Monofermentation of grass silage under mesophilic conditions: measurements and mathematical modeling with ADM 1

Modifications to the anaerobic digestion model no. 1 (ADM1) for enhanced understanding and application of the anaerobic treatment processes - A comprehensive review

Anaerobic digestion (AD) is a promising method for the recovery of resources and energy from organic wastes. Correspondingly, AD modelling has also been developed in recent years. The International Water Association (IWA) Anaerobic Digestion Model No. 1 (ADM1) is currently the most commonly used structured AD model. However, as substrates become more complex and our understanding of the AD mechanism grows, both systematic and specific modifications have been applied to the ADM1. Modified models have provided a diverse range of application besides AD processes, such as fermentation and biogas upgrading processes. This paper reviews research on the modification of the ADM1, with a particular focus on processes, kinetics, stoichiometry and parameters, which are the major elements of the model. The paper begins with a brief introduction to the ADM1, followed by a summary of modifications, including extensions to the model structure, modifications to kinetics (including inhibition functions) and stoichiometry, as well as simplifications to the model. The paper also covers kinetic parameter estimation and validation of the model, as well as practical applications of the model to a variety of scenarios. The review highlights the need for improvements in simulating AD and biogas upgrading processes, as well as the lack of full-scale applications to other substrates besides sludge (such as food waste and agricultural waste). Future research directions are suggested for model development based on detailed understanding of the anaerobic treatment mechanisms, and the need to recover of valuable products.

Review on solid-state anaerobic digestion of lignocellulosic biomass and organic solid waste

Sustainable management of organic solid wastes especially the municipal solid waste (MSW) is essential for the realization of various sustainable development goals (SDGs). Resource recovery centric waste processing technologies generate valorizable products to meet the operations and maintenance (O&M) costs while reducing the GHG emissions. Solid-state anaerobic digestion (SSAD) of organic solid wastes is a biomethanation process performed at a relatively higher total solids (TS) loading in the range of 10-45%. SSAD overcomes various limitations posed by conventional anaerobic slurry digesters such as higher degradable matter per unit volume of the bioreactor resulting in a smaller footprint, low freshwater consumption, low wastewater generation, simple upstream and downstream processes, relatively lower operation, and maintenance costs. This review elucidates the recent developments and critical assessment of different aspects of SSAD, such as bioreactor design, operational strategy, process performances, mass balance, microbial ecology, applications, and mathematical models. A critical assessment revealed that the operating scale of SSAD varies between 1000 and 100,000 ts/year at organic loading rate (OLR) of 2-15 g volatile solids (VS)/L·day. The SSAD experiences process failures due to the formation of volatile fatty acids (VFAs), biogas pockets and clogging of the digestate outlet. Acclimatization of microbes accelerates the startup phase, steady-state performances, and the enrichment of syntrophic microbes with 10-50 times greater population of cellulolytic and xylanolytic microbes in thermophilic SSAD over mesophilic SSAD. Experimental limitations in the accurate determination of rate constants and the oversimplification of biochemical reactions result in an inaccurate prediction by the models.

A Kinetic Model for Anaerobic Digestion and Biogas Production of Plant Biomass under High Salinity

The aim of this study is to evaluate the anaerobic digestion and biogas production of plant biomass under high salinity by adopting a theoretical and technical approach for saline plant-biomass treatment. Two completely mixed lab-scale mesophilic reactors were operated for 480 days. In one of them, NaCl was added and the sodium ion concentration was maintained at 35.8 g-Na⁺·L⁻¹, and the organic loading rate was 0.58-COD·L⁻¹·d⁻¹–1.5 g-COD·L⁻¹·d⁻¹; the other added Na₂SO₄–NaHCO₃ and kept the sodium ion concentration at 27.6 g-Na⁺·L⁻¹ and the organic loading rate at 0.2 g-COD·L⁻¹·d⁻¹–0.8 g-COD·L⁻¹·d⁻¹. The conversion efficiencies of the two systems (COD to methane) were 66% and 54%, respectively. Based on the sulfate-reduction reaction and the existing anaerobic digestion model, a kinetic model comprising 12 types of soluble substrates and 16 types of anaerobic microorganisms was developed. The model was used to simulate the process performance of a continuous anaerobic bioreactor with a mixed liquor suspended solids (MLSS) concentration of 10 g·L⁻¹–40 g·L⁻¹. The results showed that the NaCl system could receive the influent up to a loading rate of 0.16 kg-COD/kg-MLSS·d⁻¹ without significant degradation of the methane conversion at 66%, while the Na₂SO₄–NaHCO₃ system could receive more than 2 kg-COD·kg⁻¹-MLSS·d⁻¹, where 54% of the fed chemical oxygen demand (COD) was converted into methane and another 12% was observed to be sulfide.

