A waste characterisation procedure for ADM1 implementation based on degradation kinetics

A novel approach for the fractionation of organic components and microbial degraders in ADM1 and model validation based on the methanogenic potential

The anaerobic digestion model No 1 (ADM1), with fixed fractions of the substrate components, is currently used to simulate methane production during the anaerobic digestion (AD) of waste activated sludge (WAS). However, the goodness-of-fit for the simulation is not ideal due to the different characteristics of WAS from different regions. In this study, a novel methodology based on a modern instrumental analysis and 16S rRNA gene sequence analysis for the fractionation of organic components and microbial degraders in the WAS is investigated to modify the fractions of the components in the ADM1. The combination of Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR) analyses were used to achieve a rapid and accurate fractionation of the primary organic matters in the WAS that was verified using both the sequential extraction method and the excitation-emission matrix (EEM). The protein, carbohydrate, and lipid contents in the four different sludge samples measured using the above combined instrumental analyses were 25.0 - 50.0%, 2.0 - 10.0%, and 0.9 - 2.3%. The microbial diversity based on 16S rRNA gene sequence analysis was utilized to re-set the initial fractions of the microbial degraders in the ADM1. A batch experiment was utilized to further calibrate the kinetic parameters in the ADM1. Based on the above optimization of the stoichiometric and kinetic parameters, the ADM1 with full parameter modification for WAS (ADM1-FPM) simulated the methane production of the WAS very well with a Theil's inequality coefficient (TIC) of 0.049, which was increased by 89.8% than that of the default ADM1 fit. The proposed approach, with its rapid and reliable performance, demonstrated a strong application potential for the fractionation of organic solid waste and the modification of ADM1, which contributed to a better simulation of methane production during the AD of organic solid wastes.

Volatile fatty acid production from food waste: The effect of retention time and lipid content

The bioconversion of food waste to renewable products has an important role in alleviating the environmental burden of food wastage. This study evaluates the effect of solids retention time (1.5, 4, and 7 days) and lipid content (up to 30 % DS) on the solid's destruction efficiency and VFA yield from food waste fermentation. Although SRT below 4 days and lipid content beyond 20 % reduced the solids destruction efficiency (SRT -12 %, lipids -13 %), the VFA yield improved (SRT 0.36 to 0.48 g COD_VFA/TCOD_FED; lipids 0.17 to 0.39 g COD_VFA/TCOD_FED). This appeared to be a mechanism of improved acidification which doubled to 0.77 gCOD_VFA/g SCOD at 1.5-day SRT. The introduction of easily degradable organics in waste oils and methanogen inhibition by LCFAs were likely causes of process instability when lipids >20 %. Further research is needed considering the COD fractionation of the feed to maximize recoverable products on a commercial scale.

Sewage-Water Treatment and Sewage-Sludge Management with Power Production as Bioenergy with Carbon Capture System: A Review

Sewage-water treatment comprehends primary, secondary, and tertiary steps to produce reusable water after removing sewage contaminants. However, a sewage-water treatment plant is typically a power and energy consumer and produces high volumes of sewage sludge mainly generated in the primary and secondary steps. The use of more efficient anaerobic digestion of sewage water with sewage sludge can produce reasonable flowrates of biogas, which is shown to be a consolidated strategy towards the energy self-sufficiency and economic feasibility of sewage-water treatment plants. Anaerobic digestion can also reduce the carbon footprint of energy sources since the biogas produced can replace fossil fuels for electricity generation. In summary, since the socio-economic importance of sewage treatment is high, this review examined works that contemplate: (i) improvements of sewage-water treatment plant bioenergy production and economic performances; (ii) the exploitation of technology alternatives for the energy self-sufficiency of sewage-water treatment plants; (iii) the implementation of new techniques for sewage-sludge management aiming at bioenergy production; and (iv) the implementation of sewage-water treatment with bioenergy production and carbon capture and storage.

