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Application of the ADM1 model to advanced anaerobic digestion
Bioresource Technology 96 (2005) 1832–1842
Application of the ADM1 model to advanced anaerobic digestion Wayne J. Parker
*
Department of Civil Engineering, University of Waterloo, Waterloo, Ont., Canada N2L 3G1 Received 20 November 2003; received in revised form 3 January 2005; accepted 5 January 2005 Available online 8 March 2005
Abstract In this paper the ADM1 model that has been developed by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes is summarized. The model was applied to a variety of anaerobic digestion scenarios that are presented in the literature and for each data set the model predictions were compared to experimental values. Based upon the model applications it was apparent that for accurate model simulations the influent sludge should be well characterized in terms of biodegradable and recalcitrant COD and also nitrogenous compounds. In almost all cases the model was able to reflect the trends that were observed in the experimental data however the concentrations of VFAs were consistently over-predicted in digesters with short SRTs. It would appear that the inhibition functions associated with low pH values tend to overestimate the impact of pH on biokinetic rates for the acid-consuming bacteria. Application of the model with flow through of active biomass between digesters in series in temperature-phased systems needs to be further evaluated in the future. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Anaerobic digestion; Two-phase; Temperature-phased; Mesophilic; Thermophilic; Model; Sludge
1. Introduction Owners and operators of wastewater treatment plants are increasingly considering the use of advanced digestion technologies for producing pathogen-free biosolids and for enhancing sludge stabilization. Some examples of such technologies include staged thermophilic (Krugel et al., 1998), temperature-phased (TPAD) (Han et al., 1997), two-phase (Ghosh, 1987) and threephase digestion (Drury et al., 2002). With the increasing complexity of these processes it is difficult to evaluate the impact of all process variables on the performance of the digesters. Hence, it is difficult to optimize the design and operation of these processes. Pilot testing for the purposes of optimization is challenging due to the extended time periods that are required to operate
*
Tel.: +1 519 888 4567x6324; fax: +1 519 888 4349. E-mail address: [email protected]
0960-8524/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2005.01.022
these processes. Given these factors, the use of models for predicting process performance over a range of design and operating conditions becomes attractive. Over the years a range of models have been developed for modeling anaerobic digestion processes. Early models were steady state and assumed a rate-limiting step (Lawrence, 1971). However, the increasing complexity of the advanced digestion technologies requires more complex models that can represent the impacts of changing environments on chemical and microbial species. Based on reports in the literature there is evidence of a number of multi-species models that are based upon differing assumptions and have differing configurations (Angelidaki et al., 1999; Pavlostathis and Gossett, 1986; Siegrist et al., 1993). Relatively recently there has been a move by the International Water AssociationÕs (IWA) Task Group for Mathematical Modelling of Anaerobic Digestion Processes to develop a common model that can be used by researchers and practitioners (IWA, 2002). This model (ADM1) has a
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structure that is similar to the IWA activated sludge models that have received acceptance by practitioners over the last 10 years. The application of a version of the model to municipal sludge digestion has been described by Siegrist et al. (2002). The objective of this study was to examine the application of the ADM1 model to advanced digestion technologies. This paper presents an overview of the model structure and assumptions and defines important model inputs. A description of the model application to existing data sets for a variety of anaerobic digester configurations will be presented. The impact of modifying process parameters on process performance, as predicted by the model, will be summarized. Difficulties encountered in model use and recommendations for modifications will be presented.
2. Model description The ADM1 model is described in considerable detail in the report prepared by the IWA Task Group for Mathematical Modeling of Anaerobic Digestion Processes (IWA, 2002). The following provides a brief overview of the model for the purposes of this discussion. The ADM1 model is a structured model that reflects
the major processes that are involved in the conversion of complex organic substrates into methane and carbon dioxide and inert byproducts. In Fig. 1 an overview of the substrates and conversion processes that are addressed by the model is presented. From Fig. 1 it can be seen that the model includes disintegration of complex solids into inert substances, carbohydrates, proteins and fats. The products of disintegration are hydrolyzed to sugars, amino acids and long chain fatty acids (LCFA) respectively. Carbohydrates and proteins are fermented to produce volatile organic acids (acidogenesis) and molecular hydrogen. LCFA are oxidized anaerobically to produce acetate and molecular hydrogen. Propionate, butyrate and valerate are converted to acetate (acetogenesis) and molecular hydrogen. Methane is produced by both cleavage of acetate to methane (aceticlastic methanogenesis) and reduction of carbon dioxide by molecular hydrogen to produce methane (hydrogenotrophic methanogenesis). To address these mechanisms, the model employs state variables to describe the behaviour of soluble and particulate components. All organic species and molecular hydrogen are described in terms of chemical oxygen demand (COD). Nitrogenous species and inorganic carbon species are described in terms of their molar concentrations. Soluble components are those that can pass
Complex Particulate Organic Matter (Xc)
Carbohydrates (Xch)
Proteins (Xpr)
Sugars (Ssu)
Amino Acids (Saa)
Propionate (Spro)
Inert Particulates (XI) Fats (Xli)
Long Chain Fatty Acids (Sfa)
Butyrate (Sbu) Valerate (Sva)
Hydrogen (Sh2)
Acetate (Sac)
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Methane (Sch4) Fig. 1. Conceptual model for ADM1 model.
Inert Soluble (SI)
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through microbial cellular walls and include the monomers of complex polymers (sugars, amino acids, long chain fatty acids), volatile organic acids (propionate, butyrate, valerate, acetate), hydrogen, and methane. In Fig. 1, soluble species are represented with a capital ‘‘S’’. In addition to the organic species, the model addresses inorganic carbon (carbon dioxide and bicarbonate) and nitrogenous species (ammonia and ammonium). All of the species that dissociate as a function of pH (VFAs and ammonia) have variables defined for both the protonated and non-protonated species. The model maintains a charge balance among ionic species and hence there are variables for inorganic anions and cations including the hydrogen ion. The model solves for the hydrogen ion concentration, and thereby the pH, by ensuring chemical neutrality in solution. Particulate species consist of either active biomass species or particulate substances that are incapable of directly passing through bacterial cell walls. In Fig. 1 particulate species are those with a capital ‘‘X’’. The microbial species that are considered in the model include sugar fermenters, amino acid fermenters, LCFA oxidizers, butyrate and valerate oxidizers, propionate oxidizers, aceticlastic methanogens and hydrogenotrophic methanogens. Non-microbial particulate species include complex organics that either enter the process in the influent or that result from the death and decay of microbial species and the products of disintegration of the complex organics. This latter group consists of carbohydrates, proteins and LCFAs. Substrate conversion processes are described by a number of kinetic expressions that describe the conversion rates in terms of substrate concentrations and rate constants. The disintegration of Xc and hydrolysis of Xch, Xpr and Xli are described by first order rate expressions. Substrate conversion processes have Monod-type kinetic expressions while endogenous decay processes are first order in biomass concentration. It should be noted that the ADM1 model differs from the ASM models in that microbially mediated processes are defined in terms of substrate conversion as opposed to microbial growth. For each of the above-mentioned processes the rate of generation of products is related to the process rate through stoichiometric coefficients. For example the rate of growth of an organism is related to the rate of substrate consumption through the yield coefficient for the organism on the substrate. This format is consistent with the approach that is employed in the ASM models. It is recognized that a number of the conversion processes that are active in anaerobic digestion of municipal sludges can be inhibited by the accumulation of intermediate products such as molecular hydrogen, ammonia or by extremes of pH. In the model, all microbially mediated substrate conversion processes are subject to inhibition by extremes of pH. All anaerobic oxidation
processes are subject to inhibition by accumulation of molecular hydrogen and aceticlastic methanogenesis is inhibited at elevated free ammonia concentrations. Inhibition that is caused by molecular hydrogen and free ammonia is implemented in the model by employing rate multipliers that reflect non-competitive inhibition. An empirical correlation is employed as a process rate multiplier to reflect the effects of extreme pH. Liquid–gas mass transfer of gaseous components (methane, carbon dioxide and molecular hydrogen) is described by mass transfer relationships. Hence the application of the model equations requires separate mass balances for the liquid and gas phases of the components.
