modelling sodium inhibition on the anaerobic digestion process

9
Modelling sodium inhibition on the anaerobic digestion process A. Hierholtzer and J. C. Akunna ABSTRACT Sodium is a known process inhibitor in anaerobic systems and impacts on methanogens through an increase of osmotic pressure or complete dehydration of microorganisms. In this study, a combination of experimental and modelling approaches has been employed to determine and simulate sodium inhibition on the anaerobic digestion process. The ADM1, which has been successfully used in modelling anaerobic processes, has been modied to include an extra inhibition function that considers the effect of sodium on acetoclastic methanogens and the impact on biogas production and composition. A non-competitive inhibition function was added to the rate of acetate uptake for the model to take into account sodium toxicity. Experimental studies consisted of both batch and reactor tests to obtain parameters for model calibration and validation. The calibrated model was used to predict the effect of ammonia nitrogen on sodium toxicity. It was found that relatively low sodium levels can bring about signicant levels of process inhibition in the presence of high levels of ammonia. On the other hand, where the concentration of ammonia is relatively low, the tolerance threshold for sodium ions increases. Hence, care must be taken in the use of sodium hydroxide for pH adjustment during anaerobic digestion of protein-rich substrates. A. Hierholtzer J. C. Akunna (corresponding author) Urban Water Technology Centre, School of Contemporary Sciences, University of Abertay Dundee, Bell Street, Dundee DD1 1HG, Scotland, UK E-mail: [email protected] Key words | ADM1, biogas, inhibition, methanogenesis, sodium INTRODUCTION Anaerobic digestion is appropriate for the treatment of vir- tually all types of organic wastes or residues with economic and ecological impacts proving favourable when compared with other processes (Baldasano & Soriano ; Edelmann et al. ). Common drawbacks to the uptake of anaerobic technology such as difculties in oper- ation and high initial investments with no guarantees of short-term protability are, however, among main concerns that have adversely affected its wide application. To be econ- omically viable, the system must produce enough biogas to maintain the reactor at optimal operational temperature and generate net energy gains. Hence, a readily available and secure feedstock is a necessity for sustainable and effec- tive operation of anaerobic digestion systems. Sodium is a chemical element widely found in water bodies and everyday products. It is a crucial compound in animal life because of its regulating effect on body uid volumes. Although sodium is detected in most of biodegrad- able substrates, its usually low concentration rarely affects microorganisms involved in anaerobic digestion. Furthermore, sodium appears to be essential for methano- genic bacteria at small concentrations (Appels et al. ). In most cases, the impact of sodium in anaerobic digesters treating municipal wastewater and the biodegradable frac- tion of municipal waste seems to be minor and has not been found detrimental. However, toxic levels of sodium are common in systems treating wastewater from food pro- cessing industries (Soto et al. ; Feijoo et al. ; Gebauer ), chemical industries or reactors using macro-algae as a substrate. Since sodium is also a major component of sodium hydroxide, which is widely used for pH adjustment in anaerobic digestion systems, inhibitory sodium levels can occur in anaerobic digestion systems treating non-saline feedstocks. In coastal regions with a sea- sonal abundance of marine macrophytes, the need for a viable supply of feedstock for anaerobic digestion systems has generated interest towards marine biomass as a poten- tial sole and co-substrate. High salt content, not only from sodium but also potassium, calcium and magnesium ions exists in such substrate and has been reported inhibitory to 1565 © IWA Publishing 2012 Water Science & Technology | 66.7 | 2012 doi: 10.2166/wst.2012.345

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Page 1: Modelling sodium inhibition on the anaerobic digestion process

1565 © IWA Publishing 2012 Water Science & Technology | 66.7 | 2012

Modelling sodium inhibition on the anaerobic digestion

process

A. Hierholtzer and J. C. Akunna

ABSTRACT

Sodium is a known process inhibitor in anaerobic systems and impacts on methanogens through an

increase of osmotic pressure or complete dehydration of microorganisms. In this study, a

combination of experimental and modelling approaches has been employed to determine and

simulate sodium inhibition on the anaerobic digestion process. The ADM1, which has been

successfully used in modelling anaerobic processes, has been modified to include an extra inhibition

function that considers the effect of sodium on acetoclastic methanogens and the impact on biogas

production and composition. A non-competitive inhibition function was added to the rate of acetate

uptake for the model to take into account sodium toxicity. Experimental studies consisted of both

batch and reactor tests to obtain parameters for model calibration and validation. The calibrated

model was used to predict the effect of ammonia nitrogen on sodium toxicity. It was found that

relatively low sodium levels can bring about significant levels of process inhibition in the presence of

high levels of ammonia. On the other hand, where the concentration of ammonia is relatively low, the

tolerance threshold for sodium ions increases. Hence, care must be taken in the use of sodium

hydroxide for pH adjustment during anaerobic digestion of protein-rich substrates.