Sewage-water treatment with bio-energy production and carbon capture and storage

Typical large-scale sewage-water treatments consume energy, occupy space and are unprofitable. This work evaluates a conceivable two-staged sewage-water treatment at 40,000 m³/d of sewage-water with sewage-sludge (totaling 10kg^COD/m³) that becomes a profitable bioenergy producer exporting reusable water and electricity, while promoting carbon capture. The first stage comprises microbial anaerobic digesters reducing the chemical oxygen demand (COD) by 95% and producing 60%mol methane biogas. The effluent waters enter the subsequent aerobic stage comprising microbial air-fed digesters that extend COD reduction to 99.7%. To simulate the process, up-to-date anaerobic/aerobic digester models were implemented. A biogas-combined-cycle power plant with/without post-combustion carbon capture is designed to match the biogas production, supplying electricity to the process and to the grid. Results comprehend electricity exportation of 13.21 MW (7.92 kWh/t^{Reusable-Water}) with -9.957t^CO2/h of negative carbon emission (-0.6 kg^CO2-Emitted/kg^COD-Removed). The biogas-combined-cycle without carbon capture achieves 21.08 MW of power exportation, while a 37.3% energy penalty arises if carbon capture is implemented. Configurations with/without carbon capture reach feasibility at 125 USD/MWh of electricity price, with respective net present values of 6.86 and 85.07 MMUSD and respective payback-times of 39 and 12 years. These results demonstrate that large-scale sewage-water treatment coupled to biogas-fired combined-cycles and carbon capture can achieve economically feasible bioenergy production with negative carbon emissions.

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%.

A simple mass balance tool to predict carbon and nitrogen fluxes in anaerobic digestion systems

The increase in anaerobic digestion systems has profoundly affected the waste management of territories, particularly for agricultural systems. Changes in cultural practices and imports of organic waste modify the carbon (C) and nitrogen (N) fluxes on territories where anaerobic digestion is implemented. Successful anaerobic digestion can increase the economic and ecological efficiency of the waste management system. Conversely, poor anaerobic digestion leads to low economic and environmental efficiency due to greenhouse gas emissions and nutrient loss. Modeling the impact of anaerobic digestion on the systems integrating anaerobic digestion can improve the efficiency of these practices. The aim of this study was to develop, analyze, and evaluate a simple mass balance tool able to predict carbon and nitrogen fluxes in anaerobic digestion systems. The tool is composed of an exhaustive substrate database used by three models: (i) an anaerobic digestion model that predicts C and N contents in biogas and digestate; (ii) a phase separation model that predicts C and N content in liquid and solid phase digestates; and (iii) a storage model that predicts C and N content in raw, liquid phase, and solid phase digestates, as well as C and N emissions during storage. Sensitivity analyses were performed on the tool to determine critical inputs. Sensitivity analysis showed that outputs were highly sensitive to their respective inputs and to total inputs of solids. Performance evaluation showed that the tool can provide good quality predictions with R² correlations between observation and prediction varying from 0.72 to 0.99 with the best predictions obtained for raw digestate.

Modeling the Performance of Full-Scale Anaerobic Biochemical System Treating Deinking Pulp Wastewater Based on Modified Anaerobic Digestion Model No. 1

The deinking pulp (DIP) is a main resource for paper making, and the wastewater from DIP process needs to be treated. Anaerobic biochemical technique has been widely applied in DIP wastewater treatment, due to the remarkable capability in reducing high chemical oxygen demand (COD). In this study, a mathematical simulation model was established to investigate the performance of a full-scale anaerobic biochemical system for treating DIP wastewater. The model was based on Anaerobic Digestion Model No. 1 (ADM1), which was modified according to the specific anaerobic digestion process for DIP wastewater treatment. The hydrodynamic behavior of a full-scale anaerobic biochemical system was considered in this model. The characteristics of the influent DIP wastewater were assessed, and then, the substrate COD proportion was divided successfully for the necessity of ADM1 applying. The Monte Carlo technique was implemented to distinguish the most sensitive parameters that influenced the model output indicators comprising effluent COD and biogas production. The sensitive parameters were estimated and optimized. The optimized value of k_{_m_pro} is 12.02, K_{_S_pro} is 0.35, k_{_m_ac} is 4.26, K_{_S_ac} is 0.26, k_{_m_h2} is 16.62, and K_{_S_h2} is 3.21 × 10^–5. The model was calibrated with 150 days operation values measured in the field. The subsequent 100 days on-site values were used to validate the model, and the results obtained by the simulations were in good agreement. This study provides a meaningful and theoretical model guidance for full-scale wastewater anaerobic biochemical treatment simulation.

Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion

The objective of this study is to apply machine learning models to accurately predict daily biomethane production in an industrial-scale co-digestion facility. The methodology involved applying elasticnet, random forest, and extreme gradient boosting to input-output data from an industrial-scale anaerobic co-digestion (ACoD) facility. The models were used to predict biomethane for 1-day, 3-day, 5-day, 10-day, 20-day, 30-day, and 40-day time horizons. These models were fit on four years of operational data. The results showed that elastic net (a model with assumptions of linearity) was clearly outperformed by random forest and extreme gradient boosting (XGBoost), which had out-of-sample R²values ranging between 0.80 and 0.88, depending on the time horizon. In addition, feature importance and partial dependence analysis demonstrated the marginal and interaction effects on biomethane of selected biowaste inputs. For instance, food waste co-digested with percolate were shown to have strong positive interaction effects. One implication of this study is that XGBoost and random forest algorithms applied to industrial-scale ACoD data provide dependable prediction results and may be a useful complement for experimental and mechanistic/theoretical models of anaerobic digestion, especially where detailed substrate characterization is difficult. However, these models have limitations, and suggestions for deriving additional value from these methods are proposed.

Comprehensive determination of input state variables dataset required for anaerobic digestion modelling (ADM1) based on characterisation of organic substrates

This article contains the data of 11 organic substrates including physicochemical, biochemical and nutritional characterisations. Additionally, it includes for all substrates the data of organic matter fractionation into easily biodegradable, slowly biodegradable and inert fractions performed with anaerobic respirometry method. Finally, based on physicochemical characterisations and organic matter fractionation, a detailed methodology for the determination of input state variables required for the anaerobic digestion model N°1 (ADM1) was presented and the dataset for all substrates is provided. An example of calculation for one substrate illustrates the methodology for the determination of these variables. Data provided in this article could be useful to any person interested in modelling anaerobic digestion and particularly co-digestion. Data could be also used for implementation of a database for anaerobic digestion modelling.

A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions

Anaerobic digestion (AD) has become extremely popular in the last years to treat and valorize organic wastes both at laboratory and industrial scales, for a wide range of highly produced organic wastes: municipal wastes, wastewater sludge, manure, agrowastes, food industry residuals, etc. Although the principles of AD are well known, it is very important to highlight that knowing the biochemical composition of waste is crucial in order to know its anaerobic biodegradability, which makes an AD process economically feasible. In this paper, we review the main principles of AD, moving to the specific features of lignocellulosic wastes, especially regarding the pretreatments that can enhance the biogas production of such wastes. The main point to consider is that lignocellulosic wastes are present in any organic wastes, and sometimes are the major fraction. Therefore, improving their AD could cause a boost in the development in this technology. The conclusions are that there is no unique strategy to improve the anaerobic biodegradability of lignocellulosic wastes, but pretreatments and codigestion both have an important role on this issue.

Modified anaerobic digestion model No.1 (ADM1) for modeling anaerobic digestion process at different ammonium concentrations

Anaerobic digestion (AD) is an established method for sustainable energy production. Anaerobic digestion model No.1 (ADM1) was used to simulate methane production (MP) and volatile fatty acid (VFA) concentrations at different ammonium concentrations. In accordance with the incomplete description of several biochemical reactions and the omission of several reaction processes, ADM1 was modified with the consideration of acetic acid inhibition and valeric acid existence. ADM1_ac (ADM1 added acetic acid inhibition) could obtain better simulation accuracy of MP (goodness-of-fit value = 0.945), and VFA concentrations (goodness-of-fit values > 0.39) were all higher than ADM1_original, but cannot explain the valeric acid production. ADM1_va (ADM1 added valeric acid existence) could achieve better simulation of valeric acid (achieving a breakthrough of zero), nevertheless the accuracy of propionic and butyric acids was poorer than ADM1_ac with differences between experimental and simulation values were 5%-10% lower. With both factors coordinated, MP and VFA concentrations could be simulated accurately by ADM1_ac_va (ADM1 added acetic acid inhibition and valeric acid existence), with the highest goodness-of-fit values (>0.85). The results of a verification experiment with ADM1_ac_va simulation further indicated that acetic acid inhibition and valeric acid as new component were both important in ADM1. PRACTITIONER POINTS: ADM1_ac could simulate MP and acetate, propionate and butyrate concentrations better. ADM1_va could explain the valerate production during AD of glucose. ADM1_ac_va could simulate AD process quite accurately, with the highest goodness-of-fit values (>0.85). Acetate inhibition and valerate existence were both important and should be considered in ADM1.