3. Model application In this study a selected number of data sets were chosen from previously published reports on anaerobic digestion of municipal wastewater sludges. Data sets were selected to encompass a range of digester configurations and on the basis of the completeness of the data sets that would be employed for model inputs and for comparison with model predictions. In all cases, studies that employed actual sludges from municipal wastewater treatment plants were selected. The data sets that were employed in this study are described in Table 1. The ADM1 model employs a large number of constants and coefficients. Given the model complexity it was impossible to calibrate the model parameters with any of the data sets that were available. In the report describing the ADM1 model the authors reviewed the previously published reports on anaerobic digestion processes and presented recommended values for model parameters. For the purposes of this study the recommended model parameters were employed unless additional information was provided by the original researchers that allowed for an improved estimate of the model parameters. In order to achieve accurate model predictions it is important to define the properties of the sludge stream entering the digester. For organic substances, the ADM1 model defines these inputs in terms of soluble and particulate COD. For municipal sludges a majority Table 1 Data sets referenced in this study Digester configuration
Sludge source
References
Single stage mesophilic digestion Acid phase digestion
Mixed PS and WAS
Cacho Rivero et al. (2002) Eastman and Ferguson (1981) Han and Dague (1995) Han et al. (1997) Ghosh (1987)
Temperature-phased anaerobic digestion Two-phase digestion
PS PS Mixed PS and WAS Mixed PS and WAS
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of the organic loading is associated with the particulate COD. The particulate COD entering the digester is defined in terms of biodegradable (Xc) and non-biodegradable components. Estimation of these parameters is often challenging for many data sets as in many cases the sludge COD is not reported and in almost all cases the biodegradable fraction is not independently measured. In most cases the sludge is characterized in terms of its volatile solids content. The relationship between volatile solids content and COD will depend upon the relative contribution of primary (PS) and waste activated sludge (WAS) to the sludge composition (Parkin and Owen, 1986). Primary sludges typically contain approximately 2.0 kg COD/kg VS while WAS typically has a value of 1.4 kg COD/kg VS for this parameter. The inlet COD can therefore be estimated on the basis of these typical values if the relative contributions of PS and WAS are known. The biodegradable fraction of the sludge particulate COD will also be a function of the sludge make-up. Primary sludges have been estimated to have a COD ‘‘ultimate’’ biodegradability of 69% (Parkin and Owen, 1986). The biodegradable fraction of WAS is dependent upon the sludge age that is employed in the aeration process (Gossett and Belser, 1982). Sludges that have extended solids residence times (SRT) in the aeration basin will have been highly oxidized and hence will be relatively recalcitrant to biodegradation in anaerobic digestion. The ultimate biodegradability of WAS has been found to range from 30% to 50% over the range of SRTs typically employed in wastewater treatment processes. Hence, it is apparent that accurate application of the model requires a detailed characterization of the inlet sludge composition. The sludge composition should be determined in terms of COD and the biodegradable fraction should be determined. This latter parameter could be determined through the use of a long term batch digestion test to identify the maximum biodegradability of the sludge. While there are no standard protocols for such a test, existing anaerobic biodegradability protocols could presumably be adapted for this purpose. If the contribution of PS and WAS to the digester feed were to vary substantially with time, then this testing should be performed on the PS and WAS streams separately. The properties of the composite sludge as a function of time could subsequently be estimated. The ADM1 model also estimates the behaviour of nitrogen compounds in anaerobic digestion. In the cases of municipal sludges the presence of ammonia nitrogen in the inlet and the release of ammonia from decay of solids has a substantial influence on the buffering of pH. As will be demonstrated later in this paper the concentration of ammonia/ammonium in the inlet can have a substantial impact upon the pH of acid-phase digesters that have a relatively short SRT. In addition, the digestion of highly concentrated sludges can result in the re-
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lease of elevated concentrations of ammonia that can be inhibitory to aceticlastic methanogens (IWA, 2002). It is therefore important to characterize the concentration of ammonia/ammonium in the digester inlet as well as the nitrogen content of the sludges. It should be noted that the ADM1 model does not maintain a perfect mass balance on nitrogen (Blumensaat and Keller, 2005). Ammonium that is taken up by microbial growth is not completely released during subsequent decay. Hence, it can be expected that the model will underestimate the concentrations of ammonium. The data sets employed in this study did not contain all of the information that was previously described. Where necessary, typical values were assumed. The impact of these assumptions on model predictions will be subsequently discussed. 3.1. Single stage mesophilic digestion Cacho Rivero et al. (2002) reported a study that assessed the impact of digester SRT on mesophilic anaerobic digestion of mixed PS and WAS. A series of digesters were operated over SRTs ranging from 5 to 40 days. In their paper the sludge COD, ammonia and TKN content and VFA composition were detailed. For this study, the biodegradable COD was estimated by extrapolating the results that were obtained for extended SRTs. In their study COD removal, ammonia and TKN content as well as VFA concentrations in the digested sludges were reported and were employed for comparison with the model predictions. The comparison of the model predictions for effluent COD, NH4/NH3-N, and VFAs is summarized in Fig. 2. The error bars in Fig. 2 represent 1 standard deviation of the experimental data. From Fig. 2 it can be seen that the model was able to predict the effluent COD with considerable accuracy. Nitrogen concentrations were accurately predicted for the shorter SRTs and while the trend of increasing concentrations with increasing SRT was reproduced, the absolute values that were predicted at longer SRTs were somewhat lower that the observed values. The differences in nitrogen concentrations may have been due to the lack of mass balance on nitrogen in the ADM1 model. It would be expected that under conditions where there is substantial solids destruction that the model would underestimate the concentrations of ammonium-nitrogen. The differences between the model predictions and the observed results may also have resulted from differences between the assumed and the actual protein content of the sludge. The reference did not provide any information on the distribution of carbohydrates, proteins and lipids in the sludge and hence the default model values were employed for this parameter. The model predictions for VFA concentrations were relatively accurate for SRTs greater than or equal to
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W.J. Parker / Bioresource Technology 96 (2005) 1832–1842 2500
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Fig. 2. Comparison of model predictions with data of Cacho Rivero et al. (2002).