Modelling of autogenerative high-pressure anaerobic digestion in a batch reactor for the production of pressurised biogas

Background

Pressurised anaerobic digestion allows the production of biogas with a high content of methane and, at the same time, avoid the energy costs for the biogas upgrading and injection into the distribution grid. The technology carries potential, but the research faces practical constraints by a.o. the capital investment needed in high-pressure reactors and sensors and associated sampling limitations. In this work, the kinetic model of an autogenerative high-pressure anaerobic digestion of acetate, as the representative compound of the aceticlastic methanogenesis route, in batch configuration, is proposed to predict the dynamic performance of pressurised digesters and support future experimental work. The modelling of autogenerative high-pressure anaerobic digestion in batch configuration, which is not extensively studied and simulated in the present literature, was developed, calibrated, and validated by using experimental results available from the literature.

Results

Under high-pressure conditions, the assessment of the Monod maximum specific uptake rate, the half-saturation constant and the first-order decay rate was carried out, and the values of 5.9 kg COD kg COD⁻¹ d⁻¹, 0.05 kg COD m⁻³ and 0.02 d⁻¹ were determined, respectively. By using the predicted values, excellent fittings of the final pressure, the CH₄ molar fraction and the specific methanogenic yield calculation were obtained. Likewise, the variation in the gas–liquid mass transfer coefficient by several orders of magnitude showed negligible effects on the model predictive values in terms of methane molar fraction of the produced biogas, while the final pressure seemed to be slightly influenced.

Conclusions

The proposed model allowed to estimate the Monod maximum specific uptake rate for acetate, the half-saturation rate for acetate and the first-order decay rate constant, which were comparable with literature values reported for well-studied methanogens under anaerobic digestion at atmospheric pressure. The methane molar fraction and the final pressure predicted by the model showed different responses towards the variation of the gas–liquid mass transfer coefficient since the former seemed not to be affected by the variation of the gas–liquid mass transfer coefficient; in contrast, the final pressure seemed to be slightly influenced. The proposed approach may also allow to potentially identify the methanogens species able to be predominant at high pressure.

Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.

Biogas maximization using data-driven modelling with uncertainty analysis and genetic algorithm for municipal wastewater anaerobic digestion

Anaerobic digestion processes create biogases that can be useful sources of energy. The development of data-driven models of anaerobic digestion processes via operating parameters can lead to increased biogas production rates, resulting in greater energy production, through process modification and optimization. This study assessed processed and unprocessed input operating parameter variables for the development of regression models with transparent structures ('white-box' models) to: (1) estimate biogas production rates from municipal wastewater treatment plant (MWTP) anaerobic digestors; (2) compare their performances to artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with opaque structures ('black-box' models) using Monte Carlo Simulation for uncertainty analysis; and (3) integrate the models with a genetic algorithm (GA) to optimize operating parameters for maximization of MWTP biogas production rates. The input variables were anaerobic digestion operating parameters from a MWTP including volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate, which were processed via correlation tests and principal component analysis. Overall, the results indicated that the processed data did not improve regression model performances. Additionally, the developed non-linear regression model with the unprocessed inputs had the best performance based on values including R = 0.81, RMSE = 0.95, and IA = 0.89. However, this model was less accurate, but interestingly had less uncertainty, as compared to ANN and ANFIS models which indicates the compromise between model accuracy and uncertainty. Thus, all three models were coupled with GA optimization with maximum biogas production rate estimates of 22.0, 23.1, and 28.6 m³/min for ANN, ANFIS, and non-linear regression models, respectively.

Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) - Laboratory experiments and model application

Due to a limited number of available measurements on agricultural biogas plants, established process models, such as the Anaerobic Digestion Model No. 1 (ADM1), are rarely applied in practise. To provide a reliable basis for model-based monitoring and control, different model simplifications of the ADM1 were implemented for process simulation of semi-continuous anaerobic digestion experiments using agricultural substrates (maize silage, sugar beet silage, rye grain and cattle manure) and industrial residues (grain stillage). Individual model structures enable a close depiction of biogas production rates and characteristic intermediates (ammonium nitrogen, propionic and acetic acid) with equal accuracy as the original ADM1. The impact of different objective functions and standard parameter values on parameter estimates of first-order hydrolysis constants and microbial growth rates were evaluated. Due to the small number of required model parameters and suitable system characteristics, simplified model structures show clear advantages for practical application on agricultural biogas plants.

Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) - Model development and stoichiometric analysis

Rigorous process models provide a reliable basis for model-based monitoring and control of anaerobic digestion plants. Due to the complex model structure and non-linear system characteristics, the established Anaerobic Digestion Model No. 1 (ADM1) is rarely applied in industrial plant operation. The present investigation proposes a systematic procedure for successive model simplification and presents the description of five model variants of a mass-based ADM1. Individual model structures greatly differ in their number of implemented process phases, characteristic components and required parameters. Simplified model variants combine nutrient degradation and biogas formation based on first-order sum reactions, whereas complex model structures describe individual degradation pathways and intermediates during acido- and acetogenesis. Characteristic features of the derived model structures as well as the stoichiometric methane potentials and microbial biomass yields of the underlying degradation pathways of individual model variations are evaluated and discussed in detail.

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.

Wet biowaste digestion: ADM1 model improvement by implementation of known genera and activity of propionate oxidizing bacteria

Anaerobic digestion of biowaste not only reduces environmental burden but also plays an important role for sustainable energy supply. For process optimization simulation based on the Anaerobic Digestion Model No. 1 (ADM1) is commonly used. The ADM1 was extended to include the known three genera of propionate oxidizing bacteria (POB) and the two routes of propionate degradation (methyl-malonyl CoA and C6-dismutation pathway). Kinetic parameters for anaerobic propionate oxidation by single strains of the three propionate oxidizing genera were determined from defined tri-cultures of the POB with hydrogenotrophic and acetotrophic methanogens and implemented into ADM1. The such improved model ADM1xpro was evaluated with operational data from a full scale wet biowaste digestion plant. Predicted amounts of biogas and composition with ADM1xpro (2201 m³ d^-1, 68.1 % CH₄ and 31.9 % CO₂) correlated well with full-scale process data (2171 m³ d^-1, 67.5 % CH₄ and 31.9 % CO₂).

Fast characterization of solid organic waste content with near infrared spectroscopy in anaerobic digestion

The development of anaerobic digestion involves both co-digestion of solid wastes and optimization of the feeding recipe. Within this context, substrate characterisation is an essential issue. Although it is widely used, the biochemical methane potential is not sufficient to optimize the operation of anaerobic digestion plants. Indeed the biochemical composition in carbohydrates, lipids, proteins and the chemical oxygen demand of the inputs are key parameters for the optimisation of process performances. Here we used near infrared spectroscopy as a robust and less-time consuming tool to predict the solid waste content in carbohydrates, lipids and nitrogen, and the chemical oxygen demand. We built a Partial Least Square regression model with 295 samples and validated it with an independent set of 46 samples across a wide range of solid wastes found in anaerobic digestion units. The standard errors of cross-validation were 90mgO₂⋅gTS^-1 carbohydrates, 2.5∗10^-2g⋅gTS^-1 lipids, 7.2∗10^-3g⋅gTS^-1 nitrogen and 99mgO₂⋅gTS^-1 chemical oxygen demand. The standard errors of prediction were 53mgO₂⋅gTS^-1 carbohydrates, 3.2∗10^-2g⋅gTS^-1 lipids, 8.6∗10^-3g⋅gTS^-1 nitrogen and 83mgO₂⋅gTS^-1 chemical oxygen demand. These results show that near infrared spectroscopy is a new fast and cost-efficient way to characterize solid wastes content and improve their anaerobic digestion monitoring.