Modeling the anaerobic co-digestion of solid waste: From batch to semi-continuous simulation

The main purpose of this study was to validate the use of a simple model for forecasting methane production in co-digestion reactors run semi-continuously using substrate data acquired in batch mode. Firstly, seven solid substrates were characterized individually in successive batches to assess their Biochemical Methane Potential (BMP) and kinetic parameters. Afterwards, eight mixtures of two, three or five substrates were processed in semi-continuous mode at an organic loading rate of 1 g VS L^-1 d^-1. The experimental methane production was always greater than that calculated from the BMP of each substrate. This result suggested that, endogenous activity needs to be taken into consideration in order to predict total methane production accurately. Near equivalence between experimental and modeled methane production was found after integration in the model of the endogenous activity. The results confirmed the possibility for use of substrate batch data (BMP and kinetics) to predict methane production in semi-continuous operations.

Robust assessment of both biochemical methane potential and degradation kinetics of solid residues in successive batches

The well-known batch assay test is used worldwide to determine the biochemical methane potential (BMP) of solid substrates in a single batch but its use to estimate the degradation kinetics may lead to underestimations. To overcome this problem, a different approach was carried out to characterize simultaneously both BMP of solid substrates and their degradation kinetics in successive batches, i.e. after an acclimation period. In a second step, a simple model was developed based on the methane production curve in batch mode for dividing the organic matter of the substrate into three sub-fractions according to their degradation rates (rapid, moderate and slow). The protocol developed was applied to 50 different substrates and a database was built. This database includes: the overall BMP (mL CH₄/g VS) and the degradation kinetics for each substrate, i.e. the global specific organic degradation rate (g VS/g VSS.d) along with the 3 sub-fractions and their specific degradation rates. The comparison with the BMP from the literature did not highlight significant difference with the BMP measured in this study. Furthermore, the degradation rates seem to be specific characteristics for each substrate and no clear correlation was found between the degradation kinetics and the kind of substrates. The information available in the database will be useful for the design and operation of anaerobic digesters: Optimization of the mix of co-substrates, choice of the applied OLR, simulation of methane production and of the rate of substrate degradation.

Fast ADM1 implementation for the optimization of feeding strategy using near infrared spectroscopy

Optimization of feeding strategy is an essential issue of anaerobic co-digestion that can be greatly assisted with simulation tools such as the Anaerobic Digestion Model 1. Using this model, a set of parameters, such as the biochemical composition of the waste to be digested, its methane production yield and kinetics, has to be defined for each new substrate. In the recent years, near infrared analyses have been reported as a fast and accurate solution for the estimation of methane production yield and biochemical composition. However, the estimation of methane production kinetics requires time-consuming analysis. Here, a partial least square regression model was developed for a fast and efficient estimation of methane production kinetics using near infrared spectroscopy on 275 bio-waste samples. The development of this characterization reduces the time of analysis from 30 days to a matter of minutes. Then, biochemical composition and methane production yield and kinetics predicted by near infrared spectroscopy were implemented in a modified Anaerobic Digestion Model n°1 in order to simulate the performance of anaerobic digestion processes. This approach was validated using different data sets and was demonstrated to provide a powerful predictive tool for advanced control of anaerobic digestion plants and feeding strategy optimization.

Biomethane potential of food waste: modeling the effects of mild thermal pretreatment and digestion temperature

In order to enhance anaerobic biodegradability of food waste (FW), thermal pretreatment was applied. The effectiveness in terms of biodegradability extent and process rate improvement was investigated. To this aim, Biomethane Potential tests were carried out under mesophilic and thermophilic conditions. The IWA anaerobic digestion Model 1 (ADM1), a powerful tool for modeling the anaerobic digestion (AD) of different substrates, was implemented to predict the methane production. Disintegration constant (k_dis) and maximum acetate uptake rate (km_ac) were identified as the most sensitive parameters and were calibrated over the observed methane production. Pretreatment improvement was more evident in enhancing parameters related to the process rate, such as solubilization extent (+153%) and disintegration constant (+18%), rather than increasing substrate biodegradability. Thermophilic conditions proved to be effective in speeding up the whole AD process, since all the kinetics were significantly improved (disintegration rate increased up to fivefold). Furthermore, it was demonstrated that, after k_dis and km_ac calibration, default thermophilic ADM1 parameters can be suitable to model FW digestion.

