Brebbia C. (Ed), WIT Press, pp 301-310.

III- Rodriguez R., L. Moreno (2011), Simulation of a UASB reactor.

Manuscript submitted for publication. January 2011.

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CONTENTS LIST OF PAPERS ........................................................................................... IVABSTRACT ..................................................................................................... IIICONTENTS .................................................................................................... IVPREFACE ...................................................................................................... VII1- INTRODUCTION ......................................................................................... 1

1.1 BACKGROUND ........................................................................................................................ 11.2 THE AIM OF THE THESIS ....................................................................................................... 21.3 THE THESIS OUTLINE ............................................................................................................ 2

2- ANAEROBIC PROCESSES ........................................................................ 42.1 ANAEROBIC DIGESTION .......................................................................................................... 42.2 ANAEROBIC REACTORS ........................................................................................................... 5

3- THE UPFLOW ANAEROBIC SLUDGE BLANKET (UASB) REACTOR ...... 93.1 GRANULE DEVELOPMENT (THEORIES) ............................................................................... 9

3.1.1 PHYSICAL THEORIES ........................................................................................................ 103.1.2 MICROBIAL THEORIES ..................................................................................................... 113.1.3 THERMODYNAMIC THEORIES ........................................................................................ 12

3.2 MECHANISM OF GRANULE AGITATION INSIDE A UASB REACTOR ............................ 143.3 FACTORS INFLUENCING REACTOR PERFORMANCE ...................................................... 16

3.3.1 pH ......................................................................................................................................... 163.3.2 TEMPERATURE .................................................................................................................. 163.3.3 ORGANIC LOADING RATE (OLR) .................................................................................... 163.3.4 HYDRAULIC RETENTION TIME AND UP-FLOW VELOCITY ........................................ 163.3.5 SUBSTRATE ......................................................................................................................... 183.3.6 SLUDGE INOCULATION ................................................................................................... 19

4- KINETICS MODELS .................................................................................. 204.1 THE MICHAELIS-MENTEN MODEL ...................................................................................... 214.2 THE MONOD MODEL ............................................................................................................... 224.3 THE CONTOIS MODEL ............................................................................................................. 23

5- MODELS DESCRIBING UASB BEHAVIOUR ........................................... 255.1 EXISTING MODELS OF A UASB REACTOR ......................................................................... 25

6- PRELIMINARY MODEL ............................................................................. 326.1 STUDY CASE FOR THE PRELIMINARY MODEL ................................................................. 336.2 RESULTS AND DISCUSSION (PRELIMINARY MODEL) ..................................................... 34

7- THE MAIN MODEL .................................................................................... 367.1 MODEL DEVELOPMENT ......................................................................................................... 367.2 KINETIC RATE CONSTANT BASED ON THE MONOD MODEL ........................................ 367.3 THE GOVERNING EQUATIONS ALONG THE HEIGHT OF THE REACTOR ..................... 367.4 KINETIC RATE CONSTANT BASED ON A MASS BALANCE ............................................. 387.5 KINETIC RATE CONSTANT IN THE DEVELOPED MODEL ............................................... 407.6 MODEL APPLICATION ............................................................................................................ 40

8- CONCLUSIONS ........................................................................................ 45ACKNOWLEDGEMENT ................................................................................................................. 45

9-REFERENCES ........................................................................................... 46

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List of abbreviations AM2 Anaerobic Model Number 2 ADM1 Anaerobic Digestion Model Number 1 CSTR Continuous Stirred-Tank Reactor COD Chemical Oxygen Demand EGSB Expanded Granular Sludge Bed ECP Extracellular Polymers GSS Gas Solid Separator HRT Hydraulic Retention Time ODE Ordinary Differential Equation OLR Organic Loading Rate PVA Polyvinyl Alcohol RTD Residence Time Distribution TBC Turbulent Bed Contactor TSS Total Suspended Solids UASB Upflow Anaerobic Sludge Blanket Reactor VSS Volatile Suspended Solids VFA Volatile Fatty Acids

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PREFACE This research was financially supported by the Swedish International Development cooperation Agency (SIDA/SAREC). It was carried out as a cooperation program between the Department of Chemical Engineering and Technology in the Royal Institute of Technology (KTH), Stockholm, Sweden, and the National University of Engineering (UNI), Managua, Nicaragua. To be able to do this work, I received help directly and indirectly from many people I would like to thank:

�• My supervisor, Luis Moreno, for his encouragement and guidance. �• My parents for their unconditional backing and love. �• My Nicaraguan friends and partners for their faith and advice. �• Special thanks to Larisa Korsak and Isabelle Hughes for their support.

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1- INTRODUCTION 1.1 BACKGROUND Discharges of inadequately treated wastewater may have a great impact on natural water sources. The treatment of wastewater has been an issue of high priority in most industrial countries and they have therefore reached a very satisfactory quality of their wastewater discharges. However, due to economic factors, attitudes and education, the developing countries have problems in the treatment of residual water. In some cases, industrial wastewaters are discharged directly into public collection systems, and this affects the performance of the treatment plants. In many countries, wastewater is discharged directly into rivers or lagoons with damage to the aquatic life and to the quality of the water sources. Wastewaters with high amounts of nutrients and organic material may cause eutrophication. This problem could be solved by improving the wastewater-treatment system. There are two main types of biological processes for the treatment of wastewaters with a high content of organic material: aerobic processes and anaerobic processes. An advantage of the latter type is the small generation of cell mass; around three per cent of the organic matter present in the wastewater feed is converted to cell mass, whereas a high amount of organic matter is converted to cell mass in aerobic processes (Haandel and Lettinga 1994). Anaerobic processes also require less energy and, in addition, they produce methane that can be used as a source of energy. Some of the disadvantages of the anaerobic processes are that they require a long time to start-up, they may need additional alkali to control the pH, and they are more susceptible to toxic substances (Tchobanoglous et al. 2003). There are several anaerobic processes for the treatment of wastewater; one of these is the Upflow Anaerobic Sludge Blanket (UASB) reactor. This type of reactor may, in many cases, be the optimal solution for wastewater treatment in developing countries due to its simplicity and low cost. Moreover, this kind of treatment may be a very attractive alternative for wastewaters (industrial or/and municipal) containing high amounts of organic material in tropical countries, since the UASB reactor works better at high temperatures. In the design and construction of a wastewater-treatment plant or in the optimization of an existing plant, it is important to study the impact of the operating parameters on the treatment performance, as well as the consequences of the construction of the treatment plant for the environment. A helpful tool in the decision-making process is a simulation of the processes occurring in the treatment plant, but there are, at present, few models describing the processes in UASB reactors. The existing models include dispersion, advection and reaction processes to describe the substrate degradation (Kalyuzhnyi et al. 2006, Wu et al. 2007). The model proposed by Wu and co-workers (Wu et al. 2005) takes into account only the dispersion and transport terms since their model studies the distribution of a conservative tracer in the reactor so that there is no reaction term. Sponza and Uluköy (2008) compared three kinetic models describing the substrate removal kinetics of UASB reactors. Narnoli and Merhotra (1997) modelled the sludge distribution on the UASB reactor blanket. The Monod model is the most common model describing reaction kinetics (Rodriguez and Moreno 2009,

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Sponza and Uluköy 2008, Soto et al. 1993). However, another kinetic model used by several researchers is the Contois model (Rodriguez and Moreno 2010, Sponza and I ik 2002, Hu et al., 2002). Korsak et al. (2008) developed a model describing processes occurring in UASB reactors using the Monod kinetics to describe the reaction within the granules. The existing models show, in some cases, important weaknesses or some processes are missing. The UASB reactor is an adequate alternative for developing countries situated in tropical zones, since the process is simple, the equipment and operation are not too expensive, and the process works better at higher temperatures. This type of reactor could therefore, with advantage, be used in Nicaragua, a developing country located in Central America. Industries in Nicaragua do not treat their wastewater in a satisfactory way, in spite of knowledge of the consequences of allowing inadequately treated water to flow into the water recipients or municipal treatment plants. Economic aspects and a lack of consciousness are the main factors influencing the construction and improvement of the wastewater-treatment plants. Some industries have their own treatment plants, but in the majority of the cases they are inefficient. There are several anaerobic treatments like UASB reactors handling municipal, distillery and brewery wastewaters, but they have not achieved the granule formation of the UASB reactors. However these reactors produce biogas, which in some cases is utilised as fuel. For these reasons, we decided to study the UASB reactor, seeking to understand all the processes occurring in a UASB reactor, in order to be able to develop models to predict the changes in the performance of the UASB reactor when operating conditions are varied. In the future, the models developed in this study can hopefully be applied to Nicaragua�’s UASB reactors. 1.2 THE AIM OF THE THESIS

The aim of this work has been to develop a model that takes into account the most important processes occurring in a UASB reactor. In addition to the advection, dispersion, and reaction terms, the model should include the sludge distribution as a function of height in the reactor and describe the change of particle size in the reactor. The model includes the reactions occurring within the granule in an analytical way, and this significantly reduces the computation time. The model has been validated using data from the literature. 1.3 THE THESIS OUTLINE

This thesis is divided into eight parts. The first part is the introduction, in which a synopsis of the thesis is presented. The second chapter is an overview of anaerobic processes and it describes the conceptual aspects of the UASB reactor. In the third chapter several theories of granule formation are explained, and the mechanisms of granule agitation and factors controlling the reactor behaviour are discussed. The fourth chapter considers the different kinetics models commonly used to describe the reactions in a UASB reactor. Chapter five shows and explains the existing models describing UASB reactors, their weaknesses and strengths. In chapter six, a preliminary model describing UASB reactor behaviour is developed and tested with

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experimental data taken from the literature. This is the basis for the development of the model explained in chapter seven. In the model, an expression for the kinetic rate constant has been developed. Conclusions are presented in chapter eight.