Modelling anaerobic digestion in a biogas reactor: ADM1 model development with lactate as an intermediate (Part I)

The Anaerobic Digestion Model No. 1 (ADM1) was extended to include lactate, a crucial metabolic product during sugar fermentation. This study tests the validity of the modified ADM1 model in improving the predictions of a standard biogas reactor. This reactor was prepared in the laboratory with simple organic substrates with an intention to represent an 'average biogas plant'. Kinetic parameters were determined from a lactic acid enriched steady-state reactor. The parameters were adjusted further in order to acquire satisfying simulation results systematically with the batch experiments and then against the standard biogas reactor. Arresting methanogenesis revealed that lactate degradation occurred majorly via acetate followed by propionate, and a non-negligible proportion of butyrate too was found, which were further updated in the model. The modified ADM1 provided a successful correlation with the experimental results for the batch and continuous experiments. We justified that inclusion of lactate in the model resulted in optimized simulation for both biogas and methane content in the standard biogas reactor.

Modelling anaerobic digestion in an industrial biogas digester: Application of lactate-including ADM1 model (Part II)

A modified Anaerobic Digestion Model No. 1 (ADM1xp) including lactate was applied to a full-scale biogas plant. This model considers monosaccharides to degrade through lactic acid, which further degrades majorly into acetate followed by propionate and butyrate. Experimental data were derived from the previous works in the same laboratory, and the proposed parameters were validated against batch experiments. After successful validation, the biogas plant bearing a fermenter size of 7 dam³ and operated with food waste and cattle manure was simulated. The biogas production and methane content were reliably simulated, and a good fit could be obtained against the experimental data with an average difference of less than 1%. When compared to the original ADM1 model, the performance of the lactate-incorporated model was found to be improved. Inclusion of lactate as a parameter in the ADM1xp model is recommended for an increased sensitivity and enhanced prediction principally for systems dealing with high carbohydrate and lactate loads.

Co-digestion of manure with grass silage and pulp and paper mill sludge using nutrient additions

There is an increasing worldwide demand for biogas. Anaerobic co-digestion involves the treatment of different substrates with the aim of improving the production of biogas and the stability of the process. This study evaluates how methane production is affected by the co-digestion of pig and dairy manure with grass silage and pulp and paper mill sludge and assesses whether methane production is affected by factors other than nutrient deficiency, low buffering capacity, inadequate dilution, and an insufficient activity and amount of microorganism culture. Anaerobic digestion was performed in batch reactors under mesophilic conditions for 20 days. The season of grass silage and manure collection proved to be an important factor affecting methane production. Spring grass silage produced a maximum of 250 mL/VSadded and spring manure 150 mL/VSadded, whereas autumn grass silage produced at most 140 ml/VSadded and autumn manure 45 mL/VSadded. The pulp mill sludge used is comprised of both primary and secondary sludge and produced at most 50 mL/VSadded regardless of season; this substrate benefitted most from co-digestion.

Modelling anaerobic co-digestion in Benchmark Simulation Model No. 2: Parameter estimation, substrate characterisation and plant-wide integration

Anaerobic co-digestion is an emerging practice at wastewater treatment plants (WWTPs) to improve the energy balance and integrate waste management. Modelling of co-digestion in a plant-wide WWTP model is a powerful tool to assess the impact of co-substrate selection and dose strategy on digester performance and plant-wide effects. A feasible procedure to characterise and fractionate co-substrates COD for the Benchmark Simulation Model No. 2 (BSM2) was developed. This procedure is also applicable for the Anaerobic Digestion Model No. 1 (ADM1). Long chain fatty acid inhibition was included in the ADM1 model to allow for realistic modelling of lipid rich co-substrates. Sensitivity analysis revealed that, apart from the biodegradable fraction of COD, protein and lipid fractions are the most important fractions for methane production and digester stability, with at least two major failure modes identified through principal component analysis (PCA). The model and procedure were tested on bio-methane potential (BMP) tests on three substrates, each rich on carbohydrates, proteins or lipids with good predictive capability in all three cases. This model was then applied to a plant-wide simulation study which confirmed the positive effects of co-digestion on methane production and total operational cost. Simulations also revealed the importance of limiting the protein load to the anaerobic digester to avoid ammonia inhibition in the digester and overloading of the nitrogen removal processes in the water train. In contrast, the digester can treat relatively high loads of lipid rich substrates without prolonged disturbances.