doi: 10.2166/wst.2012.345

A. HierholtzerJ. C. Akunna (corresponding author)Urban Water Technology Centre,School of Contemporary Sciences,University of Abertay Dundee,Bell Street,Dundee DD1 1HG,Scotland,UKE-mail: [email protected]

Key words | ADM1, biogas, inhibition, methanogenesis, sodium

INTRODUCTION

Anaerobic digestion is appropriate for the treatment of vir-tually all types of organic wastes or residues with

economic and ecological impacts proving favourable whencompared with other processes (Baldasano & Soriano; Edelmann et al. ). Common drawbacks to the

uptake of anaerobic technology such as difficulties in oper-ation and high initial investments with no guarantees ofshort-term profitability are, however, among main concerns

that have adversely affected its wide application. To be econ-omically viable, the system must produce enough biogas tomaintain the reactor at optimal operational temperatureand generate net energy gains. Hence, a readily available

and secure feedstock is a necessity for sustainable and effec-tive operation of anaerobic digestion systems.

Sodium is a chemical element widely found in water

bodies and everyday products. It is a crucial compound inanimal life because of its regulating effect on body fluidvolumes. Although sodium is detected in most of biodegrad-

able substrates, its usually low concentration rarely affectsmicroorganisms involved in anaerobic digestion.

Furthermore, sodium appears to be essential for methano-genic bacteria at small concentrations (Appels et al. ).In most cases, the impact of sodium in anaerobic digesterstreating municipal wastewater and the biodegradable frac-tion of municipal waste seems to be minor and has not

been found detrimental. However, toxic levels of sodiumare common in systems treating wastewater from food pro-cessing industries (Soto et al. ; Feijoo et al. ;

Gebauer ), chemical industries or reactors usingmacro-algae as a substrate. Since sodium is also a majorcomponent of sodium hydroxide, which is widely used forpH adjustment in anaerobic digestion systems, inhibitory

sodium levels can occur in anaerobic digestion systemstreating non-saline feedstocks. In coastal regions with a sea-sonal abundance of marine macrophytes, the need for a

viable supply of feedstock for anaerobic digestion systemshas generated interest towards marine biomass as a poten-tial sole and co-substrate. High salt content, not only from

sodium but also potassium, calcium and magnesium ionsexists in such substrate and has been reported inhibitory to

Page 2: Modelling sodium inhibition on the anaerobic digestion process

1566 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

non-acclimatised archaea (Fang et al. ) with complete

inhibition being observed at high levels (Rinzema et al.; Jeison et al. ).

In this study, the impact of sodium concentration on

acetoclastic methanogens is studied using a combinationof experimental and modelling approaches. The AnaerobicDigestion Model No.1, which has been applied successfullyto a vast range of substrates is used here as a platform for

process simulation. The model’s open structure encouragesthe addition of specific mechanisms not originally takeninto account. The ADM1 has thus been modified to include

an extra inhibition function that considers the effect ofsodium on acetoclastic methanogens and the impact onbiogas production and composition. Batch tests and reactor

studies were conducted and results used to calibrate themodified version of the ADM1.