10 days. However the model clearly overpredicted the concentration of acetate while underpredicting the concentrations of propionate, butyrate and valerate. These results suggest that the rates of oxidation of propionate, butyrate and valerate were somewhat overestimated by the model and this would partially, but not completely, explain the elevated acetate concentrations. It would appear that the rate at which acetate was converted to methane at the lower SRT was somewhat underestimated. This may have resulted from either underestimation of the substrate consumption coefficients for aceticlastic methanogenesis or an overestimation of the
inhibition of this activity by ammonia. The model predicted a 40% reduction in the activity of these organisms due to the presence of ammonia. The impact of reduced rates of aceticlastic activity on model predictions would be greatest at the lower SRTs. 3.2. Acid phase digestion Eastman and Ferguson (1981) performed one of the first detailed studies on the acid-phase digestion of municipal sludges. In their study, the impact of HRT was assessed over a range from 9 to 36 h. The impact of seed
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culture was also evaluated. The model does not have the capability to address this parameter and hence only the tests that were conducted with raw sludge as the seed were employed for this analysis. The model predictions for ammonia/ammonium-N, pH and total volatile acids (as acetate) were compared with the observed values in Fig. 3. From Fig. 3 it can be seen that the model somewhat underpredicted the organic acid concentrations at the lowest SRT of 9 h and overpredicted these values for the longest SRT of 72 h. The underprediction of acid 12
VFA (g COD/L)
10
8
Model
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Experimental
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2
0 9
18
36
72
SRT (hrs) 7
concentrations at 9 h is in agreement with the overestimation of the effluent pH in this test. It would appear that the model underestimated the rates of disintegration, hydrolysis and acidification under these relatively extreme conditions of SRT and pH. It should be noted that the model does not correct any of the disintegration or hydrolysis rates for pH. Ghosh (1987) has demonstrated that the rate of hydrolysis is influenced by pH. An improvement of the model for addressing acid phase digesters would be to include a rate correction term for hydrolysis processes. While not presented in Fig. 3 it must be noted that although Eastman and Ferguson (1981) observed methane production at the longer SRTs the model did not predict the generation of appreciable quantities of methane under these conditions. The conversion of VFAs to methane in the experimental data may explain the higher modelpredicted VFA concentrations relative to the observed values. The results suggest that methanogens are less sensitive to pH than the pH inhibition functions suggest. The ammonia-nitrogen concentrations were underpredicted at the lowest SRTs and overpredicted at the highest SRTs. These results tend to confirm the model predictions of VFA concentrations since an underprediction of solids destruction and hydrolysis, as indicated by reduced VFAs, would also result in a reduced release of ammonium. 3.3. Temperature-phased anaerobic digestion (TPAD)
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500 Model
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Fig. 3. Comparison of model predictions with data of Eastman and Ferguson (1981).
TPAD processes consist of reactors operating at thermophilic and mesophilic temperatures in series. While either process may be first, the most common orientation has the thermophilic digester ahead of the mesophilic digester. For the purposes of this study two papers on TPAD digestion were referenced; one that studied digestion of PS alone (Han and Dague, 1995) and one that studied a mix of PS and WAS (Han et al., 1997). In the former paper the ratio of the volumes of the first and second digesters was 1:2. In the latter paper two systems were operated with system A having a ratio of volumes of 1:2.5 while system B had a ratio of volumes of 1:5. In all of the systems the mesophilic temperature was 35 °C while the thermophilic temperature was 55 °C. A comparison of the model predictions with the data presented in the paper of Han and Dague (1995) is summarized in Fig. 4. The comparison of model predictions with the results of Han et al. (1997) are presented for systems A and B in Figs. 5 and 6 respectively. It should be noted that the model does not explicitly predict volatile solids removal (VSR). For the purposes of this paper it was assumed that the removal of volatile solids was proportional to the removal of overall COD. This assumes that all of the COD remaining after digestion have the same ratio of volatile solids concentration:COD. This undoubtedly introduces some
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W.J. Parker / Bioresource Technology 96 (2005) 1832–1842
16
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SRT (d)
Fig. 4. Comparison of model predictions with data of Han and Dague (1995).
error in the estimates however, there was generally insufficient data on the composition of the digester effluent to perform a more refined conversion of COD to solids concentrations. The patterns with respect to the model predictions and observed data that are presented in Figs. 4–6 are consistent. In all three cases, the model overpredicted the production of methane by the temperature-phased processes. It should be noted that in the papers only total methane production was reported and hence it was not possible to compare methane production from the two reactors separately. In all cases the extent of overprediction was greatest for the lower SRTs and predictions improved for the longer SRTs. The predictions for VSR were best for the results presented in Fig. 4 while in Figs. 5 and 6 the model consistently overpredicted the VSR. The overprediction of VSR was consistent with the overprediction of methane generation. In all three cases the model substantially overpredicted the concentrations of VFAs in the thermophilic reactor. The greatest overprediction was associated with the shortest SRTs and the predictions improved at longer SRTs. With the exception of the 10 day SRT in Fig. 4 the model tended to underpredict the concentrations of VFAs in the mesophilic second stage digester.
The results suggest that for thermophilic conditions the model overpredicts the generation of volatile fatty acids and that this is accentuated at shorted SRTs. It should be noted that at low SRTs the model predicted substantial inhibition of the acetoclastic methanogens due to low pH (IWA, 2002). It may be that the inhibition functions for this process were too severe. Although the model predicted high VFA concentrations in the thermophilic phase reactor, it predicted that essentially all of the VFAs could be converted in the second phase mesophilic reactor. The predicted effluent concentrations were actually lower than those that were observed. It should be noted that in this modeling effort the biomass that was present in the first phase reactor was allowed to flow into the second phase reactor and remain active at the new temperature. This may have resulted in the overprediction of activity in the latter reactor as it is unlikely that all of the thermophilic biomass leaving the first digester would remain viable in the second stage digester. It may be more appropriate to assume that the biomass entering the second digester should be considered as biodegradable particulate organic matter. The differences between the model predictions and the observed values of the VFA concentrations may also
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W.J. Parker / Bioresource Technology 96 (2005) 1832–1842
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Fig. 5. Comparison of system a model predictions with data of Han et al. (1997).
have been due to the procedure used to adjust the biokinetic coefficients for temperature. In the model documentation (IWA, 2002) it is suggested that a constant correction factor be employed for all of the microbial species. Implementing this strategy tends to result in an accumulation of VFAs at the higher temperatures. It may be that differing temperature correction factors should be employed for the different microbial species.