Application of ADM1 for modeling of biogas production from anaerobic digestion of Hydrilla verticillata

The present study focused on the application of anaerobic digestion model no. 1 (ADM1) to simulate biogas production from Hydrilla verticillata. Model simulation was carried out by implementing ADM1 in AQUASIM 2.0 software. Sensitivity analysis was used to select the most sensitive parameters for estimation using the absolute-relative sensitivity function. Among all the kinetic parameters, disintegration constant (kdis), hydrolysis constant of protein (khyd_pr), Monod maximum specific substrate uptake rate (km_aa, km_ac, km_h2) and half-saturation constants (Ks_aa, Ks_ac) affect biogas production significantly, which were optimized by fitting of the model equations to the data obtained from batch experiments. The ADM1 model after parameter estimation was able to well predict the experimental results of daily biogas production and biogas composition. The simulation results of evolution of organic acids, bacteria concentrations and inhibition effects also helped to get insight into the reaction mechanisms.

Modelling the anaerobic digestion of solid organic waste - Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach

This work proposes a novel and rigorous substrate characterisation methodology to be used with ADM1 to simulate the anaerobic digestion of solid organic waste. The proposed method uses data from both direct substrate analysis and the methane production from laboratory scale anaerobic digestion experiments and involves assessment of four substrate fractionation models. The models partition the organic matter into a mixture of particulate and soluble fractions with the decision on the most suitable model being made on quality of fit between experimental and simulated data and the uncertainty of the calibrated parameters. The method was tested using samples of domestic green and food waste and using experimental data from both short batch tests and longer semi-continuous trials. The results showed that in general an increased fractionation model complexity led to better fit but with increased uncertainty. When using batch test data the most suitable model for green waste included one particulate and one soluble fraction, whereas for food waste two particulate fractions were needed. With richer semi-continuous datasets, the parameter estimation resulted in less uncertainty therefore allowing the description of the substrate with a more complex model. The resulting substrate characterisations and fractionation models obtained from batch test data, for both waste samples, were used to validate the method using semi-continuous experimental data and showed good prediction of methane production, biogas composition, total and volatile solids, ammonia and alkalinity.

Microbial kinetic for In-Storage-Psychrophilic Anaerobic Digestion (ISPAD)

In-Storage-Psychrophilic-Anaerobic-Digestion (ISPAD) is a wastewater storage tank converted into an anaerobic digestion (AD) system by means of an airtight floating geo-membrane. For process optimization, ISPAD requires modelling with well-established microbial kinetics coefficients. The present objectives were to: obtain kinetics coefficients for the modelling of ISPAD; compare the prediction of the conventional and decomposition fitting approach, an innovative fitting technique used in other fields of science, and; obtain equations to predict the maximum growth rate (μmax) of microbial communities as a function of temperature. The method consisted in conducting specific Substrate Activity Tests (SAT) using ISPAD inoculum to monitor the rate of degradation of specific substrates at 8, 18 and 35 °C. Microbial kinetics coefficients were obtained by fitting the Monod equations to SAT. The statistical procedure of Least Square Error analysis was used to minimize the Sum of Squared Errors (SSE) between the measured ISPAD experimental data and the Monod equation values. Comparing both fitting methods, the decomposition approach gave higher correlation coefficient (R) for most kinetics values, as compared to the conventional approach. Tested to predict μmax with temperature, the Square Root equation better predicted temperature dependency of both acidogens and propionate degrading acetogens, while the Arrhenius equation better predicted that of methanogens and butyrate degrading acetogens. Increasing temperature from 18 to 35 °C did not affect butyrate degrading acetogens, likely because of their dominance, as demonstrated by microbial population estimation. The estimated ISPAD kinetics coefficients suggest a robust psychrophilic and mesophilic coexisting microbial community demonstrating acclimation to ambient temperature.