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2- ANAEROBIC PROCESSES Anaerobic microorganisms are those that do not have oxygen as a terminal electron acceptor. The oxidation of organic matter in anaerobic respiration is coupled with the reduction of other electron acceptors such as sulphate (sulphate reduction), ferric iron (iron reduction), nitrate (denitrification), CO2 (methanogenesis) or some organic compounds. An anaerobic process involves the degradation of complex high-molecular-weight organic compounds to mainly methane (CH4) and carbon dioxide (CO2) (Bitton, 2005). In this chapter, we first describe the fundamental aspects of anaerobic digestion, and the different types of anaerobic reactors are then described. Therefore, the geometry and the operation of the UASB reactor are described. Some aspects of the reactor are addressed in detail, such as the mechanisms that cause the agitation within the reactor, different theories of granule formation, and finally the factors influencing the reactor performance. 2.1 ANAEROBIC DIGESTION Anaerobic digestion is a process by which microorganisms break down biodegradable material in the absence of oxygen. This process is widely used in the treatment of wastewater and organic waste because it provides a significant reduction in the mass of the input material (Haandel and Lettinga 1994). According to Seghezzo (2004), there are seven sub-processes in the anaerobic digestion of organic polymeric materials. Firstly complex organic materials are hydrolysed, secondly amino acids and sugars are fermented and thirdly long chain fatty acids and alcohols are oxidised. In the fourth stage, the anaerobic oxidation of short-chain fatty acids (except acetate) takes place, in the fifth stage, acetate is produced from carbon dioxide and hydrogen, and in the sixth stage the acetate is converted into methane. Finally, methane is produced by reduction of carbon dioxide by hydrogen. Figure 1 shows the anaerobic digestion of organic polymeric materials. Sub-processes are in colour and the roman numbers refer to the type of bacteria involved.

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Figure 1. Anaerobic degradation of complex polymers materials. Adapted from Seghezzo (2004). The Roman numerals indicate: I - fermentative bacteria, II �– hydrogen-producing acetogenic bacteria, III �– hydrogen-consuming acetogenic bacteria, IV �– carbon-dioxide- reducing methanogens, V - aceticlastic methanogens. Although there are seven sub-processes linked to the anaerobic digestion, there are only three principal classes of bacteria (Liu and Tay 2004):

�• Bacteria responsible for hydrolysis: This kind of bacteria hydrolyzes the input materials in order to break down insoluble organic polymers such as carbohydrates and make them available for other bacteria.

�• Acid-producing bacteria: Acidogenic bacteria convert the sugars and amino acids into carbon dioxide, hydrogen, ammonia, and organic acids. Acetogenic bacteria then convert these organic acids into acetic acid, along with additional ammonia, hydrogen, and carbon dioxide

�• Methane-producing bacteria: Methanogenic bacteria are finally able to convert these products to methane and carbon dioxide.

2.2 ANAEROBIC REACTORS A batch anaerobic reactor is the simplest form of reactor and it is used in poor developing countries in primitive wastewater-treatment plants. An example of this type of reactor is the septic tank. In wastewater treatment, several researchers have used batch reactors (Figure 2) to determine kinetic parameters. Huang et al. (2006), Gonzalez-Gil (2002), and Perez et al. (2001) using batch anaerobic reactors determined values of kinetic parameters following the Monod and Romero kinetics (Romero 1991) models. Kinetic parameter values determined in batch reactors have less uncertainty than values determined in other kinds of reactors since there are fewer parameters to handle.

Complex Polymers

Proteins

Carbohydrates

LipidsH

Y D

R O

L Y

S I

S

Amino acids,

Sugar

Fatty acids,

Alcohols

Intermediary

Products(propionate,

butyrate, etc.)

FERMENTATION

Acetate

ANAEROBIC

OXIDATION

HydrogenCarbon dioxide

H O

M O

A C

E T

O G

E N

E S

I S

REDUCTIVE METHANOGENESIS

ACETICLASTIC METHANOGENESIS

MethaneCarbon dioxide

II

III

IV

V

I

I

I

I

II

IIComplex Polymers

Proteins

Carbohydrates

LipidsH

Y D

R O

L Y

S I

S

Amino acids,

Sugar

Fatty acids,

Alcohols

Intermediary

Products(propionate,

butyrate, etc.)

FERMENTATION

Acetate

ANAEROBIC

OXIDATION

HydrogenCarbon dioxide

H O

M O

A C

E T

O G

E N

E S

I S

REDUCTIVE METHANOGENESIS

ACETICLASTIC METHANOGENESIS

MethaneCarbon dioxide

II

III

IV

V

I

I

I

I

II

Complex Polymers

Proteins

Carbohydrates

LipidsH

Y D

R O

L Y

S I

S

Amino acids,

Sugar

Fatty acids,

Alcohols

Intermediary

Products(propionate,

butyrate, etc.)

FERMENTATION

Acetate

ANAEROBIC

OXIDATION

HydrogenCarbon dioxide

H O

M O

A C

E T

O G

E N

E S

I S

REDUCTIVE METHANOGENESIS

ACETICLASTIC METHANOGENESIS

MethaneCarbon dioxide

Complex Polymers

Proteins

Carbohydrates

LipidsH

Y D

R O

L Y

S I

S

Amino acids,

Sugar

Fatty acids,

Alcohols

Intermediary

Products(propionate,

butyrate, etc.)

FERMENTATION

Acetate

ANAEROBIC

OXIDATION

HydrogenCarbon dioxide

H O

M O

A C

E T

O G

E N

E S

I S

REDUCTIVE METHANOGENESIS

ACETICLASTIC METHANOGENESIS

MethaneCarbon dioxide

II

III

IV

V

I

I

I

I

II

II

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Figure 2. Schematic representation of a batch reactor. The continuously stirred tank reactor (CSTR) also called a backmix reactor, is a tank in which the contents are mixed by mechanical or chemical agitation. There is always an input and output flow (Figure 3). In a CSTR reactor, it is assumed that all microorganisms are at the same concentration at every point in the reactor. Mendez-Acosta et al. (2010) used an anaerobic CSTR inoculated with sludge from a brewery in a full-scale UASB reactor to study the removal of organic material and the biogas production from wastewater from a tequila plant.

Figure 3. Schematic representation of a CSTR. An Upflow anaerobic sludge blanket (UASB) reactor is basically a tank that has a sludge bed in which organic material dissolved in the wastewater is degraded, and as a consequence of this digestion, biogas is produced. Wastewater enters at the bottom of the reactor. At the top, biogas is collected and the effluent of treated water leaves (Figure 4). At the upper part of the reactor, above the sludge bed, a blanket zone is formed where some particles of biomass are suspended. This zone acts as a separation zone between the water flowing up and the suspended biomass. One of the advantages of this kind of reactor is the low sludge production. Seghezzo et al. (2002) reported that only one discharge of sludge from a UASB is required per year for a four-meter-high reactor. UASB reactors are attractive in tropical countries because they work

Charged of reactants A and BA B

Products after reaction time

Constant inlet flow Biogas

Constant effluent flow

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better at mesophilic conditions. They are widely used to treat wastewater with a high organic load; and the treatment of wastewaters from the food industry is therefore a typical application. Granules are formed inside the UASB reactor without the need for any inert material. Other reactors based in the UASB have been developed, e.g., EGSB, IC.

Figure 4. Schematic representation of a UASB reactor. The Expanded Granular Sludge Bed (EGSB) reactor is a modification of a conventional UASB reactor. The EGSB reactor works with a higher upflow velocity and higher loading rate than a UASB reactor. The EGSB usually has an effluent recirculation, improving mixing at the bottom of the reactor. The upflow velocity (>4 m h-1) is faster than in a UASB reactor, and the sludge tends to expand more and eliminate the dead zones (Figure 5). Under these conditions, the performance of the reactor can be good at low temperatures (4-20 oC) (Seghezzo et al. 1998). Zhenjia et al. (2008) reported that the performance of the EGSB with regard to the removal of organic material was good at a high organic loading rate, but that the COD conversion to biogas was less than 50%.

Effluent

Biogas

Inlet

Biogasbubble

Granule

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Figure 5. Schematic representation of an EGSB reactor. The Internal Circulation reactor (IC) is an upflow anaerobic digester composed of basically two stages, where the main objective is biogas production. The sludge bed is found in the bottom of the reactor. Biogas is collected at that level and transported upwards together with water to the gas-liquid separator located at the top of the reactor. Due to gravity, the wastewater returns to the bottom of the reactor, increasing the mixing. The second stage, located in the upper part of the reactor, allows sedimentation of the organic material and reduces the possible wash out. A small amount of biogas is produced in that part of the reactor (Figure 6). Pareboom (1994) and Pareboom and Vereijken (1994) used the IC reactor for treating brewery and potato wastewater. Their work was focused on granule characteristics, which were compared with those of the granules in a UASB reactor. The IC reactor is a device that combines the principles of the UASB and EGSB reactors.

Figure 6. Schematic representation of an IC reactor.