3.4. Two-phase anaerobic digestion In two-phase anaerobic digestion the first digester is operated at a short SRT to wash out methanogenic bacteria and promote the establishment of an acidic environment. In the second stage digester the VFAs that are generated in the first stage are converted to methane. In the study reported by Ghosh (1987) a number of experiments were performed with digesters operating at both mesophilic and thermophilic conditions. For the purposes of this paper only the tests that were performed under mesophilic conditions were examined. Testing was conducted with total SRTs of 3 days and 7 days and an influent TS concentration of 7%. With
the 3 day SRT the first stage had an SRT of 0.9 days while the second stage had an SRT of 2.1 days. With the 7 day SRT the first stage had an SRT of 2 days and the second stage had an SRT of 5 days. In Tables 2 and 3 a comparison of some of the model predictions and the reported experimental values are presented. From Table 2 it can be see that with the exception of the first stage of the 7 day SRT digesters the model predictions for VFAs were relatively close to the observed values and the pH values for the second stage digesters were also well predicted. The paper did not report the first stage pHs and hence it was not possible to use this parameter to evaluate the predictions for VFAs. The overprediction of VFAs for the short SRT reactors was consistent with that observed in the previously described temperature phased digestion studies. The model significantly under-predicted the NH4-N concentrations for the 3 day SRT system while this response was relatively well predicted for the 7 day SRT system. It should be noted that there appeared to be an inconsistency in the data for this response since the observed values for the 3 day SRT system were substantially higher than the 7 day system. This seems to be inconsistent with the VSR data that will be subsequently
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W.J. Parker / Bioresource Technology 96 (2005) 1832–1842 60
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Fig. 6. Comparison of system B model predictions with data of Han et al. (1997).
Table 2 Comparison of volatile acids, pH And NH4-N for two phase digestion
Table 3 Comparison of VSR for two phase digestion
Response
Response
SRT = 3 days
SRT = 7 days
Stage 1
Stage 2
Stage 1
Stage 2
Vol. acids (mg/l) Exper. NA Model 3811
1680 1393
1610–1810 6711
109 180
pH Exper. Model
7.2 7.0
NA 5.2
7.3 7.3
1820 899
NA 766
1049 961
NA 5.8
NH4-N (mg/l) Exper. NA Model 472
described which indicated higher solids reduction for the longer SRT system. The predicted VSR values along with three different measures of VSR for the experimental data that were reported in the original paper are presented in Table 3. From Table 3 it can be seen that the model predictions were within the range of values that were reported in the papers. It should however be noted that the range of values reported in the paper was quite wide and hence the assessment of the model predictions could not be very rigorous.
VSR (%) SRT = 3 days
SRT = 7 days
Model
34.0
42.0
Experimental MOPa Weight of gasb Theor. gas yieldc
26.5 35.5 28.3
33.6 51.5 43.4
a
VS reduction was calculated as: VSR = 100 * (VS1 VS0)/ [VS1 (VS1 * VS0)]. b VS reduction was calculated as VSR = 100 * (weight of gas/weight of VS fed). c VS reduction was calculated as VSR = 100 * (observed gas yield/ theoretical gas yield of 1.078 SCFM/kg VS added).
4. Discussion In this paper the predictions of the ADM1 model using the default values for most of the model coefficients were able to reflect most of the trends that were reported for a variety of digester configurations. There were however consistent deviations between the model predictions and observed values for VFAs when the
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model was employed to predict the behaviour of low SRT systems. In the two phase systems the model was often able to perform reasonably in predicting second stage concentrations of VFAs as with the longer SRTs in these stages the rates of VFA conversion were able to compensate for the high inlet concentrations of VFAs. It would appear that there could be improvements made to the model in the estimation of VFA concentrations under these conditions. It may be necessary to more closely examine the relationship between pH and rate coefficients in this regard. For the purposes of this study it was often necessary to estimate the values that were input into the model for sludge characteristics such as COD, biodegradable fraction of the COD, TKN and NH4-N. These have a substantial influence on model predictions. If the model is to be used as an analysis and design tool it would benefit from more careful characterization of these parameters. A standardized protocol for determining the anaerobically biodegradable fraction of the sludge COD would assist in this regard. The model predictions for VSR that were reported in this paper assumed that the reductions in volatile solids are proportional to the reductions in COD. However, it is known that the COD content of volatile solids depends upon the sludge source and its degree of stabilization. Hence, the estimated values for VSR likely contain error. The extent of this error has not been quantified for this paper. For more accurate predictions of VSR the COD contents of volatile solids in the feed sludge should be accurately characterized. In addition, the use of typical values for the COD content of digested sludge should be employed to convert predicted COD concentrations to VS concentrations. In this implementation of the model it was assumed that for digesters in series the biomass which moved from one digester to another would be active in the downstream reactor. This assumption should be valid for two-phase systems where the digester temperatures are the same in both digesters. Implementation of the model in this manner for temperature-phased configurations requires more analysis as it is likely that the biomass entering the second stage digester will be somewhat less active than the model predicts.
5. Conclusions The ADM1 model is a powerful tool for predicting the behaviour of anaerobic digesters treating municipal sludges. However, for successful simulation the feed stream should be well characterized with respect to itÕs COD content and the biodegradable fraction of this material. A standardized protocol for measuring the latter parameter would further use of the model by the
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industry. The ammonia and TKN concentrations present in the feed need to be well characterized because of their impact on pH buffering and inhibition functions. The model tended to overpredict VFA concentrations for reactors that were operated at reduced SRTs. This was observed for both mesophilic and thermophilic digesters. The results suggest that the inhibition function for pH may over emphasize the impact of reduced pH on biological activity. In addition, the model does not incorporate a pH function for the disintegration and hydrolysis processes. This will have some impact on low SRT systems that tend to operate at reduced pHs. The relationship between COD and VS concentrations for digested sludges should be established. This would improve the estimates of VSR since the model only predicts COD concentrations. Implementation of the model for temperature-phased systems should be further examined since the current implementation assumes that the biomass leaving the upstream digesters can become active in the downstream digesters at the downstream temperature. It does not seem that this would be likely for thermophiles entering a mesophilic digester. The contribution of the incoming biomass to the activity of the digesters should be further quantified.