Performance optimization and validation of ADM1 simulations under anaerobic thermophilic conditions

In this study, two experimental sets of data each involving two thermophilic anaerobic digesters treating food waste, were simulated using the Anaerobic Digestion Model No. 1 (ADM1). A sensitivity analysis was conducted, using both data sets of one digester, for parameter optimization based on five measured performance indicators: methane generation, pH, acetate, total COD, ammonia, and an equally weighted combination of the five indicators. The simulation results revealed that while optimization with respect to methane alone, a commonly adopted approach, succeeded in simulating methane experimental results, it predicted other intermediary outputs less accurately. On the other hand, the multi-objective optimization has the advantage of providing better results than methane optimization despite not capturing the intermediary output. The results from the parameter optimization were validated upon their independent application on the data sets of the second digester.

Do two-phase biogas plants separate anaerobic digestion phases? - a mathematical model for the distribution of anaerobic digestion phases among reactor stages

In this article a mathematical model is introduced, which estimates the distribution of the four anaerobic digestion phases (hydrolysis, acidogenesis, acetogenesis and methanogenesis) that occur among the leach bed reactor and the anaerobic filter of a biogas plant. It is shown that only the hydrolysis takes place in the first stage (leach bed reactor), while all other anaerobic digestion phases take place in both reactor stages. It turns out that, besides the usually measured raw materials of the acetogenesis and the methanogenesis phases (organic acids), it is also necessary to analyze the process liquid for raw materials of the acidogenesis phase, i.e., sugars, fatty acids, amino acids, etc. The introduced model can be used to monitor the inhibition of the anaerobic digestion phases in reactor stages and can, thus, help to improve the control system of biogas plants.

Activated zeolite--suitable carriers for microorganisms in anaerobic digestion processes?

Plant cell wall structures represent a barrier in the biodegradation process to produce biogas for combustion and energy production. Consequently, approaches concerning a more efficient de-polymerisation of cellulose and hemicellulose to monomeric sugars are required. Here, we show that natural activated zeolites (i.e. trace metal activated zeolites) represent eminently suitable mineral microhabitats and potential carriers for immobilisation of microorganisms responsible for anaerobic hydrolysis of biopolymers stabilising related bacterial and methanogenic communities. A strategy for comprehensive analysis of immobilised anaerobic populations was developed that includes the visualisation of biofilm formation via scanning electron microscopy and confocal laser scanning microscopy, community and fingerprint analysis as well as enzyme activity and identification analyses. Using SDS polyacrylamide gel electrophoresis, hydrolytical active protein bands were traced by congo red staining. Liquid chromatography/mass spectroscopy revealed cellulolytical endo- and exoglucanase (exocellobiohydrolase) as well as hemicellulolytical xylanase/mannase after proteolytic digestion. Relations to hydrolytic/fermentative zeolite colonisers were obtained by using single-strand conformation polymorphism analysis (SSCP) based on amplification of bacterial and archaeal 16S rRNA fragments. Thereby, dominant colonisers were affiliated to the genera Clostridium, Pseudomonas and Methanoculleus. The specific immobilisation on natural zeolites with functional microbes already colonising naturally during the fermentation offers a strategy to systematically supply the biogas formation process responsive to population dynamics and process requirements.

Application of Anaerobic Digestion Model No. 1 for describing anaerobic digestion of grass, maize, green weed silage, and industrial glycerine

Anaerobic digestion of organic waste plays an important role for the development of sustainable energy supply based on renewable resources. For further process optimization of anaerobic digestion, biogas production with the commonly used substrates, grass, maize, and green weed silage, together with industrial glycerine, were analyzed by the Weender analysis/van Soest method, and a simulation study was performed, based on the International Water Association's (IWA) Anaerobic Digestion Model No. 1 (ADM1). The simplex algorithm was applied to optimize kinetic constants for disintegration and hydrolysis steps for all examined substrates. Consequently, new parameters were determined for each evaluated substrate, tested against experimental cumulative biogas production results, and assessed against ADM1 default values for disintegration and hydrolysis kinetic constants, where the ADM1 values for mesophilic high rate and ADM1 values for solids were used. Results of the optimization lead to a precise prediction of the kinetics of anaerobic degradation of complex substrates.