A stability assessment tool for anaerobic codigestion

Anaerobic codigestion allows for greater resource recovery from organic substrates and provides opportunities for more stable operation than mono-digestion. Despite these benefits, the adoption of codigestion is limited because it can introduce operational complexity and suffers from some of the same challenges as mono-digestion, such as ammonia inhibition and nutrient imbalances. There is a need for rapid and cost-effective assessments that can provide insight to design engineers as they explore the valorization of local organic waste streams and seek to maintain or improve digester stability. To address this need, we developed and tested a tool that can yield useful stability indicators, performance predictions, and substrate selection protocols for codigestion. This tool uses quantitative, empirical data on stability indicators within an assessment framework to evaluate a digester's process stability. The tool's accuracy was tested using real and simulated digester data, and the importance of the nitrogen and lipid composition of a substrate was identified. The resulting stability assessment tool improves our fundamental understanding of codigestion, provides a mechanism to reduce the number of experiments, and guides selection of appropriate substrate combinations that can maximize energy recovery during codigestion without compromising process stability.

Modified ADM1 for modeling free ammonia inhibition in anaerobic acidogenic fermentation with high-solid sludge

Anaerobic acidogenic fermentation with high-solid sludge is a promising method for volatile fatty acid (VFA) production to realize resource recovery. In this study, to model inhibition by free ammonia in high-solid sludge fermentation, the anaerobic digestion model No. 1 (ADM1) was modified to simulate the VFA generation in batch, semi-continuous and full scale sludge. The ADM1 was operated on the platform AQUASIM 2.0. Three kinds of inhibition forms, e.g., simple inhibition, Monod and non-inhibition forms, were integrated into the ADM1 and tested with the real experimental data for batch and semi-continuous fermentation, respectively. The improved particle swarm optimization technique was used for kinetic parameter estimation using the software MATLAB 7.0. In the modified ADM1, the K_s of acetate is 0.025, the k_m,ac is 12.51, and the K_{I_NH3} is 0.02, respectively. The results showed that the simple inhibition model could simulate the VFA generation accurately while the Monod model was the better inhibition kinetics form in semi-continuous fermentation at pH10.0. Finally, the modified ADM1 could successfully describe the VFA generation and ammonia accumulation in a 30m³ full-scale sludge fermentation reactor, indicating that the developed model can be applicable in high-solid sludge anaerobic fermentation.

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.

Anaerobic co-digestion: A critical review of mathematical modelling for performance optimization

Anaerobic co-digestion (AcoD) is a pragmatic approach to simultaneously manage organic wastes and produce renewable energy. This review demonstrates the need for improving AcoD modelling capacities to simulate the complex physicochemical and biochemical processes. Compared to mono-digestion, AcoD is more susceptible to process instability, as it operates at a higher organic loading and significant variation in substrate composition. Data corroborated here reveal that it is essential to model the transient variation in pH and inhibitory intermediates (e.g. ammonia and organic acids) for AcoD optimization. Mechanistic models (based on the ADM1 framework) have become the norm for AcoD modelling. However, key features in current AcoD models, especially relationships between system performance and co-substrates' properties, organic loading, and inhibition mechanisms, remain underdeveloped. It is also necessary to predict biogas quantity and composition as well as biosolids quality by considering the conversion and distribution of sulfur, phosphorus, and nitrogen during AcoD.

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.

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.

Biotic and abiotic roles of leachate recirculation in batch mode solid-state anaerobic digestion of cattle manure

Solid state anaerobic digestion, with leachate recirculation, is suitable for exploiting manure with a high solid content. The biotic and abiotic effects of the leachates were studied in lab-scale leach bed reactors (LBRs). LBRs were fed with cow manure and four leachates either biologically active or inert. The biotic impact of leachate was assessed by monitoring the microbial communities in the manure and in the leachates. LBRs with biologically active leachates, regardless to their origin, produced equivalent methane volumes (114.52±19.05 and 99.79±6.4NL/kgVS) while LBRs with inert leachates produced half less methane (60.22±5.71 and 58.87±13.2NL/kgVS) attesting to the biotic role of leachate. Moreover, its beneficial abiotic role is mainly due to its initial nutrient content, pH, and buffering capacity. The microbial community in the manure was strongly involved in methane production, and no transfer of microorganisms from the liquid phase was found (p<0.05).