References Angelidaki, I., Ellegard, L., Ahring, B.K., 1999. A comprehensive model of anaerobic bioconversion of complex substrates to biogas. Biotechnol. Bioeng. 63, 363–372. Blumensaat, F., Keller, J., 2005. Modelling of two-stage anaerobic digestion using the IWA Anaerobic Digestion Model No. 1 (ADM1). Water Res. 39, 171–183. Cacho Rivero, J.A., Suidan, M.T., Ginestet, P., Audic, J.-M., 2002. Effect of SRT on the anaerobic digestion of excess municipal sludge. Proceedings of WEFTEC 2002, Chicago, IL. Drury, D.D., Lee, S.A., Baker, C., 2002. Comparing three-phase thermophilic continuous feed system to semi-batch feed/hold/draw system. Proceedings of the 16th Annual WEF Residuals and Biosolids Management Conference, Austin, Texas. Eastman, J.A., Ferguson, J.F., 1981. Solubilization of particulate organic carbon during the acid phase of anaerobic digestion. J. WPCF 53, 352–366. Ghosh, S., 1987. Improved sludge gasification by two-phase anaerobic digestion. ASCE J. Environ. Eng. 113, 1265–1284. Gossett, J.M., Belser, R.L., 1982. Anaerobic digestion of waste activated sludge. ASCE J. Environ. Eng. 108, 1101–1120. Han, Y., Dague, R.R., 1995. laboratory studies on the temperaturephased anaerobic digestion of domestic wastewater sludges. Proceedings of WEFTEC 1995, Miami Beach, FL. Han, Y., Sung, S., Dague, R.R., 1997. Temperature-phased anaerobic digestion of wastewater sludges. Water Sci. Technol. 36, 367–374. IWA 2002. Anaerobic Digestion Model No. 1 (ADM1), International Water Association Scientific and Technical Report No. 13, IWA Publishing, London, UK. Krugel, S., Nemeth, L., Peddie, C., 1998. Extending thermophilic anaerobic digestion for producing class a biosolids at the greater
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vancouver regional districts annacis island wastewater treatment plant. Water Sci. Technol. 38, 409–416. Lawrence, A.W., 1971. Application of process kinetics to design of anaerobic processes. In: Gould, R.F. (Ed.), Anaerobic Biological Treatment Processes, Advances in Chemistry Series No. 105. American Chemical Society, Washington, DC. Parkin, G.F., Owen, W.F., 1986. Fundamentals of anaerobic digestion of wastewater sludges. ASCE J. Environ. Eng. 112, 867–920.
Pavlostathis, S.G., Gossett, J.M., 1986. A kinetic model for anaerobic digestion of biological sludge. Biotechnol. Bioeng. 28, 1519–1530. Siegrist, H., Renngli, D., Gujer, W., 1993. Mathematical modeling of anaerobic mesophilic sewage sludge treatment. Water Sci. Technol. 27, 25–36. Siegrist, H., Vogt, D., Garcia-Heras, J., Gujer, W., 2002. Mathematical model for meso and thermophilic anaerobic sewage sludge digestion. Environ. Sci. Technol. 36, 1113–1123.
Application of the ADM1 model to advanced anaerobic digestion Wayne J. Parker
*
Department of Civil Engineering, University of Waterloo, Waterloo, Ont., Canada N2L 3G1 Received 20 November 2003; received in revised form 3 January 2005; accepted 5 January 2005 Available online 8 March 2005
Abstract In this paper the ADM1 model that has been developed by the IWA Task Group for Mathematical Modelling of Anaerobic Digestion Processes is summarized. The model was applied to a variety of anaerobic digestion scenarios that are presented in the literature and for each data set the model predictions were compared to experimental values. Based upon the model applications it was apparent that for accurate model simulations the influent sludge should be well characterized in terms of biodegradable and recalcitrant COD and also nitrogenous compounds. In almost all cases the model was able to reflect the trends that were observed in the experimental data however the concentrations of VFAs were consistently over-predicted in digesters with short SRTs. It would appear that the inhibition functions associated with low pH values tend to overestimate the impact of pH on biokinetic rates for the acid-consuming bacteria. Application of the model with flow through of active biomass between digesters in series in temperature-phased systems needs to be further evaluated in the future. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Anaerobic digestion; Two-phase; Temperature-phased; Mesophilic; Thermophilic; Model; Sludge
1. Introduction Owners and operators of wastewater treatment plants are increasingly considering the use of advanced digestion technologies for producing pathogen-free biosolids and for enhancing sludge stabilization. Some examples of such technologies include staged thermophilic (Krugel et al., 1998), temperature-phased (TPAD) (Han et al., 1997), two-phase (Ghosh, 1987) and threephase digestion (Drury et al., 2002). With the increasing complexity of these processes it is difficult to evaluate the impact of all process variables on the performance of the digesters. Hence, it is difficult to optimize the design and operation of these processes. Pilot testing for the purposes of optimization is challenging due to the extended time periods that are required to operate
*
Tel.: +1 519 888 4567x6324; fax: +1 519 888 4349. E-mail address: [email protected]
0960-8524/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2005.01.022
these processes. Given these factors, the use of models for predicting process performance over a range of design and operating conditions becomes attractive. Over the years a range of models have been developed for modeling anaerobic digestion processes. Early models were steady state and assumed a rate-limiting step (Lawrence, 1971). However, the increasing complexity of the advanced digestion technologies requires more complex models that can represent the impacts of changing environments on chemical and microbial species. Based on reports in the literature there is evidence of a number of multi-species models that are based upon differing assumptions and have differing configurations (Angelidaki et al., 1999; Pavlostathis and Gossett, 1986; Siegrist et al., 1993). Relatively recently there has been a move by the International Water AssociationÕs (IWA) Task Group for Mathematical Modelling of Anaerobic Digestion Processes to develop a common model that can be used by researchers and practitioners (IWA, 2002). This model (ADM1) has a
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structure that is similar to the IWA activated sludge models that have received acceptance by practitioners over the last 10 years. The application of a version of the model to municipal sludge digestion has been described by Siegrist et al. (2002). The objective of this study was to examine the application of the ADM1 model to advanced digestion technologies. This paper presents an overview of the model structure and assumptions and defines important model inputs. A description of the model application to existing data sets for a variety of anaerobic digester configurations will be presented. The impact of modifying process parameters on process performance, as predicted by the model, will be summarized. Difficulties encountered in model use and recommendations for modifications will be presented.
2. Model description The ADM1 model is described in considerable detail in the report prepared by the IWA Task Group for Mathematical Modeling of Anaerobic Digestion Processes (IWA, 2002). The following provides a brief overview of the model for the purposes of this discussion. The ADM1 model is a structured model that reflects
the major processes that are involved in the conversion of complex organic substrates into methane and carbon dioxide and inert byproducts. In Fig. 1 an overview of the substrates and conversion processes that are addressed by the model is presented. From Fig. 1 it can be seen that the model includes disintegration of complex solids into inert substances, carbohydrates, proteins and fats. The products of disintegration are hydrolyzed to sugars, amino acids and long chain fatty acids (LCFA) respectively. Carbohydrates and proteins are fermented to produce volatile organic acids (acidogenesis) and molecular hydrogen. LCFA are oxidized anaerobically to produce acetate and molecular hydrogen. Propionate, butyrate and valerate are converted to acetate (acetogenesis) and molecular hydrogen. Methane is produced by both cleavage of acetate to methane (aceticlastic methanogenesis) and reduction of carbon dioxide by molecular hydrogen to produce methane (hydrogenotrophic methanogenesis). To address these mechanisms, the model employs state variables to describe the behaviour of soluble and particulate components. All organic species and molecular hydrogen are described in terms of chemical oxygen demand (COD). Nitrogenous species and inorganic carbon species are described in terms of their molar concentrations. Soluble components are those that can pass
Complex Particulate Organic Matter (Xc)
Carbohydrates (Xch)
Proteins (Xpr)
Sugars (Ssu)
Amino Acids (Saa)
Propionate (Spro)
Inert Particulates (XI) Fats (Xli)
Long Chain Fatty Acids (Sfa)
Butyrate (Sbu) Valerate (Sva)
Hydrogen (Sh2)
Acetate (Sac)
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Methane (Sch4) Fig. 1. Conceptual model for ADM1 model.