A waste characterisation procedure for ADM1 implementation based on degradation kinetics

In this study, a procedure accounting for degradation kinetics was developed to split the total COD of a substrate into each input state variable required for Anaerobic Digestion Model n°1. The procedure is based on the combination of batch experimental degradation tests ("anaerobic respirometry") and numerical interpretation of the results obtained (optimisation of the ADM1 input state variable set). The effects of the main operating parameters, such as the substrate to inoculum ratio in batch experiments and the origin of the inoculum, were investigated. Combined with biochemical fractionation of the total COD of substrates, this method enabled determination of an ADM1-consistent input state variable set for each substrate with affordable identifiability. The substrate to inoculum ratio in the batch experiments and the origin of the inoculum influenced input state variables. However, based on results modelled for a CSTR fed with the substrate concerned, these effects were not significant. Indeed, if the optimal ranges of these operational parameters are respected, uncertainty in COD fractionation is mainly limited to temporal variability of the properties of the substrates. As the method is based on kinetics and is easy to implement for a wide range of substrates, it is a very promising way to numerically predict the effect of design parameters on the efficiency of an anaerobic CSTR. This method thus promotes the use of modelling for the design and optimisation of anaerobic processes.

Mathematical modeling of process liquid flow and acetoclastic methanogenesis under mesophilic conditions in a two-phase biogas reactor

Acetoclastic methanogenesis in the second stage of a two-phase biogas reactor is investigated. A mathematical model coupling chemical reactions with transport of process liquid and with the variation of population of the microorganisms living on the plastic tower packing of the reactor is proposed. The evolution of the liquid is described by an advection-diffusion-reaction equation, while a monod-type kinetic is used for the reactions. Moreover, a new inhibition factor MO(max) is introduced, which hinders the growth of microorganisms when the plastic tower packing is overpopulated. After estimating the reaction parameters, the acetate outflow measured experimentally is in good agreement with that predicted by simulations. For coupling liquid transport with reaction processes, a spatial discretization of the reactor is performed. This yields essential information about the distribution of acetate and the production of methane in the reactor. This information allows for defining a measure of the effectiveness of the reactor.

ADM1-based modeling of methane production from acidified sweet sorghum extract in a two stage process

The present study focused on the application of the Anaerobic Digestion Model 1 on the methane production from acidified sorghum extract generated from a hydrogen producing bioreactor in a two-stage anaerobic process. The kinetic parameters for hydrogen and volatile fatty acids consumption were estimated through fitting of the model equations to the data obtained from batch experiments. The simulation of the continuous reactor performance at all HRTs tested (20, 15, and 10d) was very satisfactory. Specifically, the largest deviation of the theoretical predictions against the experimental data was 12% for the methane production rate at the HRT of 20d while the deviation values for the 15 and 10d HRT were 1.9% and 1.1%, respectively. The model predictions regarding pH, methane percentage in the gas phase and COD removal were in very good agreement with the experimental data with a deviation less than 5% for all steady states. Therefore, the ADM1 is a valuable tool for process design in the case of a two-stage anaerobic process as well.

Model selection, identification and validation in anaerobic digestion: a review

Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.