Biogas

Effluent

Inlet

Bio

gas

bu

bb

leG

ran

ule

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Biogas

Inlet

Second

stage

First

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3- THE UPFLOW ANAEROBIC SLUDGE BLANKET (UASB) REACTOR

The inventive development of the UASB reactor is attributed to Lettinga. Before the 1970s, interest in anaerobic treatment was small and the majority of researchers thought that aerobic treatments were the best way to treat wastewater. However, the importance of the UASB method increased when the price of the raw oil rose in the 1970s. In 1979, during a trip to Africa, Lettinga visited a clarigester treating wine distillery wastewater, since engineers in charge of the wastewater treatment thought that the granulation and mixing were creating problems in the reactor. He noticed sludge in form of granules. After this experience, Lettinga started the development of the UASB reactor (Lettinga 2001). This anecdote may have been the beginning of the UASB reactor. Since granule formation is an important characteristic of the UASB reactor, we here present a description of different theories of granule development. In addition, the agitation mechanisms in the reactor and other factors influencing the reactor behaviour are presented and discussed. 3.1 GRANULE DEVELOPMENT (THEORIES) A UASB reactor is considered to be successful when anaerobic granules are formed (Hulshoff et al. 2004). Several theories have been developed to explain the growth of these granules. In 1968, the anaerobic filters used an inert porous media as support to attach the microorganisms and to attain high levels of retention of the active sludge in the reactor. Lettinga, in a full-scale study, concluded that the support medium was superfluous because microorganism can self-immobilise by forming granules (McHugh et al. 2003). Based on scanning and transmission electron microscopy, MacLeod et al. (1990) reported that anaerobic granules from an upflow anaerobic sludge bed and from a filter reactor have three-layer structures. The superficial layer contains many heterogeneous populations (rods, cocci, and filaments of different sizes); together with acidogens and hydrogen-consuming microorganisms. In the intermediate layer, rod-like bacteria (acetogens and hydrogen-consuming microorganisms) predominate. The core (third layer) consists of Methanotrix-like cells (methanogens). In the interior of the granules, holes or cavities can be found which are indications of vigorous gas production. A schematic granule with its different layers as proposed by MacLoad et al. (1990) is shown in Figure 7. Hulshoff et al. (2004) divided the anaerobic sludge granulation into three groups: physical, microbial and thermodynamic.

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Figure 7. Microorganism groups in different layers of the granules (Adapted from MacLeod et al., 1990).

3.1.1 PHYSICAL THEORIES Granulation can be explained by physical phenomena occurring in the reactor. The main physical theories are the �“growth of colonised suspended solids�” and the �“selection pressure�” theories. Growth of colonised suspended solids: Pareboom (1994) reported that the granules are initially formed by small particles (microorganisms) released from larger granules or particles. These suspended microorganisms attach themselves and form new granules. A fraction of the microorganisms may be lost by washout. Pereboom also reported that microorganisms colonise onto suspended solids present in the influent; growing to form new granules. The main process under normal operation that limits the granule size is the discharge of excess biomass from the reactor. Figure 8 shows the model proposed by Pereboom (1994).

Figure 8. Model proposed by Pareboom (Adapted from Pareboom, 1994). External shear forces, internal gas production or a high liquid recirculation rate do not disintegrate granules. Shear forces only lead to the release of small particles from the granules (Pareboom, 1994). Selection pressure: There is a continuous washout of small particles from the reactor, and the selection pressure is considered to be the sum of the hydraulic load and the load caused by the gas generation. Working under a high selection pressure, the light and dispersed sludge is removed from the system while heavier particles remain in the

Sluicing or removal of excess sludge

GrowthGrGr GrGr

AttritionFines Granules± 150 µm

Influent

Washout

Sluicing or removal of excess sludge

GrowthGrGr GrGr

AttritionFines Granules± 150 µm

Influent

Washout

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reactor. The production of a finely dispersed sludge is therefore greatly reduced and the bacterial growth is concentrated to a limited number of nuclei. The first generation consist of voluminous aggregates but, after a time, they will be denser due to bacterial growth both outside and inside the particles. On the other hand, a low selection pressure will lead to the formation of dispersed biomass with poor settling characteristics and poor biogas production, which tends to float (Flaherty et al. 2003, Hulshoff et al. 2004, Liu et al. 2003). 3.1.2 MICROBIAL THEORIES Bacteria are microorganisms that usually drive anaerobic processes (Bitton 2005). Bacteria secrete extracellular polymers (ECP), which are the key to granule attachment in several models. The following models or theories link microorganisms to the granulation. Extracellular polymer (ECP) bonding model: According to the ECP model, two neighbourly cells can connect to each other (or with inert particulate matter) because the ECP can lead to a change in the negative surface charge of the bacteria. With an increase in the organic loading rate, methanosarcina bacteria grow, secreting ECP and forming larger clumps. Figure 9 shows a schematic representation of the ECP bonding model (Liu et al. 2003).

Figure 9. ECP bonding model (Adapted from Liu et al., 2003). The Capetown model: In this model, it is assumed that the methanobacterium produces ECP. Under conditions of high hydrogen partial pressure, amino acids are over-secreted, and this possibly stimulates ECP formation. Methanobacterium and other bacteria will be then trapped in the ECPs matrix, and thus lead to the start of anaerobic granulation. Other anaerobic bacteria may have characteristics similar to those of the methanobacterium, and they may also contribute to granule formation. Figure 9 is also valid for this model since it is similar to ECP bonding model (Palns et al. 1987, Liu et al. 2003, Hulshoff et al. 2004). Spaghetti theory: This theory is applicable to granule formation in a UASB reactor treating acidified wastewater. Filamentous methanosaeta are attached onto other bacteria or materials forming aggregate structures that are favoured by the turbulence generated by biogas production or by attachment to finely dispersed matter. These entangled filaments form a kind of network in which other bacteria can be trapped. After a while, the structure grows due to the multiplication of trapped bacteria and forms a �“spaghetti ball.�” Rod-shaped microorganisms are then formed and the

Bridging

Polymeric chain

Shaped

Individual bacterium

Bridging

Polymeric chain

Shaped

Individual bacterium

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�“spaghetti ball�” density is increased, forming anaerobic granules (Liu et al. 2003, Hulshoff et al. 2004). 3.1.3 THERMODYNAMIC THEORIES A description of the granulation processes must take into account granule charges. Three theories are described here: the four-step model, the proton translocation-dehydration model, and the multi-valence ion-bonding model. The four-step model for granulation: According to Schmidt and Ahring (1996a) granulation follows four steps: a) transport of cells to the surface of inert material (substratum) or other cells; b) initial reversible adsorption to the substratum by physicochemical forces; c) irreversible adhesion of the cell to the substratum by microbial and/or polymer attachment; d) multiplication of the cell and development of the granules. Advection, diffusion, fluid flow, biogas production, and sedimentation are mechanisms that can transport cells to the substratum. When two cells collide, the initial adsorption takes place and the substratum plays the role of nucleus. From the collision, different situations can occur: a weak reversible attraction of cells located at a certain distance from the substratum, and strong repulsion or attraction, depending on the sign of the charges, when electrostatic interaction dominates. The irreversible adhesion step occurs when a strong bond is established between substratum and microbial cells. Colonization starts and, with help of ECP, other cells are attached. Reproduction of microbial cells then results in granule development. Figure 10 shows the four steps for granulation process.

Figure 10. Granulation steps (Adapted from Schmidt and Ahring, 1996) .i) Reversible association of two microbial cells becomes irreversible adhesion due ECP, ii) Cell division leads to sister cells, iii) Microbial colony creation, iv) Granule formation with a large number of cells. The proton translocation-dehydration model: In this theory, proposed by Tay et al. (2000), granulation begins with the dehydration of the bacterial surface as a result of proton translocation activity. This theory consists of four stages (Figure 11): a) Dehydration of bacterial surfaces, b) Embryonic granule formation, c) Granule maturation and d) Post-maturation (Tay et al. 2000). Dehydration of bacterial surfaces takes place when the acidogenic bacteria pump protons from the cytoplasm to the membrane surface. The water molecules then react with the negative part of the membrane causing dehydration of the bacteria surface. In embryonic granule formation, degradation of complex organic compounds by acidogenic bacteria provides the substrates for acetogens and methanogens, leading to

iii ivi ii iii ivi ii

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bacteria multiplication. Microorganisms self-adhere due to external hydraulic forces acting on them to form embryonic granules, which promote the excretion of ECP. Once embryonic granules are formed, dispersed particles may be attached to them leading to granule maturation. Acidogenic, acetogenic, and methanogenic bacteria predominate and block the reproduction of other kinds of microorganisms. Spaces within the granule during maturation are used for microorganism growth and for substrate availability, facilitating the production of ECP in large quantities. ECP protects the granules against shear stress in the reactor. Granules are formed in the post-maturation stage. The mature granule is maintained in a relatively hydrophobic state thanks to the proton translocation activity.

Figure 11. Tay et al. model: i-Dehydration of Bacterial Surfaces; ii-Embryonic Granule Formation; iii-Granule Maturation; iv-Postmaturation. (Adapted from Tay et al. 2000).

The multi-valence ion-bonding model: This model is based on the electrostatic interaction between negatively and positively charged bacteria. As illustrated in Figure 12, bacteria are negatively charged under normal pH conditions, so that the introduction of a multi-valent positive ion favours the attachment between bacteria. The ions can be calcium, aluminium, magnesium or ferric iron (McHugh et al. 2003, Liu et al. 2003).

bacterium bacterium

H+

Hyd

ratio

n la

yer

Cyt

opla

smic

mem

bran

e W

ater

mol

ecul

eC

ell

wal

l

Res

pira

tion

chai

n

bact

eria

i ii ii

iii

iv

bacterium bacterium

H+

Hyd

ratio

n la

yer

Cyt

opla

smic

mem

bran

e W

ater

mol

ecul

eC

ell

wal

l

Res

pira

tion

chai

n

bact

eria

bacterium bacterium

H+

Hyd

ratio

n la

yer

Cyt

opla

smic

mem

bran

e W

ater

mol

ecul

eC

ell

wal

l

Res

pira

tion

chai

n

bact

eria

i ii ii

iii

iv

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Figure 12. Multi-valent ion-bonding model (Adapted from Liu et al., 2003). 3.2 MECHANISM OF GRANULE AGITATION INSIDE A UASB REACTOR The upflow velocity and rising biogas bubbles (Figure 13) are the principal factors causing mixing in the reactor (Seghezzo 2004, Das and Chaudhari 2009). Mixing in the sludge bed induces shear forces which are the key factor influencing the formation, stability and structure of the anaerobic granules. Agitation developed in sludge bed due mostly to upward movement of biogas and particle-to-particle collision (Bhunia and Ghangrekar 2008). Fine biogas bubbles may adhere to the granule and cause the granule to rise (Narnoli and Mehrotra 1997).