Inert Soluble (SI)
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through microbial cellular walls and include the monomers of complex polymers (sugars, amino acids, long chain fatty acids), volatile organic acids (propionate, butyrate, valerate, acetate), hydrogen, and methane. In Fig. 1, soluble species are represented with a capital ‘‘S’’. In addition to the organic species, the model addresses inorganic carbon (carbon dioxide and bicarbonate) and nitrogenous species (ammonia and ammonium). All of the species that dissociate as a function of pH (VFAs and ammonia) have variables defined for both the protonated and non-protonated species. The model maintains a charge balance among ionic species and hence there are variables for inorganic anions and cations including the hydrogen ion. The model solves for the hydrogen ion concentration, and thereby the pH, by ensuring chemical neutrality in solution. Particulate species consist of either active biomass species or particulate substances that are incapable of directly passing through bacterial cell walls. In Fig. 1 particulate species are those with a capital ‘‘X’’. The microbial species that are considered in the model include sugar fermenters, amino acid fermenters, LCFA oxidizers, butyrate and valerate oxidizers, propionate oxidizers, aceticlastic methanogens and hydrogenotrophic methanogens. Non-microbial particulate species include complex organics that either enter the process in the influent or that result from the death and decay of microbial species and the products of disintegration of the complex organics. This latter group consists of carbohydrates, proteins and LCFAs. Substrate conversion processes are described by a number of kinetic expressions that describe the conversion rates in terms of substrate concentrations and rate constants. The disintegration of Xc and hydrolysis of Xch, Xpr and Xli are described by first order rate expressions. Substrate conversion processes have Monod-type kinetic expressions while endogenous decay processes are first order in biomass concentration. It should be noted that the ADM1 model differs from the ASM models in that microbially mediated processes are defined in terms of substrate conversion as opposed to microbial growth. For each of the above-mentioned processes the rate of generation of products is related to the process rate through stoichiometric coefficients. For example the rate of growth of an organism is related to the rate of substrate consumption through the yield coefficient for the organism on the substrate. This format is consistent with the approach that is employed in the ASM models. It is recognized that a number of the conversion processes that are active in anaerobic digestion of municipal sludges can be inhibited by the accumulation of intermediate products such as molecular hydrogen, ammonia or by extremes of pH. In the model, all microbially mediated substrate conversion processes are subject to inhibition by extremes of pH. All anaerobic oxidation
processes are subject to inhibition by accumulation of molecular hydrogen and aceticlastic methanogenesis is inhibited at elevated free ammonia concentrations. Inhibition that is caused by molecular hydrogen and free ammonia is implemented in the model by employing rate multipliers that reflect non-competitive inhibition. An empirical correlation is employed as a process rate multiplier to reflect the effects of extreme pH. Liquid–gas mass transfer of gaseous components (methane, carbon dioxide and molecular hydrogen) is described by mass transfer relationships. Hence the application of the model equations requires separate mass balances for the liquid and gas phases of the components.
3. Model application In this study a selected number of data sets were chosen from previously published reports on anaerobic digestion of municipal wastewater sludges. Data sets were selected to encompass a range of digester configurations and on the basis of the completeness of the data sets that would be employed for model inputs and for comparison with model predictions. In all cases, studies that employed actual sludges from municipal wastewater treatment plants were selected. The data sets that were employed in this study are described in Table 1. The ADM1 model employs a large number of constants and coefficients. Given the model complexity it was impossible to calibrate the model parameters with any of the data sets that were available. In the report describing the ADM1 model the authors reviewed the previously published reports on anaerobic digestion processes and presented recommended values for model parameters. For the purposes of this study the recommended model parameters were employed unless additional information was provided by the original researchers that allowed for an improved estimate of the model parameters. In order to achieve accurate model predictions it is important to define the properties of the sludge stream entering the digester. For organic substances, the ADM1 model defines these inputs in terms of soluble and particulate COD. For municipal sludges a majority Table 1 Data sets referenced in this study Digester configuration
Sludge source
References
Single stage mesophilic digestion Acid phase digestion
Mixed PS and WAS
Cacho Rivero et al. (2002) Eastman and Ferguson (1981) Han and Dague (1995) Han et al. (1997) Ghosh (1987)
Temperature-phased anaerobic digestion Two-phase digestion
PS PS Mixed PS and WAS Mixed PS and WAS
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of the organic loading is associated with the particulate COD. The particulate COD entering the digester is defined in terms of biodegradable (Xc) and non-biodegradable components. Estimation of these parameters is often challenging for many data sets as in many cases the sludge COD is not reported and in almost all cases the biodegradable fraction is not independently measured. In most cases the sludge is characterized in terms of its volatile solids content. The relationship between volatile solids content and COD will depend upon the relative contribution of primary (PS) and waste activated sludge (WAS) to the sludge composition (Parkin and Owen, 1986). Primary sludges typically contain approximately 2.0 kg COD/kg VS while WAS typically has a value of 1.4 kg COD/kg VS for this parameter. The inlet COD can therefore be estimated on the basis of these typical values if the relative contributions of PS and WAS are known. The biodegradable fraction of the sludge particulate COD will also be a function of the sludge make-up. Primary sludges have been estimated to have a COD ‘‘ultimate’’ biodegradability of 69% (Parkin and Owen, 1986). The biodegradable fraction of WAS is dependent upon the sludge age that is employed in the aeration process (Gossett and Belser, 1982). Sludges that have extended solids residence times (SRT) in the aeration basin will have been highly oxidized and hence will be relatively recalcitrant to biodegradation in anaerobic digestion. The ultimate biodegradability of WAS has been found to range from 30% to 50% over the range of SRTs typically employed in wastewater treatment processes. Hence, it is apparent that accurate application of the model requires a detailed characterization of the inlet sludge composition. The sludge composition should be determined in terms of COD and the biodegradable fraction should be determined. This latter parameter could be determined through the use of a long term batch digestion test to identify the maximum biodegradability of the sludge. While there are no standard protocols for such a test, existing anaerobic biodegradability protocols could presumably be adapted for this purpose. If the contribution of PS and WAS to the digester feed were to vary substantially with time, then this testing should be performed on the PS and WAS streams separately. The properties of the composite sludge as a function of time could subsequently be estimated. The ADM1 model also estimates the behaviour of nitrogen compounds in anaerobic digestion. In the cases of municipal sludges the presence of ammonia nitrogen in the inlet and the release of ammonia from decay of solids has a substantial influence on the buffering of pH. As will be demonstrated later in this paper the concentration of ammonia/ammonium in the inlet can have a substantial impact upon the pH of acid-phase digesters that have a relatively short SRT. In addition, the digestion of highly concentrated sludges can result in the re-
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lease of elevated concentrations of ammonia that can be inhibitory to aceticlastic methanogens (IWA, 2002). It is therefore important to characterize the concentration of ammonia/ammonium in the digester inlet as well as the nitrogen content of the sludges. It should be noted that the ADM1 model does not maintain a perfect mass balance on nitrogen (Blumensaat and Keller, 2005). Ammonium that is taken up by microbial growth is not completely released during subsequent decay. Hence, it can be expected that the model will underestimate the concentrations of ammonium. The data sets employed in this study did not contain all of the information that was previously described. Where necessary, typical values were assumed. The impact of these assumptions on model predictions will be subsequently discussed. 3.1. Single stage mesophilic digestion Cacho Rivero et al. (2002) reported a study that assessed the impact of digester SRT on mesophilic anaerobic digestion of mixed PS and WAS. A series of digesters were operated over SRTs ranging from 5 to 40 days. In their paper the sludge COD, ammonia and TKN content and VFA composition were detailed. For this study, the biodegradable COD was estimated by extrapolating the results that were obtained for extended SRTs. In their study COD removal, ammonia and TKN content as well as VFA concentrations in the digested sludges were reported and were employed for comparison with the model predictions. The comparison of the model predictions for effluent COD, NH4/NH3-N, and VFAs is summarized in Fig. 2. The error bars in Fig. 2 represent 1 standard deviation of the experimental data. From Fig. 2 it can be seen that the model was able to predict the effluent COD with considerable accuracy. Nitrogen concentrations were accurately predicted for the shorter SRTs and while the trend of increasing concentrations with increasing SRT was reproduced, the absolute values that were predicted at longer SRTs were somewhat lower that the observed values. The differences in nitrogen concentrations may have been due to the lack of mass balance on nitrogen in the ADM1 model. It would be expected that under conditions where there is substantial solids destruction that the model would underestimate the concentrations of ammonium-nitrogen. The differences between the model predictions and the observed results may also have resulted from differences between the assumed and the actual protein content of the sludge. The reference did not provide any information on the distribution of carbohydrates, proteins and lipids in the sludge and hence the default model values were employed for this parameter. The model predictions for VFA concentrations were relatively accurate for SRTs greater than or equal to
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Fig. 2. Comparison of model predictions with data of Cacho Rivero et al. (2002).