Optimizing the operation of a two-phase anaerobic digestion system digesting grass silage

This paper examines the optimization of an existing two-phase anaerobic digestion process using grass silage as a feedstock. The system comprises 6 leach beds connected to an upflow anaerobic sludge blanket (UASB). The existing system produced 305 L CH(4) kg(-1) VS added at an overall retention time of 42 days (6 leach beds emptied and fed sequentially every 7 days in series). The desired improvements were a reduction in retention time with increased methane production. It was noted in the existing system that biogas production and COD levels fell off in the last 2 days of each 7-day cycle. Thus the first change involved reduction in retention time to 30 days (6 leach beds fed sequentially every 5 days in series). This lead to a slight improvement in methane production (310 L CH(4) kg(-1) VS added). The second change was effected by separation of flows to the first stage (leach beds) and the second stage (UASB) through addition of an extra pump to optimize leaching. This led to an increase in CH(4) production (341 L CH(4) kg(-1) VS). The overall improvement from the existing system was an increase of 11.8% in methane production and a reduction in size or retention time of 40% (42 days decreased to 30 days retention time).

Modelling mono-digestion of grass silage in a 2-stage CSTR anaerobic digester using ADM1

This paper examines 174 days of experimental data and modelling of mono-digestion of grass silage in a two stage wet process with recirculation of liquor; the two vessels have an effective volume of 312 L each. The organic loading rate is initiated at 0.5 kg VS m(-3) d(-1) (first 74 days) and subsequently increased to 1 kg VS m(-3) d(-1). The experimental data was used to generate a mathematical model (ADM1) which was calibrated over the first 74 days of operation. Good accuracy with experimental data was found for the subsequent 100 days. Results of the model would suggest starting the process without recirculation and thus building up the solids content of the liquor. As the level of VFA increases, recirculation should be employed to control VFA. Recirculation also controls solids content and pH. Methane production was estimated at 88% of maximum theoretical production.

Mono fermentation of grass silage by means of loop reactors

A loop reactor was operated for mono fermentation of grass silage without manure addition under mesophilic conditions (38 degrees C). An averaged specific biogas production of 0.50 m(N)(3) per kg volatile solids (VS) with a methane concentration of 52% at an organic loading rate of up to 3.5 kg(VS)/(m(3) d) was obtained. The retention time varied from 440 days at 1.0 kg(VS)/(m(3) d) to 50 days at 3.5 kg(VS)/(m(3) d). The degradation level was more than 60% based on VS and 75% based on COD. The first-order hydrolysis rate constant of the process was estimated to be 0.6 d(-1). Despite the relative high ammonium concentration of up to 4 g/l, the system worked stable for an operation period of 310 days. In particular the TS content in the fermenter was found to be a key parameter and should not exceed 12% in order to avoid instabilities.

Analysis of methane potentials of steam-exploded wheat straw and estimation of energy yields of combined ethanol and methane production

Agrarian biomass as a renewable energy source can contribute to a considerable CO(2) reduction. The overriding goal of the European Union is to cut energy consumption related greenhouse gas emission in the EU by 20% until the year 2020. This publication aims at optimising the methane production from steam-exploded wheat straw and presents a theoretical estimation of the ethanol and methane potential of straw. For this purpose, wheat straw was pretreated by steam explosion using different time/temperature combinations. Specific methane yields were analyzed according to VDI 4630. Pretreatment of wheat straw by steam explosion significantly increased the methane yield from anaerobic digestion by up to 20% or a maximum of 331 l(N)kg(-1) VS compared to untreated wheat straw. Furthermore, the residual anaerobic digestion potential of methane after ethanol fermentation was determined by enzymatic hydrolysis of pretreated wheat straw using cellulase. Based on the resulting glucose concentration the ethanol yield and the residual sugar available for methane production were calculated. The theoretical maximum ethanol yield of wheat straw was estimated to be 0.249 kg kg(-1) dry matter. The achievable maximum ethanol yield per kg wheat straw dry matter pretreated by steam explosion and enzymatic hydrolysis was estimated to be 0.200 kg under pretreatment conditions of 200 degrees C and 10 min corresponding to 80% of the theoretical maximum. The residual methane yield from straw stillage was estimated to be 183 l(N)kg(-1) wheat straw dry matter. Based on the presented experimental data, a concept is proposed that processes wheat straw for ethanol and methane production. The concept of an energy supply system that provides more than two forms of energy is met by (1) upgrading obtained ethanol to fuel-grade quality and providing methane to CHP plants for the production of (2) electric energy and (3) utility steam that in turn can be used to operate distillation columns in the ethanol production process.