Improved ADM1 model for anaerobic digestion process considering physico-chemical reactions

The "Anaerobic Digestion Model No. 1" (ADM1) was modified in the study by improving the bio-chemical framework and integrating a more detailed physico-chemical framework. Inorganic carbon and nitrogen balance terms were introduced to resolve the discrepancies in the original bio-chemical framework between the carbon and nitrogen contents in the degraders and substrates. More inorganic components and solids precipitation processes were included in the physico-chemical framework of ADM1. The modified ADM1 was validated with the experimental data and used to investigate the effects of calcium ions, magnesium ions, inorganic phosphorus and inorganic nitrogen on anaerobic digestion in batch reactor. It was found that the entire anaerobic digestion process might exist an optimal initial concentration of inorganic nitrogen for methane gas production in the presence of calcium ions, magnesium ions and inorganic phosphorus.

Comparative evaluation of anaerobic digestion for sewage sludge and various organic wastes with simple modeling

Anaerobic co-digestion of sewage sludge and other organic wastes, such as kitchen garbage, food waste, and agricultural waste, at a wastewater treatment plant (WWTP) is a promising method for both energy and material recovery. Substrate characteristics and the anaerobic digestion performance of sewage sludge and various organic wastes were compared using experiments and modeling. Co-digestion improved the value of digested sewage sludge as a fertilizer. The relationship between total and soluble elemental concentrations was correlated with the periodic table: most Na and K (alkali metals) were soluble, and around 20-40% of Mg and around 10-20% of Ca (alkaline earth metals) were soluble. The ratio of biodegradable chemical oxygen demand of organic wastes was 65-90%. The methane conversion ratio and methane production rate under mesophilic conditions were evaluated using a simplified mathematical model. There was reasonably close agreement between the model simulations and the experimental results in terms of methane production and nitrogen concentration. These results provide valuable information and indicate that the model can be used as a pre-evaluation tool to facilitate the introduction of co-digestion at WWTPs.

Modified ADM1 for modelling an UASB reactor laboratory plant treating starch wastewater and synthetic substrate load tests

A laboratory plant consisting of two UASB reactors was used for the treatment of industrial wastewater from the wheat starch industry. Several load tests were carried out with starch wastewater and the synthetic substrates glucose, acetate, cellulose, butyrate and propionate to observe the impact of changing loads on gas yield and effluent quality. The measurement data sets were used for calibration and validation of the Anaerobic Digestion Model No. 1 (ADM1). For a precise simulation of the detected glucose degradation during load tests with starch wastewater and glucose, it was necessary to incorporate the complete lactic acid fermentation into the ADM1, which contains the formation and degradation of lactate and a non-competitive inhibition function. The modelling results of both reactors based on the modified ADM1 confirm an accurate calculation of the produced gas and the effluent concentrations. Especially, the modelled lactate effluent concentrations for the load cases are similar to the measurements and justified by literature.

Application of Anaerobic Digestion Model No. 1 to describe the syntrophic acetate oxidation of poultry litter in thermophilic anaerobic digestion

A molecular analysis found that poultry litter anaerobic digestion was dominated by hydrogenotrophic methanogens which suggests that bacterial acetate oxidation is the primary pathway in the thermophilic digestion of poultry litter. IWA Anaerobic Digestion Model No. 1 (ADM1) was modified to include the bacterial acetate oxidation process in the thermophilic anaerobic digestion (TAD). Two methods for ADM1 parameter estimation were applied: manual calibration with non-linear least squares (MC-NLLS) and an automatic calibration using differential evolution algorithms (DEA). In terms of kinetic parameters for acetate oxidizing bacteria, estimation by MC-NLLS and DEA were, respectively, km 1.12 and 3.25 ± 0.56 kg COD kg COD(-1)d(-1), KS 0.20 and 0.29 ± 0.018 kg COD m(-3) and Yac-st 0.14 and 0.10 ± 0.016 kg COD kg COD(-1). Experimental and predicted volatile fatty acids and biogas composition were in good agreement. Values of BIAS, MSE or INDEX demonstrate that both methods (MC-NLLS and DEA) increased ADM1 accuracy.

Prediction of anaerobic biodegradability and bioaccessibility of municipal sludge by coupling sequential extractions with fluorescence spectroscopy: towards ADM1 variables characterization

Advanced dynamic anaerobic digestion models, such as ADM1, require both detailed organic matter characterisation and intimate knowledge of the involved metabolic pathways. In the current study, a methodology for municipal sludge characterization is investigated to describe two key parameters: biodegradability and bioaccessibility of organic matter. The methodology is based on coupling sequential chemical extractions with 3D fluorescence spectroscopy. The use of increasingly strong solvents reveals different levels of organic matter accessibility and the spectroscopy measurement leads to a detailed characterisation of the organic matter. The results obtained from testing 52 municipal sludge samples (primary, secondary, digested and thermally treated) showed a successful correlation with sludge biodegradability and bioaccessibility. The two parameters, traditionally obtained through the biochemical methane potential (BMP) lab tests, are now obtain in only 5 days compared to the 30-60 days usually required. Experimental data, obtained from two different laboratory scale reactors, were used to validate the ADM1 model. The proposed approach showed a strong application potential for reactor design and advanced control of anaerobic digestion processes.