10 days. However the model clearly overpredicted the concentration of acetate while underpredicting the concentrations of propionate, butyrate and valerate. These results suggest that the rates of oxidation of propionate, butyrate and valerate were somewhat overestimated by the model and this would partially, but not completely, explain the elevated acetate concentrations. It would appear that the rate at which acetate was converted to methane at the lower SRT was somewhat underestimated. This may have resulted from either underestimation of the substrate consumption coefficients for aceticlastic methanogenesis or an overestimation of the
inhibition of this activity by ammonia. The model predicted a 40% reduction in the activity of these organisms due to the presence of ammonia. The impact of reduced rates of aceticlastic activity on model predictions would be greatest at the lower SRTs. 3.2. Acid phase digestion Eastman and Ferguson (1981) performed one of the first detailed studies on the acid-phase digestion of municipal sludges. In their study, the impact of HRT was assessed over a range from 9 to 36 h. The impact of seed
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culture was also evaluated. The model does not have the capability to address this parameter and hence only the tests that were conducted with raw sludge as the seed were employed for this analysis. The model predictions for ammonia/ammonium-N, pH and total volatile acids (as acetate) were compared with the observed values in Fig. 3. From Fig. 3 it can be seen that the model somewhat underpredicted the organic acid concentrations at the lowest SRT of 9 h and overpredicted these values for the longest SRT of 72 h. The underprediction of acid 12
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concentrations at 9 h is in agreement with the overestimation of the effluent pH in this test. It would appear that the model underestimated the rates of disintegration, hydrolysis and acidification under these relatively extreme conditions of SRT and pH. It should be noted that the model does not correct any of the disintegration or hydrolysis rates for pH. Ghosh (1987) has demonstrated that the rate of hydrolysis is influenced by pH. An improvement of the model for addressing acid phase digesters would be to include a rate correction term for hydrolysis processes. While not presented in Fig. 3 it must be noted that although Eastman and Ferguson (1981) observed methane production at the longer SRTs the model did not predict the generation of appreciable quantities of methane under these conditions. The conversion of VFAs to methane in the experimental data may explain the higher modelpredicted VFA concentrations relative to the observed values. The results suggest that methanogens are less sensitive to pH than the pH inhibition functions suggest. The ammonia-nitrogen concentrations were underpredicted at the lowest SRTs and overpredicted at the highest SRTs. These results tend to confirm the model predictions of VFA concentrations since an underprediction of solids destruction and hydrolysis, as indicated by reduced VFAs, would also result in a reduced release of ammonium. 3.3. Temperature-phased anaerobic digestion (TPAD)
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TPAD processes consist of reactors operating at thermophilic and mesophilic temperatures in series. While either process may be first, the most common orientation has the thermophilic digester ahead of the mesophilic digester. For the purposes of this study two papers on TPAD digestion were referenced; one that studied digestion of PS alone (Han and Dague, 1995) and one that studied a mix of PS and WAS (Han et al., 1997). In the former paper the ratio of the volumes of the first and second digesters was 1:2. In the latter paper two systems were operated with system A having a ratio of volumes of 1:2.5 while system B had a ratio of volumes of 1:5. In all of the systems the mesophilic temperature was 35 °C while the thermophilic temperature was 55 °C. A comparison of the model predictions with the data presented in the paper of Han and Dague (1995) is summarized in Fig. 4. The comparison of model predictions with the results of Han et al. (1997) are presented for systems A and B in Figs. 5 and 6 respectively. It should be noted that the model does not explicitly predict volatile solids removal (VSR). For the purposes of this paper it was assumed that the removal of volatile solids was proportional to the removal of overall COD. This assumes that all of the COD remaining after digestion have the same ratio of volatile solids concentration:COD. This undoubtedly introduces some
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Fig. 4. Comparison of model predictions with data of Han and Dague (1995).
error in the estimates however, there was generally insufficient data on the composition of the digester effluent to perform a more refined conversion of COD to solids concentrations. The patterns with respect to the model predictions and observed data that are presented in Figs. 4–6 are consistent. In all three cases, the model overpredicted the production of methane by the temperature-phased processes. It should be noted that in the papers only total methane production was reported and hence it was not possible to compare methane production from the two reactors separately. In all cases the extent of overprediction was greatest for the lower SRTs and predictions improved for the longer SRTs. The predictions for VSR were best for the results presented in Fig. 4 while in Figs. 5 and 6 the model consistently overpredicted the VSR. The overprediction of VSR was consistent with the overprediction of methane generation. In all three cases the model substantially overpredicted the concentrations of VFAs in the thermophilic reactor. The greatest overprediction was associated with the shortest SRTs and the predictions improved at longer SRTs. With the exception of the 10 day SRT in Fig. 4 the model tended to underpredict the concentrations of VFAs in the mesophilic second stage digester.