MODEL DESCRIPTION

ADM1 structure

The model is fully described in a seminal literature byBatstone et al. (). Only a brief description of the

model implementation is given here. The ADM1 uses chemi-cal oxygen demand (COD) as the main unit since it is widelyused in wastewater measurements and depicts a certain con-

sistency with other IWA models. It introduces anaerobicdigestion by dividing the process into different steps suchas the disintegration of particulate material, hydrolysis, acid-ogenesis, acetogenesis, aceticlastic methanogenesis and

hydrogenotrophic methanogenesis.The model initially consists of 12 differential equations

representing soluble matter concentrations in the liquid

phase including inorganic carbon and inorganic nitrogen,a further 12 differential equations for particulate matter con-centrations, another two equations to model cations and

anions levels and a final six differential equations for acid–base reactions. Hydrogen, methane and carbon dioxide arethe main components in the gaseous phase that are taken

into account. Inhibition that could result from low pH,lack of inorganic nitrogen or high levels of ammonia nitro-gen are introduced by a set of algebraic equations.Biochemical rate coefficients and kinetic rate equations

are represented by a rate matrix involving 19 biochemicalrate processes. All cellular kinetics are described throughtheir uptake, growth and decay rates. Particular attention

is given here to the expression of acetate uptake and corre-sponding inhibition factors.

Implementation and proposed modifications

The implementation of the ADM1 in Matlab/Simulink(MathWorks Inc., USA) carried out by Rosen & Jeppsson

() was used. Each unit of the model is represented bya system function (S-function) incorporated in Matlabthrough Simulink which provides an interactive graphicalinterface. The files containing the model code have to be

compiled and converted to MEX files before being used byMatlab. Non-competitive inhibitions from hydrogen andfree ammonia are originally implemented in the ADM1

along with pH inhibition. Since sodium has been reportedto lower the maximum specific growth rate and yield of acet-oclastic methanogens (Rinzema et al. ), an extra

inhibition factor INaþ can be applied to the rate of acetateuptake as expressed in Equation (1) below:

Iacetate ¼ IpH,ac � IIN,lim � INh3 � INaþ (1)

where INaþ is a non-competitive function taking into con-sideration the effect of sodium concentration not

represented in the original ADM1 model. INaþ can beexpressed as shown in Equation (2) below:

INaþ ¼ 11þ SNaþ=KI,Naþ

� � (2)

With KI,Naþ being the inhibitory sodium concentrationfor acetate degrading organisms and SNaþ the concentrationof sodium implemented within the model as expressed in

Equation (3) below:

dSNaþ

dt¼ qin

VliqSNaþ,in � SNaþ� �

(3)

where qin is the reactor inflow, Vliq the effective capacity of

the reactor. SNaþ and SNa,in

þ being the initial concentrationsof sodium in the system and in the feedstock, respectively.

An acclimatisation phenomenon to highly saline

environment for microorganisms involved in anaerobicdegradation has been observed by many authors (Bashir &Matin ; Gebauer ; Sialve et al. ) and is believedto occur through the adaptation of the original microorgan-

isms (tolerance to higher osmotic pressure induced bysodium) or a complete shift in microbial population. Theexperimental design and duration adopted in this study

was to ensure negligible, and reduce the effect of, acclimat-isation of the methanogenic archaea to sodium.

Page 3: Modelling sodium inhibition on the anaerobic digestion process

1567 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

MATERIALS AND METHODS

Batch and reactor experiments

Batch tests were conducted in triplicate during 50 days todetermine digestion kinetics. The method used wasadapted fromHansen et al. () and consisted of 2 L bottles

inoculated with 400 mL of anaerobically digested sludgeand 100 mL non-growth medium (2.7 g/L KH2PO4, 3.5 g/LK2HPO4, 0.005 g/L MgSO4.7H2O, 0.0005 g/L CaCl2,

0.0005 g/L FeCl2, 0.0005 g/L KCl2, 0.0001 g/L CoCl2.6H2O,0.0001 g/L NiCl2). Solutions containing 10 g of blendedgreen peas diluted with 100 mL of freshwater were

added and the bottles were flushed for 2 min with N2,closed with rubber caps and incubated at mesophilictemperature (37± 1 WC). Blanks containing only

inoculum and freshwater were also incubated at the sametemperature.

An 8 L capacity (with 5 L effective capacity) laboratory-scaled reactor was used to assess the effect of increasing

concentration and accumulation of sodium within thesystem. The reactor was firstly inoculated with anaerobicallydigested sludge obtained from a wastewater treatment plant

and set in batch mode until the start up of biogas pro-duction. Feeding was carried out once daily with 100 g ofblended peas, Pisum sativum, diluted with 150 mL of dis-

tilled water. The reactor was operated under mesophilictemperature (37 WC± 1 WC) with a 20 days hydraulic reten-tion time. At steady state, varying sodium concentrationsin form of NaHCO3 was introduced into the reactor at

specific intervals, as shown in Table 1. The addition ofsodium as NaHCO3 ensured that the pH values of thedigesting culture were maintained at a suitable range

during the experiment.