Figure 13. Granules rising due to the upflow velocity and biogas attachment.

A long hydraulic retention time (HRT) is related to a low up-flow liquid velocity, and this may facilitate the growth of dispersed bacteria and be less favourable for granule formation. In contrast, a short HRT combined with a high up-flow liquid velocity can result in the washout of non-granulated microorganisms and thus promote sludge granulation. A fraction of the flocculent anaerobic sludge can be converted into an active granular sludge by handling the hydraulic stress and the ability of the anaerobic granules to settle (Liu and Tay 2004). Seghezzo et al. (2002) studied the effect of up-flow velocity and sludge discharge on the removal of solids in UASB reactors. In their pilot scale UASB reactor, the height of the sludge bed grew at a rate of only 8.6 cm per month, so the reactor needed to be discharged only once per year. TSS/VSS removal is inversely proportional to the up-

Opposite charge attraction Self-immobilization

Multi-valent positive ion Negatively charged bacterium

Granule rising

(biogas bubbles)

gran

ule

Bio

gas

bubb

le

Gra

nule

Wat

er fl

ow

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flow velocity, and this means that TSS/VSS is a good criterion for deciding the right moment for sludge discharge. When sludge discharge takes place, it causes a disturbance in the sludge bed. The discharge did not however affect the specific methanogenic activity in the reactor (Seghezzo et al. 2002). At low temperatures, the biogas production rate is low so that agitation due to rising bubbles is not sufficient to give good mass transfer between substrate and granules. A solution to prevent channelling and to have good biomass and wastewater contact could be to put a slow mixing device at the bottom of the reactor as Singh and Viraraghavan (2002) did in their studies of kinetic coefficients, but this increases the cost of operating the UASB reactor. Granule-to-granule collision influences the agitation in a UASB reactor (Bhunia and Ghangrekar 2008). Collisions may be caused by a variation in particle density, a pushing of the granule due to the up-flow velocity, granules being dragged by biogas bubbles, biogas entrapped inside the granule or settling. All these factors may be grouped under the heading of granule buoyancy. Buoyant particles are defined as those whose density is almost the same as that of the carrier fluid, which usually corresponds to a solid�–liquid two-phase flow system (Yang et al. 2007) (Figure 14).

Figure 14. a) Granule collision, b) Entrapped biogas Yoda and Nishimura (1997) studied the buoyancy of UASB granules. They reported that granules floated due to buoyant forces created by gas bubbles entrapped in the hollow spaces of the granules, as reported by MacLeod et al. (1990). Granules have large cavities in the granule core indicating that biogas is produced in the centre of the particle (MacLeod et al. 1990). According to Narnoli and Mehrotra (1997), the dragging of particles (granules) due to the attachment of gas bubbles is caused only by small (fine) gas bubbles.

Entrapped biogas

Granule

a)b)

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3.3 FACTORS INFLUENCING REACTOR PERFORMANCE 3.3.1 pH There are three principal bacteria involved in biogas production: bacteria responsible for hydrolysis, acid-producing bacteria, and methane-producing bacteria. The acid-producing bacteria commonly tolerate a low pH, but their optimal pH range is from 5.0 to 6.0, on the other hand, most methane-producing bacteria work better in a pH range of 6.7 to 7.4. If the reactor pH goes out of the 6.0 - 8.0 range, the activity of the methane-producing bacteria is reduced and this may negatively influence the reactor performance. The bicarbonate produced by the methane-producing bacteria normally controls the pH reduction caused by acid-producing bacteria (Liu and Tay 2004). 3.3.2 TEMPERATURE Methanogenic activity is seriously affected at temperatures below 30 oC. In the range from 37 to 55 oC, Fang and Lau (1996), have reported sludge washout and an inefficient COD removal. When the UASB reactor temperature is above 55 oC, the quality of the effluents is not as good as when the temperature is under mesophilic conditions. Additional energy is then needed to heat the reactor and this increases operation costs (Liu and Tay 2004). The UASB reactor must operate under mesophilic conditions (30 to 35 oC) for successful results (Fang and Lau 1996). However, Harada et al. (1996) reported that thermophilic conditions (i.e. 55 oC) lead to a successful treatment of vinasse. 3.3.3 ORGANIC LOADING RATE (OLR) The OLR is the mass of organic matter loaded per day per cross-section area of the reactor (Zhou and Mancl 2007). The degree of starvation of microorganisms in biological systems is dependent on the OLR. At a high OLR, microorganisms are subject to fast microbial growth (but intoxication may occur with high quantities of organic matter), whereas at a low OLR, microorganism starvation takes place (Liu and Tay 2004). A practical approach for the rapid start-up of a UASB reactor is to operate the system at a COD reduction of 80%, which can be reached by manipulating the OLR (Fang and Chui 1993). However, if the applied OLR is too high, the biogas production rate may increase, and the resulting strong agitation can then lead to washout of the inoculated sludge (Liu and Tay 2004). 3.3.4 HYDRAULIC RETENTION TIME AND UP-FLOW VELOCITY The hydraulic retention time (HRT) is the average time that the influent water remains inside the reactor (Bitton 2005), and the up-flow velocity is the liquid velocity crossing a transverse-cross section of the UASB reactor; its units are m3m-2h-1. The stagnant film around a granule can be reduced by increasing the up-flow liquid velocity. The goal is to reduce the mass transfer resistance in the stagnant liquid around the granule in order to increase the diffusion from the liquid phase into the

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solid (granule) and thus increase the degradation of the substrate and stimulate the microorganism growth. The up-flow water velocity usually ranges between 0.1 and 1.4 m.h-1 in a UASB reactor (Korsak 2008, Kalyuzhnyi et al. 2006). In a reactor treating wastewater, the sludge bed and the blanket have dispersed particles between which the wastewater passes and gas bubbles flow upwards. Therefore, even if the inlet fluid enters the reactor with a relatively slow velocity, the flow distribution is not uniform due to the presence of these granules and to the agitation produced by the gas bubbles. These phenomena determine the residence time distribution (RTD). In order to determine the hydraulic conditions in the reactor, several researchers have performed residence time distribution tests (Atmakidis and Kenig 2009, Levenspiel 1999, Borroto et al. 2003, Singh et al. 2006). Atmakidis and Kenig (2009) studied the resident time distribution in a fixed bed reactor using two methods: the tracer method and the post-processing method. The first involves the injection of a non-reactive tracer into the system and the second calculates the RTD directly from the local velocity field. These authors reported that the two methods gave similar results. The main advantage of the post-processing method is that it requires less computational time than the tracer method. Since the sludge bed and also the blanket are formed mainly of particles (granules) suspended in the flowing water, it is reasonable to expect that the up-flow velocity in the sludge bed is slightly higher than the up-flow velocity in the upper part of the reactor (Figure 15) since the granules reduce the space for water flow (The Venturi effect). The velocity increases in the space between particle and particle.

Figure 15. Upflow velocity in the sludge bed. There appears to be nothing in the literature about the change in velocity inside a UASB reactor (sludge bed). The velocity difference has perhaps been neglected. The up-flow velocity inside the reactor can however be calculated making some assumptions. As indicated above, the HRT is the average time that the water remains in the reactor. The HRT may then be calculated as the ratio of the volume of the reactor to the water flow rate (Qwater). However, the granules occupy a part of the space in the reactor, so that, the HRT should be calculated considering only the volume (Vwater) occupied by

Wastewater flow

Venturi effect

Wastewater flow

Venturi effect

Wastewater flow

Venturi effect

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the water in the reactor.

(1) The average water velocity in the reactor may then be calculated as the ratio of the reactor height to the HRT, assuming that the reactor has a constant cross-sectional area. As mentioned above, the concentration of granules is high in the lower part of the reactor, the sludge bed, and low in the upper part, blankets (Figure 16). The local water velocity at any level in the reactor may then be calculated as the ratio of the water flow rate to the cross-sectional area (Awater) available for the water at that level, i.e., the cross-sectional area of the reactor minus the area occupied by the granules. To show the consequences of this, a simple calculation was carried out. If the volume fraction of the granules at the bottom of the reactor is 0.20 and in the blanket 0.01, the cross-sectional area available for the water will be 0.80 and 0.99 of the reactor cross-sectional in the sludge bed and blanket respectively. The ratio of the water velocity at the bottom and the water velocity in the blanket will be 0.99/0.80; i.e., about a 20 % greater at the bottom.

Figure 16. Cross-section of the UASB reactor. a) Large and abundant granules in the lower part, b) smaller and fewer granules in the upper part.

3.3.5 SUBSTRATE Many types of wastewater can be fed into a UASB reactor; but, the wastewater should contain nutrients and dissolved organic material. For this reason, wastewater from the food industry is one of the most effective feeds for a UASB reactor. In some cases, sewage does not contain sufficient nutrients and it is necessary to add nutrients to create the right conditions to ensure the efficiency of the anaerobic reactor. Researchers have used UASB reactors for a large variety of wastewaters, from among others the synthetic textile industry, vegetable oil industry, methanol, phenol compounds, and municipal and paper industry wastewaters (Sponza and I ik 2002, Shastry et al. 2010, Nishio et al. 1993, Razo-Flores et al. 2003, Singh et al. 2006). It is possible to improve the ECP production by adding acidogenic bacteria during the start-up period if the feed contains low molecular weight carbohydrates. The acidogenic bacteria then produce a substrate for the methanogens and, as a

a) b)

Large granules Small granules

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consequence the digestion biogas is improved (Tay et al. 2000). 3.3.6 SLUDGE INOCULATION Any medium containing the appropriate microbial flora can be used as seed sludge for a UASB reactor (Tay et al. 2000). Granules grow in the presence of volatile fatty acids (acetate, propionate, and butyrate). In a mesophilic environment, the particles can be classified into three types according to the predominant acetate substrate that is utilized by the methanogens bacteria (Hulshoff et al. 1983, De Zeeuw 1984):

�• Rod-type granules, which are mainly composed of rod-shaped bacteria in fragments of four to five cells, like Methanothrix.