The results suggest that for thermophilic conditions the model overpredicts the generation of volatile fatty acids and that this is accentuated at shorted SRTs. It should be noted that at low SRTs the model predicted substantial inhibition of the acetoclastic methanogens due to low pH (IWA, 2002). It may be that the inhibition functions for this process were too severe. Although the model predicted high VFA concentrations in the thermophilic phase reactor, it predicted that essentially all of the VFAs could be converted in the second phase mesophilic reactor. The predicted effluent concentrations were actually lower than those that were observed. It should be noted that in this modeling effort the biomass that was present in the first phase reactor was allowed to flow into the second phase reactor and remain active at the new temperature. This may have resulted in the overprediction of activity in the latter reactor as it is unlikely that all of the thermophilic biomass leaving the first digester would remain viable in the second stage digester. It may be more appropriate to assume that the biomass entering the second digester should be considered as biodegradable particulate organic matter. The differences between the model predictions and the observed values of the VFA concentrations may also
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W.J. Parker / Bioresource Technology 96 (2005) 1832–1842
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have been due to the procedure used to adjust the biokinetic coefficients for temperature. In the model documentation (IWA, 2002) it is suggested that a constant correction factor be employed for all of the microbial species. Implementing this strategy tends to result in an accumulation of VFAs at the higher temperatures. It may be that differing temperature correction factors should be employed for the different microbial species.
3.4. Two-phase anaerobic digestion In two-phase anaerobic digestion the first digester is operated at a short SRT to wash out methanogenic bacteria and promote the establishment of an acidic environment. In the second stage digester the VFAs that are generated in the first stage are converted to methane. In the study reported by Ghosh (1987) a number of experiments were performed with digesters operating at both mesophilic and thermophilic conditions. For the purposes of this paper only the tests that were performed under mesophilic conditions were examined. Testing was conducted with total SRTs of 3 days and 7 days and an influent TS concentration of 7%. With
the 3 day SRT the first stage had an SRT of 0.9 days while the second stage had an SRT of 2.1 days. With the 7 day SRT the first stage had an SRT of 2 days and the second stage had an SRT of 5 days. In Tables 2 and 3 a comparison of some of the model predictions and the reported experimental values are presented. From Table 2 it can be see that with the exception of the first stage of the 7 day SRT digesters the model predictions for VFAs were relatively close to the observed values and the pH values for the second stage digesters were also well predicted. The paper did not report the first stage pHs and hence it was not possible to use this parameter to evaluate the predictions for VFAs. The overprediction of VFAs for the short SRT reactors was consistent with that observed in the previously described temperature phased digestion studies. The model significantly under-predicted the NH4-N concentrations for the 3 day SRT system while this response was relatively well predicted for the 7 day SRT system. It should be noted that there appeared to be an inconsistency in the data for this response since the observed values for the 3 day SRT system were substantially higher than the 7 day system. This seems to be inconsistent with the VSR data that will be subsequently
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Table 2 Comparison of volatile acids, pH And NH4-N for two phase digestion
Table 3 Comparison of VSR for two phase digestion
Response
Response
SRT = 3 days
SRT = 7 days
Stage 1
Stage 2
Stage 1
Stage 2
Vol. acids (mg/l) Exper. NA Model 3811
1680 1393
1610–1810 6711
109 180
pH Exper. Model
7.2 7.0
NA 5.2
7.3 7.3
1820 899
NA 766
1049 961
NA 5.8
NH4-N (mg/l) Exper. NA Model 472
described which indicated higher solids reduction for the longer SRT system. The predicted VSR values along with three different measures of VSR for the experimental data that were reported in the original paper are presented in Table 3. From Table 3 it can be seen that the model predictions were within the range of values that were reported in the papers. It should however be noted that the range of values reported in the paper was quite wide and hence the assessment of the model predictions could not be very rigorous.
VSR (%) SRT = 3 days
SRT = 7 days
Model
34.0
42.0
Experimental MOPa Weight of gasb Theor. gas yieldc
26.5 35.5 28.3
33.6 51.5 43.4
a
VS reduction was calculated as: VSR = 100 * (VS1 VS0)/ [VS1 (VS1 * VS0)]. b VS reduction was calculated as VSR = 100 * (weight of gas/weight of VS fed). c VS reduction was calculated as VSR = 100 * (observed gas yield/ theoretical gas yield of 1.078 SCFM/kg VS added).
4. Discussion In this paper the predictions of the ADM1 model using the default values for most of the model coefficients were able to reflect most of the trends that were reported for a variety of digester configurations. There were however consistent deviations between the model predictions and observed values for VFAs when the
W.J. Parker / Bioresource Technology 96 (2005) 1832–1842
model was employed to predict the behaviour of low SRT systems. In the two phase systems the model was often able to perform reasonably in predicting second stage concentrations of VFAs as with the longer SRTs in these stages the rates of VFA conversion were able to compensate for the high inlet concentrations of VFAs. It would appear that there could be improvements made to the model in the estimation of VFA concentrations under these conditions. It may be necessary to more closely examine the relationship between pH and rate coefficients in this regard. For the purposes of this study it was often necessary to estimate the values that were input into the model for sludge characteristics such as COD, biodegradable fraction of the COD, TKN and NH4-N. These have a substantial influence on model predictions. If the model is to be used as an analysis and design tool it would benefit from more careful characterization of these parameters. A standardized protocol for determining the anaerobically biodegradable fraction of the sludge COD would assist in this regard. The model predictions for VSR that were reported in this paper assumed that the reductions in volatile solids are proportional to the reductions in COD. However, it is known that the COD content of volatile solids depends upon the sludge source and its degree of stabilization. Hence, the estimated values for VSR likely contain error. The extent of this error has not been quantified for this paper. For more accurate predictions of VSR the COD contents of volatile solids in the feed sludge should be accurately characterized. In addition, the use of typical values for the COD content of digested sludge should be employed to convert predicted COD concentrations to VS concentrations. In this implementation of the model it was assumed that for digesters in series the biomass which moved from one digester to another would be active in the downstream reactor. This assumption should be valid for two-phase systems where the digester temperatures are the same in both digesters. Implementation of the model in this manner for temperature-phased configurations requires more analysis as it is likely that the biomass entering the second stage digester will be somewhat less active than the model predicts.
5. Conclusions The ADM1 model is a powerful tool for predicting the behaviour of anaerobic digesters treating municipal sludges. However, for successful simulation the feed stream should be well characterized with respect to itÕs COD content and the biodegradable fraction of this material. A standardized protocol for measuring the latter parameter would further use of the model by the
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industry. The ammonia and TKN concentrations present in the feed need to be well characterized because of their impact on pH buffering and inhibition functions. The model tended to overpredict VFA concentrations for reactors that were operated at reduced SRTs. This was observed for both mesophilic and thermophilic digesters. The results suggest that the inhibition function for pH may over emphasize the impact of reduced pH on biological activity. In addition, the model does not incorporate a pH function for the disintegration and hydrolysis processes. This will have some impact on low SRT systems that tend to operate at reduced pHs. The relationship between COD and VS concentrations for digested sludges should be established. This would improve the estimates of VSR since the model only predicts COD concentrations. Implementation of the model for temperature-phased systems should be further examined since the current implementation assumes that the biomass leaving the upstream digesters can become active in the downstream digesters at the downstream temperature. It does not seem that this would be likely for thermophiles entering a mesophilic digester. The contribution of the incoming biomass to the activity of the digesters should be further quantified.
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