Table 1 | Summary of reactor addition

Day Amount NaHCO3 added (g) Equivalent sod

0–27 – –

28 35 0.083

29–34 – –

35 35 0.14

36–41 – –

42 35 0.2

43–48 – –

49 35 0.26

50–55 – –

Analytical methods

Biogas production from the anaerobic reactor wasmeasured by water displacement and its composition

determined by a GA2000 gas analyser (GeotechnicalInstruments, UK). For the batch tests, biogas productionwas measured by displacement of a syringe piston andgas chromatography was used to evaluate methane con-

tent using a Hewlett-Packard 5890 Series II gaschromatograph with dual thermal conductivity detectorand an AT-Alumina stainless steel capillary column. Injec-

tor, oven and detector temperatures were 100, 75 and120 WC respectively. The helium carrier gas flow rate was7 mL min�1. Methane yield results were converted to

standard temperature and pressure (STP: 273.15 K;1013.25 hPa). Alkalinity was determined daily by titrationaccording to standard methods (APHA ), COD wasmeasured using Hach-Lange cuvette tests (LCK 014)

and samples were centrifuged for the determinationof soluble COD. Total and volatile solids weredetermined based on standard methods (APHA ).

Concentration of ammonium nitrogen was determinedby cuvette tests (LCK 304), total VFA were quantifiedby esterification (Montgomery et al. ) and colorimetric

determination using a DR5000 spectrophotometer(Hach-Lange, USA).

EXPERIMENTAL RESULTS AND DISCUSSION

Parameters estimation

First-order kinetics were determined from the results ofbatch tests through the cumulative production of methane

ium in reactor (mol/l) Method of addition

No addition

Added once, directly in the reactor

No addition

Added once, directly in the reactor

No addition

Added once, directly in the reactor

No addition

Added once, directly in the reactor

No addition

Page 4: Modelling sodium inhibition on the anaerobic digestion process

1568 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

described by means of Equation (4) as described by Guna-

seelan ().

B(t) ¼ B0 × 1� e �kdis�tð Þ� �

(4)

where B0 and Kdis represent the maximum methane yield

and the disintegration rate constant respectively.The values of soluble variables such as COD and volatile

fatty acids (VFA), were derived from experimental data

obtained from the reactor studies. The concentration of inor-ganic carbon in the effluent (SIC) was obtained from thepartial alkalinity, i.e. addition of acid to the sample until

pH reached 5.75, since it is believed that inorganic carbonwill be mainly due to bicarbonate between pH 6 and 8(Van Haandel & Lettinga ). Total cations concentration(SCAT) was estimated by the total alkalinity of the digested

substrate. The concentration of inorganic nitrogen (SIN)was estimated by the measured ammonium nitrogen con-centration. VFA components (Sva, Sbu, Spro and Sac) were

calculated from the total VFA and apportioned accordingto the proportion of each component in the effluent. Firstorder parameters corresponding to the hydrolysis kinetic

rates of carbohydrates, lipids and proteins were set at ratessimilar to the disintegration step since their influence isnot significant for homogenous substrates (Blumensaat &

Keller ; Feng et al. ).Parameters representing yields of products on sub-

strates were calculated from the concentrations of protein,carbohydrate and fibre. For example, cellulose content

was estimated from the content of crude fibre accordingto Matrone et al. () and lignin content from the differ-ence between acid detergent fibre and cellulose (Reichert

Table 2 | Effluent characteristics and biogas production

Parameters Minimum Maximum

pH 6.8 7.6

CODS (mg COD/l) 1455.0 19064.0

CODT (mg COD/l) 13128.0 29313.0

TS (g/l) 8.1 19.2

VS (%TS) 43.3 76.0

VFA (mg COD/l) 304.9 7888.4

T. Alkalinity (mg CaCO3/l) 708.1 2662.4

P. Alkalinity (mg CaCO3/l) 576.5 1961.8

Biogas volume (l/day) 2.7 6.8

CH4 (%) 21.5 61.9

CO2 (%) 38.8 67.8

& MacKenzie ). Setting the yield of soluble inert to

zero, fpr,xc, fch,xc, fli,xc and fxi,xc were taken to be equal to0.189, 0.263, 0.011 and 0.536 kgCOD.kgCOD�1, respect-ively. Influent inert particulates were estimated from the