�• Filament-type granules, which consist predominantly of long multicellular rod-shaped bacteria.

�• Sarcina-type granules, which develop when a high concentration of acetic acid is maintained in the reactor.

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4- KINETICS MODELS The kinetics describe the reaction rate with which a microbial population converts substrate into biogas using as feed the organic material present in the wastewater. Enzyme fermentation (cannot reproduce itself) is the degradation of substrate by an enzyme (acting as catalyst), whereas microbial fermentation (can reproduce itself) is the conversion of substrate to biogas by microbial cells which also results in the generation of new cells (Levenspiel 1999). UASB kinetics parameters are often determined in a batch reactor, in laboratory, pilot or industrial environment. Granules (anaerobic microorganisms) are set in a batch reactor (e.g. a test tube or a cylinder) containing wastewater (synthetic or real). The kinetics can be divided in four phases (Figure 17) (Bitton 2005): The lag phase, also known as the acclimatization stage: This is the adaptation phase where microorganisms become accustomed to the new substrate, which occurs when microorganisms are used to degrade organic material present in a substrate different from that to which they are accustomed. The duration of lag phase depends on factors such as cell age and culture medium. If the microorganisms are transferred to a similar medium, no lag phase is observed. The exponential phase, also known as the Log phase: This is the period when the microorganism population grows exponentially. Typically the microorganism splits into two, generating two microorganisms (daughter organism). These two microorganisms then generate four microorganisms and so on, until a steady state is reached as explained below. The exponential growth depends on the type of microbial population and on parameters such as temperature and wastewater composition. The steady-state phase: This is the stationary phase where the microorganism population stops growing, when the rate of microbial population growth is practically the same as the rate of microbial population death. The death phase: This is the phase where the death rate is higher than the growth rate, so that the active microorganism population disappears. This can occurs due to the generation of toxic compounds (that provoke microbial death), a lack of nutrients (microorganisms become predators), a lack of electron acceptors (for respiration) or lysis.

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Figure 17. Microorganism growth phases. Several kinetics models for predicting the microorganism growth may be found in the literature, and most of them assume the substrate consists of a single soluble substance. The most common model is presented and explained in the following section. 4.1 THE MICHAELIS-MENTEN MODEL A kinetic model was derived by Michaelis and Menten in 1913 (Richardson and Peacock 1994). It is often used for enzymatic fermentation, where the enzyme does not reproduce itself but merely acts as a catalyst (Levenspiel 1999), but the model has also been applied to the kinetics of anaerobic reactors (Feng 2004, Wendland 2008) including the UASB reactor. Zandvoort et al. 2004 determined kinetics constants based on the Michaelis-Menten model in a study of methanol degradation in a laboratory-scale UASB reactor. The Michaelis-Menten model predicts the rate of reaction and it includes an inhibition constant Zandvoort et al. 2004:

R = rmaxS

Km + SX I (2)

Where R is the reaction rate, maxr is the maximum specific uptake rate of the process, S is the substrate concentration, mK is the Michaelis-Menten constant, X is the microorganism concentration and I is the inhibition constant. The Michaelis-Meneten constant is equal to the substrate concentration when the specific reaction rate is half the maximum specific reaction rate. A schematic Michaelis-Menten curve is shown in Figure 18.

Log

phas

e

Lag

phas

e

Stationary phase Death phase

Mic

roor

gani

sms

Num

ber

Time

Log

phas

e

Lag

phas

e

Stationary phase Death phase

Mic

roor

gani

sms

Num

ber

Time

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Figure 18. Michaelis-Menten curve. The reaction rate is the quantity of substrate consumed, or product formed, per unit time. The kinetics parameters rmaxand Km may be determined by fitting the experimental data. 4.2 THE MONOD MODEL The most frequently used kinetic model is the Monod model, developed by Jacques Monod in 1942. The model describes the growth of the microorganism with time, with respect to the nutrient or substrate concentration. It is an empirical equation that states that the growth rate is zero when there is no substrate and that it reaches a maximum when there is excess of substrate (Lorby et al. 1992). The Monod equation for microorganism growth is:

XSK

SdtdX

S += maxµ (3)

where, X is the microorganism concentration, maxµ the maximum specific growth rate, S the substrate concentration, the substrate concentration when the specific growth rate is half the maximum specific growth rate, and t is time. Figure 19 shows a Monod curve as well as the meaning of and maxµ .

Substrate Concentration

Sp

ecif

ic r

eact

ion

rat

e

rmax

Km

½ rmax

SK

SK

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Figure 19. Monod curve, is the specific growth rate. According to Monod curve, the microorganism growth rate is exponential at the beginning. However, with time, the growth rate may decrease due to a lack of availability of substrate or nutrients or to other problems such as accumulation of toxic metabolites (Krylów 2003), predation (protozoa are predators of bacteria) (Bitton 2005) and lysis (Kalyuzhnyi et al. 2006). A decay constant, Kd, is therefore introduced into the Monod Equation and the amount of microorganisms in the reactor may reach a steady state. If the conditions are unfavourable, the amount the microorganisms may in fact decrease. Kd is a term used by several researchers in UASB models; e.g., Vlyssides et al. (2007), Kalyuzhnyi et al. (2006), Blumensaata and Keller (2005), Sponza (2001), and Seghezzo (2004). Kd is a function of the operating conditions. Taken into account the Kd, the Monod equation for microorganism growth is:

XKSK

SdtdX

dS +

= )( maxµ (4)

4.3 THE CONTOIS MODEL The Contois model describes the growth of microorganisms with time, and it was developed about 20 years after the Monod model. The principal difference between these two models is that the Monod model considers that the limiting concentration of substrate KS (when the growth rate is half the maximum growth rate,) is independent of the population density, whereas the Contois model says that KS is a function of population density (Contois 1959). The Contois equation including the decay constant for microorganism growth is:

XKSXB

SdtdX

d+= )( maxµ (5)

where B is a kinetic growth parameter [kg COD (kgVSS)-1]. The Contois model is based on the Monod model, but it is an improved microbial growth model (Contois 1959). I ik and Sponza (2005) studied the kinetics in a laboratory-scale UASB reactor and compared several kinetics models including the Contois and Monod models.

Substrate [mg.L-1]µ

[tim

e-1 ]

µmax

KS

½ µmax

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They reported that both models have the same sensitivity, in order to obtain the kinetic coefficient, from Monod, and from Contois. Several researchers have compared the Contois and Monod models, but there is no clear consensus as to which model is the better. Bhattacharya and Khai (1987) report that the Contois model is better for modelling cow dung digesters. Kry ów and Tal-Figiel (2003) on the other hand say that in order to describe the kinetics for low strength municipal wastewater of individual stages of process such as substrate degradation, VFA behaviour, and microbial growth; it is more convenient to use the Monod model. Hu et al. (2002) studied the kinetics of anaerobic digestion using synthetic ice-cream wastewater and they reported that the Contois equation is more adequate than the Monod equation for describing the kinetics of the process. Sponza and Uluköy (2008) compared three models (Monod, Modified Stover-Kincannon, and Grau second order) in order to determine which of these was most suitable for predicting the substrate removal kinetics of a UASB reactor. They found that the Monod model is suitable for predicting the degradation kinetics for both glucose and 2,4-dichlorophenol. Other models that have been used are the Moser, Edwards and Logistic models for predicting the microbial growth rate (Richardson and Peacock 1994). There is no best kinetics model. The appropriate model depends on the microbial characteristics and on the reactor operating conditions.

SK

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5- MODELS DESCRIBING UASB BEHAVIOUR Models describing the processes that occur in a UASB reactor are few. Some of them are very complex and include several processes. These models require many parameters, which have to be determined or assumed. Due to their complexity, these models are difficult to apply (Batstone et al. 2002, Kalyuzhnyi et al. 2006, López et Borzacconi 2009). It is difficult to develop models to predict what is happening in a UASB reactor since the processes in the reactor are strongly coupled, and the parameters are linked within the system (e.g., granule shape, types of microorganism, processes in the granule, water residence time in the reactor, etc.). There are many types of models, which may be classified into two extreme groups. In one class of models, correlations are obtained for a given reactor and the results are used to optimize the reactor or other reactors working under similar conditions. The other models, in which we are interested, are phenomenological models based on the processes taking place in the reactor. To develop an acceptable model, it is important to bear in mind the well-known fundamental axioms of model simulations (Jeppsson 1996): Do not fall in love with the model; analyse the model critically (Figure 20,a). Do not try to adapt reality to the model (Figure 20,b). Do not extrapolate the area of validity of your model too far (Figure 20,c).

