Modelling sodium inhibition on the anaerobic digestion process

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  • 1565 IWA Publishing 2012 Water Science & Technology | 66.7 | 2012Modelling sodium inhibition on the anaerobic digestion

    process

    A. Hierholtzer and J. C. AkunnaABSTRACTSodium 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.2012.345A. 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: j.akunna@abertay.ac.ukKey words | ADM1, biogas, inhibition, methanogenesis, sodiumINTRODUCTIONAnaerobic 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 theuptake 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

    mailto:j.akunna@abertay.ac.uk

  • 1566 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012non-acclimatised archaea (Fang et al. ) with completeinhibition 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 models 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 themodel 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 acidbase 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 extrainhibition factor INa can be applied to the rate of acetateuptake as expressed in Equation (1) below:

    Iacetate IpH,ac IIN,lim INh3 INa (1)

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

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

    INa 1

    1 SNa=KI,Na (2)

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

    Equation (3) below:

    dSNadt

    qinVliq

    SNa,in SNa

    (3)

    where qin is the reactor inflow, Vliq the effective capacity ofthe reactor. SNa

    and SNa,in being the initial concentrations

    of 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.

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

    027

    28 35 0.083

    2934

    35 35 0.14

    3641

    42 35 0.2

    4348

    49 35 0.26

    5055 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 min1. 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 colorimetricdetermination 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 methaneium 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

  • 1568 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012described by means of Equation (4) as described by Guna-

    seelan ().

    B(t) B0 1 e kdist

    (4)

    where B0 and Kdis represent the maximum methane yieldand the disintegration rate constant respectively.

    The values of soluble variables such as COD and volatilefatty 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 digestedsubstrate. The concentration of inorganic nitrogen (SIN)was estimated by the measured ammonium nitrogen con-centration. VFA components (Sva, Sbu, Spro and Sac) werecalculated 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 (ReichertTable 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.kgCOD1, respect-ively. Influent inert particulates were estimated from the

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

    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 Matlabimplementation 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 pHAverage 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

  • 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 | 2012show 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.

  • 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 | 2012During 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 Na. SCAT isoriginally implemented in the ADM1 to represent metallic

    ions such as Na, hence SNa 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.

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

  • 1572 A. Hierholtzer & J. C. Akunna | Modelling sodium inhibition Water Science & Technology | 66.7 | 2012production 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 Na/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 tolerancethreshold 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.REFERENCES

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    Modelling sodium inhibition on the anaerobic digestion processINTRODUCTIONMODEL DESCRIPTIONADM1 structureImplementation and proposed modifications

    MATERIALS AND METHODSBatch and reactor experimentsAnalytical methods

    EXPERIMENTAL RESULTS AND DISCUSSIONParameters estimationModelling of reactor performanceApplication of the modified ADM1 in process diagnosis and control

    CONCLUSIONSThis work has been funded by the University of Abertay. The authors wish to express their gratitude to Dr Ulf Jeppsson and Dr Christian Rosen for providing their Matlab code of ADM1.REFERENCES

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