Thermal pretreatment and hydraulic retention time in continuous digesters fed with sewage sludge: assessment using the ADM1

Thermal pretreatment is an interesting technique not only for increasing sludge biodegradability, leading to higher methane productivity, but also for improving degradation rates, allowing full-scale plants to reduce the size of digesters. In this study, the Anaerobic Digestion Model No. 1 (ADM1) was used as a tool to assess the effects of thermal pretreatment and hydraulic retention time (HRT) on the performance of three pilot-scale digesters fed with mixed sludge with/without pretreatment applied to the waste activated sludge fraction. Calibration procedures using batch tests showed an increase of up to five times in the model disintegration coefficient due to the pretreatment, and the validations performed presented good accuracy with the experimental data, with under/overestimation lower than 15% in both average and global accumulated CH4 productions. Therefore, the ADM1 demonstrated its feasibility and usefulness in predicting and assessing the behavior of the digesters under these conditions.

Anaerobic digestion of seven different sewage sludges: a biodegradability and modelling study

Seven mixed sewage sludges from different wastewater treatment plants, which have an anaerobic digester in operation, were evaluated in order to clarify the literature uncertainty with regard to the sewage sludge characterisation and biodegradability. Moreover, a methodology is provided to determine the Anaerobic Digestion Model No. 1 parameters, coefficients and initial state variables as well as a discussion about the accuracy of the first order solubilisation constant, which was obtained through biomethane potential test. The results of the biomethane potential tests showed ultimate methane potentials from 188 to 214 mL CH4 g(-1) CODfed, COD removals between 58 and 65% and two homogeneous groups for the first order solubilisation constant: (i) the lowest rate group from 0.23 to 0.35 day(-1) and (ii) the highest rate group from 0.27 to 0.43 day(-1). However, no statistically significant relationship between the ultimate methane potential or the disintegration constant and the sewage sludge characterisation was found. Next, a methodology based on the sludge characterisation before and after the biomethane potential test was developed to calculate the biodegradable fraction, the composite concentration and stoichiometric coefficients and the soluble COD of the sewage sludge; required parameters for the implementation of the Anaerobic Digestion Model No. 1. The comparison of the experimental and the simulation results proved the consistency of the developed methodology. Nevertheless, an underestimation of the first order solubilisation constant was detected when the experimental results were simulated with the solubilisation constant obtained from the linear regression experimental data fitting. The latter phenomenon could be related to the accumulation of intermediary compounds during the biomethane potential assay.

ADM1 calibration using BMP tests for modeling the effect of autohydrolysis pretreatment on the performance of continuous sludge digesters

Improving anaerobic digestion of sewage sludge through pretreatment techniques is a suitable solution for better sludge management. In this sense, modeling may present itself as an important tool to assess and predict process performance and pretreatment effects. In this study, the feasibility of using biochemical methane potential (BMP) tests data for calibrating the Anaerobic Digestion Model No. 1 (ADM1) was evaluated, in order to simulate the operation of continuous digesters fed, at different HRTs, with raw and autohydrolysis-pretreated waste activated sludge. This was achieved using a simplified COD fractioning methodology proposed to define ADM1 inputs. Hydrolysis constant rates were determined as the most sensitive parameters, and estimated using BMP tests. The calibrated model was then cross-validated with continuous digesters data sets. Good model performance was attained employing these techniques. The ADM1 was able to successfully represent the consumption of slowly biodegradable organic matter in BMP tests, the changes in hydrolytic limiting steps due to the autohydrolysis pretreatment and the behavior of the continuous digesters in overall. The COD fractioning methodology and the Xc variable manipulation proposed seemed to be crucial for proper model predictions. Results indicate that BMP tests are a suitable data source for ADM1 calibration, and that the model can be a powerful tool to assess the effect of the autohydrolysis pretreatment on the anaerobic digestion of sewage waste activated sludge.