Figure 20. Representation of the model�’s axioms (adapted from Jeppsson 1996) 5.1 EXISTING MODELS OF A UASB REACTOR A few models have been developed to explain the behaviour of a UASB reactor. Some of these, which may be relevant in our case, are explained below. Wu and Hickey (1997) considered the UASB reactor to be divided into a non-ideal continuously stirred tank reactor (CSTR) followed by a plug flow reactor (PFR). The CSTR represents the sludge bed and the PFR the clarifier zone above the sludge bed (i.e. the blanket). In the model, they introduced a bypass from the inlet to the outlet of

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the reactor, to take into account the dead volume found in the reactor since the sludge bed is considered to be a non-ideal CSTR (Figure 21). Based on this, Wu and Hickey (1997) proposed a dynamic model including reactor hydraulics, diffusion and reaction following the Monod growth model. Basically, the dynamic model describes the variation in substrate concentration within the granule and the variation in substrate concentration in the PFR (clarifier or blanket zone). In the PFR stage, the reaction term is not taken into account because the reaction in the clarifier is considered to be negligible. To test the model, they performed laboratory experiments using a laboratory-scale UASB reactor. They first started up the UASB reactor until it reached a steady state, and they then injected a tracer of LiCl at the inlet and monitored it at the outlet. Similar experiments were carried out injecting acetate. The LiCl tracer was used to study the reactor�’s hydraulics and the acetate tracer was used to study the degradation of the substrate and to test the dynamic model. These authors reported that the dynamic model represented the flow within the UASB reactor well despite the fact that disturbances within the UASB reactor were observed. Granules clogged in the gas collector and this affected the experimental work. When this problem was solved, they observed a reduction in the sludge bed uniformity and a decrease in the dispersion. On the other hand, the behaviour of the acetate in the model was similar to the experimental observation.

Figure 21. Schematic model proposed by Wu and Hickey. (Adapted from Wu and Hickey, 1997)

Narnoli and Mehrotra (1997) found that the blanket zone of the UASB reactor was extremely important for the performance of the reactor, and they therefore studied the forces acting on the solid particles and the equilibrium between these forces, and the solid distribution as a function of blanket height in the reactor. Their results showed a major solid concentration at the bottom of the blanket, and a lower sludge concentration in the upper part of the reactor. They did not study the solids distribution in the bed zone because, in their case, the bed was composed of large granules which formed a uniformly packed bed. Figure 22 shows the sludge concentration as a function of height in the reactor.

CSTR PFR

Bypass

Inlet OutletCSTR PFR

Bypass

Inlet Outlet

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Figure 22. Sludge distribution as a function of height in the reactor (Adapted from Narnoli and Mehrotra 1997)

Gomes et al. (1998) developed a model to describe the fluid flow characteristics of a UASB reactor. They divided the UASB reactor into two zones, the sludge bed zone and the blanket zone (Figure 23). They assumed that the flow patterns in the two zones corresponded to axially dispersed zones. They considered that a part of the liquid (wastewater) bypasses zone 1 and enters directly into zone 2. The model takes into account dispersion and flow. To obtain experimental data, a lithium tracer was injected and monitored in a UASB reactor. Experimental and theoretical data were compared, and a good correlation was found between them. According to Gomes et al. (1998), dividing the UASB reactor into two zones is a suitable way of describing the fluid flow characteristics without any loss of accuracy in the predictions and their interpretations. They also reported that dividing the UASB reactor into two zones instead of the three typical zones reduces the number of variables.

Figure 23. Schematic model proposed by Gomes et al. (Adapted from Gomes et al., 1998)

Although UASB reactors do not need inert material for granule development, some researchers have added such material to achieve faster granule growth. The experimented materials include synthetic and natural polymeric material, clay, sand, and polyvinyl alcohol (PVA)-gel beads (Yu et al. 1999, Lettinga et al. 1980, Wenjie et al. 2008). In 2001, Pritchett and Dockery proposed a steady state solution for a one-dimensional biofilm model that could be applied to UASB reactors. Their model consists of flat surface inert materials onto which microorganisms are attached. The attached microorganisms consume substrates and form a biofilm that grows until it reaches a steady state. The model assumes that the biofilm consists of a single species, but it takes into account both the active and inactive biomass. Pritchett and

Transition

Bla

nke

tB

edSludge Concentration0

Biogas

Inlet

Zone 1 Zone 2

Bypass

Inlet OutletZone 1 Zone 2

Bypass

Inlet Outlet

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Dockery also assumed that the biofilm grows only in the direction of the water-film interface. The model includes a diffusion term and it is based on a mass balance for substrate in a small control volume (Figure 24). To test the model, they used literature data and they concluded that a steady state solution is reached only if detachment (liberation of biomass) occurs. Otherwise the biomass thickness grows continually, and this is not physically possible.

Figure 24. Biofilm model (Adapted from Pritchett and Dockery, 2001) Wu et al. (2005) applied the axial dispersion model proposed in 1998 by Gomes et al. to a laboratory-scale UASB reactor with an injection of Rhodamine as tracer. The Rhodamine concentration was measured at different heights in the sludge bed to study the fluid flow characteristics. The model does not account for the reaction term, since the study focuses on the hydrodynamics of the system, through the behaviour of the conservative tracer. The model was solved by using an orthogonal collocation algorithm. Wu et al. (2005) found that the dispersion term is essential to describe the fluid flow distribution of the tracer. To obtain a good match between experimental and theoretical data, the authors made the assumption that the dispersion is practically linearly dependent on the upflow velocity at all levels in the sludge zone. This assumption is based on the work of Krishnaiah et al. (2003) who evaluated correlations between the Péclet number obtained using the axial dispersion model and Péclet numbers reported in the literature. The experimental set-up was a turbulent bed contactor (TBC). The work of Wu et al. (2005) is an improvement on that of Gomes et al. (1998). Although the model proposed by Wu et al. (2005) does not take into account the reaction term, it gives a better understanding of the importance of dispersion in the study of reactors like the UASB reactor. Kalyuzhnyi et al. (2006) developed a model applicable to the solid, liquid and gaseous components in a UASB reactor. Focused on the solid phase (granular sludge), the solution of the model leads to complications in the determination of both the terminal vertical velocity and the dispersion coefficient of the sludge aggregates. To solve the problem, Kalyuzhnyi et al. determined the settling velocity of granules based on Stoke�’s law. The problem thus lay in the determination of the viscosity since the suspension in a UASB reactor acts as a non-Newtonian liquid due to the presence of the granules. To calculate the viscosity, they used an empirical equation directly taken from Darton�’s review (Kalyuzhnyi et al. 2006). The dispersion coefficient was determined using the equation proposed by Narnoli and Mehrotra (1997), which meant that some constants in empirical equations were used.

Inert material Biofilm

)t(

Bulk LiquidVfilm

z-axis

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The dispersion coefficient for the soluble components was the same as that calculated for the solid components neglecting the settling velocity of the granules. The gaseous components were treated as solutes, and the mass transfer coefficient was determined using the empirical equation reported by Zhukova in 1991 (Kalyuzhnyi et al. 2006). In the biological field, the model followed the Monod model with pH control, which means that the growth of microbial organisms followed the Monod model as a function of the pH. To validate the model, Kalyuzhnyi et al., (2006) used data from the literature. It is remarkable that many empirical equations were used in this model. They reported a total of 35 variables in their model, which decreases the reliability of the model. Based on a mass balance for a UASB reactor, Korsak et al. (2008) developed a one-dimensional model to describe the processes occurring in the reactor. The model is transient and takes into account the degree of mixing, the water flow and the biological degradation of the substrate. The kinetics follows the Monod model. They deduced an expression for the reaction rate constant and took a value for the reaction rate from the literature to test the model. Their results showed that excessive mixing in the bed zone reduces the efficiency of the system due to a back-mixing effect. They also reported that larger particles diameters do not mean faster reaction rate, since in large particles the degradation of substrate only take place close to the surface of the granule, so that the substrate degradation rate is reduced. In an earlier work (Rodriguez and Moreno 2009), we proposed a model describing both substrate degradation and microorganism growth in a UASB reactor with time. In this model, the reactor was divided into several small reactors with the same volume. The model takes into account the decay of microorganisms, the fraction of microorganisms carried out by the effluent, and the convective and reaction terms. The dispersion is accounted for in the number of small reactors. Figure 25 shows the UASB reactor divided into several small reactors (e.g. six small reactors). The kinetics follows the Monod equation with regard to both substrate degradation and microorganism growth. The model is able to predict the behaviour of a laboratory-scale UASB reactor using values taken from literature.

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Figure 25. UASB reactor divided into six small reactors, qi: Flow through the reactor; Xi: Microorganism concentration; Si: Substrate concentration; wi: washed fraction.

A global anaerobic digestion model (ADM1) was developed by a group of researchers, which may be applied in various anaerobic processes depending on its characteristics (Batstone et al. 2002). Zaho et al. (2010) reported that the ADM1 is not suitable for describing the start-up behaviour of the UASB reactor due to the possible existence of non-constant kinetics parameters in the exponential phase. For this reason, Zaho et al. (2010) proposed a model linking principles of transport phenomena and the ADM1 model to describe the start-up period of a UASB reactor. Their model assumes that the kinetic parameters (hydrolysis rate and specific uptake rate coefficients) increase with time during the reactor start-up. There is a linear relationship between the increase in the rate coefficients and the driving force for these increases, and they therefore applied the linear phenomenological equation (LPE) to describe the variation of each rate. Their model was tested with experimental data obtained from a laboratory-scale UASB reactor fed with soybean wastewater and with data taken from the literature, and they reported that their model predicts the start-up of an anaerobic system. However the model is not suitable for predicting the behaviour in the case of wastewater containing sulphur compounds and toxic substances. In 2001, Bernard et al. developed a model considering the microbial population of the anaerobic digestion comprising two steps (acidogenesis and methanization). The model is based on the biological reactions of acidogenic and methanogenic bacteria. The reactions involving acidogenic and methanogenic bacteria follow the Monod model, but the methanogenic process takes into account the inhibition constant, since the products of the acidogenic bacteria are the substrate for the methanogenic bacteria. This work also identified the parameters linked with the anaerobic process (i.e. yield). López and Borzacconi (2009) reported that it is difficult to apply ADM1 in a UASB reactor because ADM1 involves a lot of variables (i.e. at least 26 dynamic variables and many parameters). They therefore improved the model proposed by Bernard et al. (2001) for a full-scale UASB reactor. This new model, which includes new

Biogas output

Effluent

Influent

qi ; Xi*wi; Si

qi ; Xi-1*wi-1; Si-1

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parameters representing the solid fraction leaving the reactor, was denoted AM2. López and Borzacconi (2009) indicated that a proper steady-state is difficult to reach in a full-scale reactor due to disturbances in the inlet flow. For this reason, it is very difficult to determine the acidogenic and methanogenic biomass concentrations in the reactor by using a kinetic expression. The amount of these bacteria, as well as of the organic substrate and the VFA production, can be determined if the stoichiometric coefficients are know by applying the concept of Luenberger (state observer). The UASB reactor behaviour will then be known.