total solids (TS) and volatile solids (VS) values. KI,Naþ

was adjusted to 0.21 mol/L by fitting the model outputs tothe experimental data, mainly VFA and biogas composition.Similarly, maximum specific uptake rate and half saturation

values were adjusted for acetate from experimental data.Chen et al. () reported IC50 values for sodium inhibitionranging from 0.24 to 2.3 mol/L depending on reactor con-

figuration, substrate, potential microbial adaptation andpresence of other cations. The relatively small valueobtained in this study is believed to be characteristic of

the particular system considered with no acclimatisationor significant concentration of other cations. The valuesof the operating parameters, i.e. temperature, flow rateand reactor size were also added in the model. Other par-

ameters such as particulate components, half saturationvalues or decay rates were taken to be equal to the valuessuggested by Batstone et al. () and from the Matlab

implementation of Rosen et al. ().

Modelling of reactor performance

The ability of the model to simulate appropriately the effectsof sodium addition in the reactor was evaluated by compar-ing experimental values with simulation results. Table 2summarises the characteristics of the reactor effluent over

the experimental period.Simulation for pH variation and biogas production can

be seen on Figures 1 and 2. Simulation values for pH

Average Standard deviation N. of samples

7.3 0.2 57

5741.3 5637.4 18

18988.0 8968.9 3

12.7 3.4 14

59.7 10.6 14

2035.2 2348.5 50

1399.5 638.2 50

1038.0 421.1 50

4.7 1.2 57

47.9 13.5 57

49.3 9.1 57

Page 5: Modelling sodium inhibition on the anaerobic digestion process

Figure 1 | pH simulation and experimental values.

Figure 2 | Biogas production simulation and experimental values.

1569 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

show a good agreement with measurements both in stablestate and during perturbations caused by the addition of

sodium. In day 28, the addition of NaHCO3 resulted in aquick increase of pH, and this response was correctlypredicted by the modified ADM1. Subsequent pH pertur-

bations were also accurately predicted by the model.Figure 2 shows a good fit between experiment and

simulated values for biogas production. During the first

20 days of the study, the model tended to underestimatethe amount of biogas produced but a better fit was obtainedduring periods of instability caused by the addition ofsodium.

Sodium addition resulted in a rapid increase of VFA asshown in Figure 3, an indication of inhibition on methano-

genesis. Since the anaerobic inoculum used in the studywas not acclimatised to sodium prior to the experiment,every addition of NaHCO3 resulted in a quick decrease

in biogas methane content followed by a stabilisationperiod until the next addition (Figure 4). Although eachaddition of NaHCO3 might have resulted in some degree

of microbial acclimatisation, the fact that the sodium saltwas added each time as a single shock load to thesystem was likely to reduce the effect of acclimatisationon the microbial response.

Page 6: Modelling sodium inhibition on the anaerobic digestion process

Figure 3 | Total VFA simulation and experimental values.

Figure 4 | %CH4 and %CO2 simulation and experimental values.

1570 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

During simulation, carbon dioxide was underestimated

by around 10% which could be explained by the non-optimisation of gas transfer and solubility coefficients(Blumensaat & Keller ).

To reproduce the addition of sodium, it was necessary toadjust SCAT since the concentration of cations was increas-ing proportionally with the addition of Naþ. SCAT isoriginally implemented in the ADM1 to represent metallic

ions such as Naþ, hence SNaþ was not directly added to

the charge balance equation. Operating at high concen-trations of sodium bicarbonate was interpreted by an

increase in SIC in the effluent with the input bicarbonateinfluencing the overall inorganic carbon balance.

The addition of significant amounts of NaHCO3 in the

system resulted in the stripping of bicarbonate in the formof CO2 which was observed experimentally after eachaddition of sodium bicarbonate (as shown in Figure 4) and

might have resulted in an increase in hydrogenotrophicactivities. However, it is assumed that this pathway will beadversely affected by the increased sodium concentration.This seemed to be a realistic assumption, since there was a

general decrease in biogas production (as shown in Figure 2)following each addition of sodium bicarbonate, indicatingthat the inhibitory effect of sodium on the overall methano-

genesis was greater than any increase in hydrogenotrophicactivities caused by the added bicarbonate.