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6- PRELIMINARY MODEL As a preliminary work, Rodriguez and Moreno (2009) developed a model describing substrate degradation and microorganism growth in a UASB reactor. To describe the UASB performance, the UASB reactor is represented as n-small well-stirred reactors in series (Figure 25). The i-reactor is shown in Figure 26, where q is the flow through each reactor [Ld-1], X is the microorganism concentration [mgL-1], and S is the substrate concentration [mgL-1]. All reactors have the same volume, V/n, where V is the active volume of the UASB reactor and n the number of small reactors in the model. w is the fraction of microorganisms that is washed out from one reactor into the next one.

Figure 26. Scheme of i-reactor Initially, the small reactors have given concentrations of substrate (Sinitial) and microorganisms (Xinitial). The influent flow-rate is q with a substrate concentration of So free of microorganisms. The emphasis of this preliminary model was set on the substrate degradation and on the growth of microorganism (i.e. from the start-up of the reactor until a steady state is reached). This may be expressed by a mass balance for the substrate degradation and microorganism growth in the i-reactor:

(6)

(7)

where and represent the reaction terms for substrate degradation and microorganism growth respectively. Assuming that the growth of microorganism (i.e. the kinetics) follows the Monod model (Eq. 4), the mass balance may be written as:

(8)

qi, Xi wi, Si

qi, Xi-1 wi-1, Si-1

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(9)

where is the yield coefficient, expressing the mass of biomass produced per mass of substrate degraded. is a constant that takes into account the rate at which the microorganisms disappear [d-1]. In Equation (8), the term on the left-hand side is the accumulation term, the first term on the right-hand side is the advective term and the second is the reaction term. In Equation (9), the first term on the right-hand side is the amount of microorganisms that are washes out from the small reactors. The second term describes the growth and death of microorganisms. The dispersion in the UASB reactor is accounted for by the number of small reactors used in the system. A small number of reactors indicate a large dispersion. To solve the governing equations of the model, the MATLAB program was used. Experimental data was taken from the literature to test the model. 6.1 STUDY CASE FOR THE PRELIMINARY MODEL Sponza and Uluköy (2008) used a stainless steel UASB laboratory-scale reactor (Figure 27) to treat synthetic wastewater containing glucose. The UASB reactor had an effective volume of 2.5 L with a height of 100 cm and an internal diameter of 6 cm. The temperature was maintained at 37 oC. The UASB was inoculated with settled partially granulated anaerobic sludge taken from a Yeast factory in Izmir.

Figure 27. Representation of the UASB reactor used by Sponza and Uluköy.

(Adapted from Sponza and Uluköy, 2008). The glucose concentration in the synthetic wastewater was 3000 mg L-1. The total operation time was 96 days with 6 different HRTs, varying from 2 h to 20 h. Table 1 shows data reported by Sponza and Uluköy (2008).

The scope of the Sponza and Uluköy (2008) study was to compare three kinetics models to determine the substrate removal in the laboratory-scale UASB reactor. The feed solution consisted of glucose as the primary carbon source. The kinetics models studied by Sponza and Uluköy were: the Monod, the Second-order Grau kinetics and

Inlet

Biogas

Outlet

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the Modified Stover-Kincannon. They conclude that the Monod model can be used to predict the performance of glucose degradation (as COD) in a UASB reactor.

Table 1. Kinetics parameters (Sponza and Uluköy, 2008). Parameter Symbol Units Values

Reactor volume V (L) 2.5

Inlet substrate concentration S0 (mg L-1) 3000

Yield Y (mg VSS [mg COD]-1) 0.000780

Decay rate Kd (d-1) 0.093

Max. Specific growth rate µmax (d-1) 0.21

Monod constant Ks (mg L-1) 561

Initial biomass concentration Xinitial (mg L-1) 5

Initial substrate concentration Sinitial (mg L-1) 3000

6.2 RESULTS AND DISCUSSION (PRELIMINARY MODEL) The model was run for two flow rates (3 and 9 L d-1) corresponding to an HRT of 20 h and 7 h respectively. The washout fraction was assumed to be 0.002. Figures 28 and 29 show the model response for a flow rate of 3 L d-1.

Figure 28. Substrate concentration versus time Figure 29. Active biomass concentration

versus time. Figure 28 shows the COD degradation in the six small reactors in the model. In reactor 1 at the bottom of the UASB reactor the COD concentration is reduced by 83%, and the total removal of COD in the reactor was 96% when steady state was

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reached. Sponza and Uluköy (2008) reported a reduction of 83%. This difference may be due to the different operation times; the steady state was reached in 12 days in their case, whereas in our case it was not reached until after 40-50 days. The microorganism behaviour is shown in Figure 29. In reactor 1, the active biomass grew at a faster rate due to the larger amount of substrate that was degraded in that reactor. The faster degradation rate was due mainly to the high substrate concentration (Equation 8). The biomass growth rate was lower in the other reactors because a part of the substrate had been degraded in the reactors near the inlet. The steady state was reached after 50-60 days. Figure 30 and 31 show the substrate and active biomass performance for a HRT of 7 hours, the flow rates then were being 9 L d-1. The behaviour was similar to the case with a flow rate of 3 L d-1. The substrate concentration decreased until a steady state was reached and the active biomass increased, but with the greater flow, it took longer to reach a steady state. This delay may be because the water flow drags a larger quantity of biomass from one reactor to the next. A larger concentration of biomass was found in the reactor, as shown in Figure 31.

Figure 30. Substrate concentration versus. Figure 31. Active biomass concentration time. versus time In this model, it is assumed that the degradation of the substrate produces CO2 and CH4 directly by the action of the bacteria, but this is a very complex process involving different populations of bacteria and with the reaction occurring along several paths. At present there are no data available to enable a model to be developed taking into account all these reactions. The model has been successfully used to simulate the performance of the UASB reactor studied by Sponza and Uluköy (2008). With this type of model it is possible to study the behaviour of the reactor with time. The model includes substrate degradation, biomass growth and death, and the washout of microorganisms.

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7- THE MAIN MODEL 7.1 MODEL DEVELOPMENT In order to model the main processes occurring in the UASB reactor, the following assumptions or simplifications are made: �• The processes depend only on the height in the reactor (one dimensional model).

The processes in any given cross-section of the reactor are considered to be uniform.

�• The substrate is a single biodegradable substance. �• The particles or granules are spherical. The granule size is constant at a given

height in the reactor, but decreases with the increasing height in the reactor. �• The temperature is constant. �• The kinetics follows the Monod model. It is assumed that the degradation within

the granule is controlled by a single process; for example, the hydrolysis of the organic material. Acetate and methane production are considered to be rapid.

7.2 KINETIC RATE CONSTANT BASED ON THE MONOD MODEL In chapter 4, the Monod model was presented and Equation (4) describes the change in amount of microorganisms with time. It includes the decay rate constant. The degradation of substrate and the growth of the microorganisms are directly linked by the yield , which is defined as the mass of biomass produced per unit mass of substrate degraded (Seghezzo 2004)

(10) The change in amount of substrate with time following the Monod model is therefore:

(11) 7.3 THE GOVERNING EQUATIONS ALONG THE HEIGHT OF THE REACTOR Figure 32 shows a schematic representation of the UASB reactor; a mass balance is done to describe the substrate degradation and microorganism growth as a function of height in the reactor with time, where the upflow velocity is [m3 m-2 h-1], the height of the reactor is H [m] and the cross section area is A [m2]. A mass balance along the reactor gives:

(12)

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Figure 32. Schematic representation of the UASB reactor The term on the left-hand side of Equation (12) is the accumulation of substrate in [kg m-3 h-1] in a differential element in the bed. The first term on the right-hand side is the dispersive term, the second term is the transport of substrate by advection and the third term is the reaction term. [h] is time, z [m] the vertical distance, [m2 h-1] the dispersion coefficient, and [h-1] is the reaction rate constant. The initial substrate concentration in the reactor is . The substrate enters the reactor with a velocity q and concentration ; i.e., with a mass flux of . At the outlet, the substrate leaves the reactor only by convective transport. Hence, the following initial and boundary conditions for Equation 12 may be written, I.C.: At ; (13) B.C.1: At ; (14)

B.C.2: At ; (15) Assuming that microorganism growth in the UASB reactor follows the Monod model, Eq. (12) can be rewritten as

(16) and the expression for microorganism behaviour is

Biogas

Outlet

Inlet (St=0)

H

0

Sz+ z q

Sz qz

z-ax

is

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(17) In Equation (17) the advective term is neglected due to the fact that the biomass remains inside the reactor. The parameter includes the decay term and the fraction of microorganism that leaves the reactor by washout , the washed-out fraction of microorganism corresponds to the fraction of biomass present in the effluent of the reactor. Experimental work has reported the significant presence of solids in the effluent at start-up (Yan et al. 1989; Kalyuzhnyi 2001; Cavalcanti 2003; Kumar et al. 2007). This washout reduces the volatile suspended solids (VSS) concentration in the sludge bed during the start-up of the reactor (Mu et al. 2007). However, under steady-state conditions, the washout fraction can be neglected in the model (Seghezzo et al. 2002). These authors studied the effect of sludge discharge in a pilot scale UASB reactor with about seven years of operation. Granules were already formed and, despite a sludge discharge, they reported no significant washout in the output of the reactor. 7.4 KINETIC RATE CONSTANT BASED ON A MASS BALANCE Biotransformation of substrate occurs inside the particle (granule) where there are three main microbial populations: acidogenics, acetogenics and methanogenics (MacLeod et al. 1990). The granules are assumed to be spherical. They can be seen as porous biocatalysts media (Figure 33) and, as a product of their digestion, they produce biogas and new cells. This causes an increase in granule size.