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1571 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

The difference between experimental and simulation

values follows a normal distribution for both the biogasproduction and pH according to the Shapiro-Wilk testat a 5% level of significance. Consequently, the differences

found between these values can be confidently attributedto errors in practical measurements and not to falsemodel structure or experiment design (Koutrouli et al.). Table 3 shows a comparison of the values of

some key variables from the experimental and simulationstudies.

Table 3 | Reactor outputs: experimental and simulation average values

Parameters Experimental

Normal operation

pH 7.23

Biogas volume (l/day) 5.73

CH4 fraction (%) 59.64

CO2 fraction(%) 41.57

Total VFA (mgCOD/l) 416

Sodium addition

pH 7.23

Biogas volume (l/day) 3.85

CH4 fraction (%) 39.39

CO2 fraction(%) 54.57

Total VFA (mgCOD/l) 4210

Figure 5 | Effect of sodium and nitrogen concentrations on methane yield.

Application of the modified ADM1 in processdiagnosis and control

Using the calibrated model, an attempt was made to evalu-

ate the effects of varying levels of sodium in an anaerobicdigester when other known process inhibitors were present.The most common potential process inhibitor is inorganic(or ammonia) nitrogen. Ammonia is an essential macro-

nutrient for microbial growth, but can also be toxicbeyond certain levels. Figure 5 shows the average methane

Simulation Relative error (%)

7.26 0

5.34 7

57.65 3

34.19 18

441 6

7.16 1

4.08 6

43.23 10

48.5 11

4032 4

Page 8: Modelling sodium inhibition on the anaerobic digestion process

1572 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012

production over 100 days of simulation at steady state with a

simultaneous variation of sodium and inorganic nitrogenconcentrations and without considering the potentialacclimatisation of the methanogenic archaea.

The figure shows that optimal methane yield occurs atlow values of both nitrogen and sodium concentrations. Adecrease of more than 90% methane produced can benoticed when operating at poor conditions. In the absence

of other inhibitory compounds, substrates with low sodiumand relatively low ammonia levels will result in highermethane yield. High methane production can also be

obtained at relatively high ammonia when the sodium islow, whilst high levels of both sodium and ammonia canbring about process failure. Figure 5 also shows that the

threshold inhibitory concentration for sodium is about0.35 mol/L at low concentrations of inorganic nitrogen. Adecrease of about 5% in methane yield is predicted at a con-centration of 0.2 mol Naþ/L and is in good agreement with

the decreased activity of acetoclastic methanogens observedby Rinzema et al. () at similar sodium levels. At low con-centrations of sodium, the inhibitory concentration of

ammonia is found at 0.4 mol/L with literature inhibitoryvalues ranging from 0.1 to 0.82 mol/L (Chen et al. )and 0.25 mol/L for the most sensitive methanogens (Jarrell

et al. ). These results indicate that careful attention isrequired when using sodium salts for pH correction in thedigestion of protein-rich substrate. However, these predicted

results do not take into account possible antagonistic effectsbetween sodium and ammonium ions and should be con-sidered carefully.

CONCLUSIONS

Both the experimental and modelling approaches used inthis study have shown sodium toxicity with increasingaddition of sodium salts. The ADM1 has been modified

and calibrated to take into account the effect of sodium onacetate degrading organisms. The adjustment of a reducedset of parameters and limited experimental work led to

accurate simulation for pH, VFA, biogas production andcomposition. A good fit was found between experimentalvalues and simulation results at inhibitory sodium concen-trations. Using the model, it has been possible to predict

the effect of ammonia on sodium toxicity. The calibratedmodel predicts that relatively low sodium levels can bringabout significant levels of process inhibition in the presence

of high levels of ammonia. On the other hand, when theconcentration of ammonia is relatively low, the tolerance

threshold for sodium ions increases. Hence, care must be

taken in the use of sodium hydroxide for pH adjustmentduring anaerobic digestion.

ACKNOWLEDGEMENTS

This work has been funded by the University of Abertay. The

authors wish to express their gratitude to Dr Ulf Jeppssonand Dr Christian Rosen for providing their Matlab code ofADM1.

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First received 17 February 2012; accepted in revised form 9 May 2012