Figure 33. Granule as porous biocatalyst media For the biotransformation of substrate, there are three resistances in series: for the transport of substrate through the stagnant film around the granule, for the transport by diffusion within the granule, and for the degradation reaction. However, in order to keep the model simple it is assumed that the degradation within the granule is controlled by a single process; for example, the hydrolysis of the organic material. The other processes are considered to be rapid. Moreover, since the variations in substrate concentration in the granule with time are small with respect to the amount of degraded substrate, a steady-state mass balance may be applied for the

R

Liquid phase (S)

Substrate diffusion

x

y

Stagnant film

SP

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concentration of substrate in the granule,

(18) where [kg m-3] is the concentration of substrate in the pore water within the granule, [m2 h-1] is the effective diffusion coefficient of substrate within the granule, [h-1] is the substrate volumetric conversion rate, and r [m] is the radial distance from the granule centre. The transport of substrate into the granule is determined by the convective transport at the granule surface given by

, so that the boundary conditions may be written: at the granule surface (r=R) (19) at the granule centre (r=0) (20) Equation (18) is solved for a substrate concentration (S) in the bulk liquid [kg m-3] and for a mass transfer coefficient km [m h-1] for the substrate through the stagnant wastewater film around the granule. The solution of the ODE (Bird et al. 2002) is:

(21)

Applying the boundary conditions, the substrate concentration within the interior of the granule may be written as

(22)

where is a dimensionless number called the Thiele Modulus (Levenspiel,

1999). The mass flow of substrate into the granule, may then be calculated as

(23) The amount of substrate degraded per unit of volume of reactor may be calculated considering the volume fraction occupied by the granules . Since the reaction rate in Equation 12 is , the kinetics rate constant [h-1] in Equation (12) may be determined as:

(24)

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7.5 KINETIC RATE CONSTANT IN THE DEVELOPED MODEL In this work, the kinetic rate constant had been determined based on the Monod model as well as on a mass balance of substrate within the particle. From Equation (12) and (24), it is possible to establish the following equality for the kinetic constant:

(25) Equation (25) may be applied assuming that the substrate concentration is not too high, i.e., the Monod model is in the linear interval. If the substrate concentration is high, some deviation in the behaviour may be expected at locations near the bottom where the substrate concentration has its highest value. Therefore, the governing equations for substrate degradation and microorganism growth may be rewritten as functions of the kinetic constant as in Equations (26) and (27) respectively:

(26)

(27) 7.6 MODEL APPLICATION Harada et al. (1996) carried out an experimental study to investigate the feasibility of using a thermophilic UASB reactor to treat vinasse; i.e. residual water from the ethanol production process. The study focused on the degradation of organic matter (as COD) present in the vinasse (wastewater) from an alcohol distillery. The UASB reactor used for the experimental study had a volume of 140 L, and the operation time was 430 days. The reactor height was 4 m with an inner diameter of 20 cm. The UASB reactor contained a jacket to maintain thermostatic conditions (i.e. 55 oC). Figure 34 shows a schematic representation of the bioreactor used by Harada et al. (1996).

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Figure 34. Representation of the UASB reactor used by Harada et al. (Adapted from Harada et al. 1996).

The UASB reactor used by Harada et al. (1996) had an inclined gas-solid separator (GSS) in order to reduce the fraction of solid washed out. The reactor was seeded with 87 L of thermophilic sludge collected from a digester in the same alcohol distillery. The volatile suspended solids (VSS) concentration of the inoculated sludge was 12 000 mg L-1. The raw vinasse had a COD of 120 g L-1 but, to carry out the experiment, Harada and co-workers diluted the raw vinasse to a concentration of 10 g L-1 with the objective of having a constant amount of organic matter to feed into the UASB reactor. In the simulation the UASB reactor was assumed to be only vertical, despite the fact that Harada et al. (1996) installed an inclined GSS at the top of the reactor. To solve the governing equations [Equation (26) and (27)] in the model, COMSOL Multiphysic 3.5 was used and some parameters were assumed or calculated based on Harada et al. (1996). The initial particle size was assumed and the kinetic rate constant was determined by applying a mass balance for the substrate within the granule. The granule size distribution along the reactor was determined for each time step using the amounts of microorganism present in the reactor at different locations. The substrate corresponds to the organic matter, which is measured as COD. The initial and inlet substrate concentrations are assumed to be the same. Harada et al. (1996) applied seven different HRTs during the experiment. In the present case, the HRT applied was in the range of those applied by Harada et al. (1996). Table 2 shows the main parameters employed in the simulations:

Outlet

Inlet

Inclinedplates

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Table 2. Parameters applied in the model. Parameter Symbol Units Values Reactor height H [m] 4 Reactor radius R [m] 0.10 Hydraulic retention time HRT [h] 32 Yield coefficient Y [kg VSS (kg COD)-1] 0.03 Upflow velocity Q [m3 m-2 h-1] 0.12 Initial granule radius R0 [m] 0.0012 Decay constant Kd [h-1] 2.08E-4 Initial substrate concentration S0 [kg m-3] 10 Initial biomass concentration X0 [kg m-3] 12 Washout fraction w [-] 0.0008 Mass transfer coefficient km [m h-1] 0.0012

Regarding the results from the model simulation, the response for substrate degradation and microorganism growth are shown in Figures 35 and 36 respectively. Figure 37 shows the radius of the granule as a function of the height in the reactor. The model was running for a period of 400 days. Most of substrate was degraded at the bottom of the reactor. The substrate concentration decreased continuously with increasing height in the reactor, reaching a value about 1.7 kg m-3 at the reactor outlet at the top. The substrate concentration at the bottom directly after entering the reactor is lower than the inlet substrate concentration. This is due to the dispersion effect inside the reactor.

Figure 35. Model response for the substrate concentration profiles at different operating times (hours)

Harada et al. (1996) reported a maximum COD reduction of 60%, with an operation time of 430 days. The model gives a COD removal of 79%. This difference in COD removal is attributed to the operation time and to the HRT used in the model.

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With respect to biomass (Figure 36), there is a major presence of microorganism at the bottom of the reactor, where the rate of degradation of organic matter is fastest. The biomass concentration decreased with the height in the reactor until it reached a value approaching to zero at the top of the reactor. After the UASB reactor had been in operation for 417 days, the biomass concentration increased from 12 to 28 kg m-3. A sludge discharge could probably be applied with this biomass concentration. The final biomass concentration was not reported in the experimental paper.

Figure 36. Model response for the biomass concentration profiles at different operating times (hours).

The particle size distribution (i.e. granule radius) is shown in Figure 37. At the bottom of the reactor, the granule diameter ranged from 2.4 mm to 3.4 mm during the operation time of the reactor. It is know that biogas production takes place inside granules and that can affect the particle density (MacLeod et al. 1990), but granules with a diameter of 3 mm usually have a sufficiently high density to fall down. In addition, the biomass refers to the active microorganism. Inert material and inactive microorganisms are not taken into account in the model.

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Figure 37. Model response for the granule size distribution at different operating times (hours).

The developed model is a transient model. An important aspect of this modelling is the high sensitivity of the model response to the values used for some parameters (e.g., the washout fraction, decay constant). The model has been successfully used to simulate the performance of the UASB reactor studied by Harada et al. (1996). The kinetic rate constant was determined based on a mass balance of substrate within the granule and it follows the Monod model. It also includes the substrate diffusion resistance in the granule and the stagnant film around the particle. The kinetic term in the model includes the growth and death of microorganisms and the fraction of biomass washed out by the up-flowing wastewater. The granules in the reactor are distributed according to size, the larger ones being located in the lower part of the bioreactor.

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8- CONCLUSIONS A model was proposed for the Upflow Anaerobic Sludge Blanket (UASB) reactor. The proposed model describes the substrate degradation, the microorganism concentration growth and the particle size distribution along the height in the reactor. This type of reactor is an attractive alternative for regions in hot climates since it works better under mesophilic conditions and it does not need any supporting structure for the development of microorganisms, which grow in the form of granules. The developed model is transient and is based on mass balances for the substrate and microorganisms in the reactor. For the substrate, the processes included in the model are dispersion, advection and degradation of the organic matter in the substrate. The reaction rate for the microorganisms includes the growth and decay of the microorganisms. The decay takes into account the microorganism dying and the fraction of biomass that may be dragged into the effluent. The microorganism development is described by a Monod type equation including the death constant; the use of the Contois equation for describing the microorganism growth was also addressed In addition, a model considering the substrate degradation in the granule was developed. For this, a stationary mass balance within the granule was carried out and an expression for the reaction kinetics was then developed. The model for the granule takes into account the mass transport through the stagnant film around the granule, the intra-particle diffusion, and the specific degradation rate. The model was solved using commercial software (COMSOL Multiphysics). The model was validated using results reported in the literature from experiments carried out at pilot scale. The results of the model are quite sensitive to some parameters applied in the simulation, e.g. if the decay constant (Kd) is too high, the amount of microorganism will decrease in the reactor, and the degradation of substrate will therefore be low. However, the model describes satisfactorily the degradation of substrate along the height in the reactor; the major part of the substrate is degraded at the bottom of the reactor due to the high density of biomass present in that region. Models like the one developed here are useful tools in the development and design of UASB reactors. It allows researchers to study the consequences for the reactor performance of different types of substrate, different inlet substrate concentration, different flow rates and different kinds of biomass. ACKNOWLEDGEMENT This work is supported by the Swedish International Development Cooperation Agency (SIDA/SAREC).

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