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Comparison of best model outputs (X, XS, XX, XXS) with experimental methane flow from GW digestion for the pulse loading occurring at 65 days for GW (a) and 58 days for FW (b).
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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 f...
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... tion R 2 increased from 74.0% for model X to 90.0% for model XXS. The parameter uncertainty remained low in all cases indicating that the dataset was sufficiently rich to allow independent estima- tion of each parameter. The exception to this was in the application of the XXS model to the GW data which resulted in a maximum error of 9.7%. Fig. 3 shows an example of a response of the systems to a pulse load of GW and FW, demonstrating the comparative ability of the four fractionation models to describe the methane production kinetics. This figure is indicative of the fit over the whole experi- mental period except the early stages and final stages during which less good fit was ...
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... models to describe the methane production kinetics. This figure is indicative of the fit over the whole experi- mental period except the early stages and final stages during which less good fit was observed (Fig. 4), probably due to the effects of microbial acclimatisation and inhibition respectively, which will be discussed separately. Fig. 3a allows the following observations with regard to the GW fractionation; The X model tends to underestimate both high flow and low flow data points. The introduction of a further particulate fraction in XX model improves noticeably the fitting. The introduc- tion of the soluble fraction in XS model improves the fitting of the high flow ...
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... the FW fractionation models, the results of which are shown in Fig. 3b, the following observations can be made; The X model, similarly to GW, tends to underestimate both high flow and low flow data points. The introduction of a soluble fraction (XS) allows better reproduction of the peak biogas production directly after the feedings, although the fitting in the remainder of the profile is less accurate. ...
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BACKGROUND
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Citations
... By using experimental measurements, the model, and data on the composition of wastewater, they were able to expand knowledge of the characteristics of the influent and describe the reactor's behavior. D. Poggio et al. [34] introduced and evaluated a detailed methodology for substrate characterization to be used with ADM1. This methodology combines biochemical and kinetic fractionation approaches. ...
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... Horschig T et al. 2019 [48] Bhatt A H Tao L 2020 [49] Agbejule et al. 2021 [50] Bartoli A et al. 2020 [51] Gaida D et al. 2012 [52] Meng L et al. 2020 [53] Smith J U et al. 2015 [54] Sadhukhan J 2014 [45] Ddiba A K et al. 2022 [55] Segundo-Aguilar et al. 2021 [56] Poggio D et al. 2016 [57] Bar R Ehrensperger A 2018 [58] In this sense, multicriteria decision making allows decision makers to simultaneously consider the environmental, social, environmental, and technical aspects of biogas projects. According to the studies presented in Table 1, biogas projects impact not only the energy efficiency and economic forecasts but also play an important role in sustainable waste management, mitigating environmental impacts, and promoting social development. ...
While biogas production offers promising solutions for waste management, energy diversification, and sustainable development, effective project implementation requires comprehensive evaluation criteria that encompass diverse aspects, such as the problem to be addressed, biodigester technology selection, business model development, investment considerations, and final product utilization. A preliminary study involving an integrative review of 58 articles yielded 499 unique criteria. These criteria were categorized into four groups: economic, environmental, social, and technical, encompassing a total of 39 subcriteria. Six stages of the biogas production cycle were considered in the analysis: project, initiation, biodigester type selection, location determination, operational cycle definition, and final product utilization. The analysis revealed that existing decision-making models often prioritize technical and economic considerations while neglecting broader social and environmental perspectives. This paper addresses this gap by proposing, for the first time, stage-specific, multicriteria decision-making (MDCA) models tailored to each phase of a biogas production cycle. These models empower project managers and policymakers to optimize resource allocation, minimize the environmental impact, maximize social benefits, and ensure project viability and profitability. The models’ adaptability allows for tailored prioritization based on specific project requirements and contexts. This groundbreaking research fills a critical void in biogas decision making by bridging the gap between existing technical and economic model limitations and the growing need for truly sustainable project development.
... The parameters were determined through the weighted least-squares method to identify optimal values that minimize the sum of squared residuals, S, as in Poggio et al. (2016): ...
The Anaerobic Digestion Model No. 1 (ADM1) was employed to simulate methane (CH 4) production in an anaerobic reactor (AR), and the associated bench-scale biochemical methane potential (BMP) assay, having sewage sludge (SWS) from a municipal wastewater treatment plant (WWTP) as feedstock. The SWS presented the following physical-chemical characteristics: pH (7.4-7.6), alkalinity (2,382 ± 100 mg CaCO3 L −1), tCOD (21,903 ± 1,000 mg L −1), TOC (895 ± 100 mg L −1), TS, TVS, and VSS (2.0%, 1.1%, and 0.8%, respectively). The BMP assay was conducted in six replicates under anaerobic mesophilic conditions (37 ± 0.1 • C) for 11 days with a CH 4 yield registered of 137.6 ± 6.39 NmL CH 4 or 124 ± 6.72 CH 4 g −1 VS −1. When the results obtained with the BMP bench-scale reactors were compared to the output generated with computational data by the ADM1 model having as input data the same initial sewage tCOD, similar cumulative CH 4 production curves were obtained, indicating the accuracy of the ADM1 model. This approach allowed the characterization of the sludge and estimation of its biogas production potential. The combination of BMP assays, experimental data, and ADM1 model simulations provided a framework for studying anaerobic digestion (AD) processes.
... Another significant drawback from a practical standpoint is the necessity of measuring several states and characteristics, mostly with laboratory equipment. This issue has been addressed in a variety of methods, including by using Biochemical Methane Potential (BMP) test data for calibrating the model [17] and using a biochemical or kinetic parameter estimation approach in batch experiments [18][19][20][21]. ...
Using renewable energy is increasingly prevalent as part of a global effort to safeguard the environment, with a reduction in $${\mathrm{CO}}_{2}$$ CO 2 being one of the primary objectives. A biogas plant provides an opportunity to produce green energy, but its profitability prevents it from being utilized more frequently. A suitable response to this economic issue would be flexible biogas production to exploit fluctuating energy prices. Nevertheless, the complex nature of the anaerobic digestion process that proceeds within the biogas plant and the wide range of substrates that may be utilized as the plant’s feeds make it challenging to achieve flexible biogas production truly. Most plant operators will rely on their experience and intuition to run the plant without knowing exactly how much biogas they will produce with the feed substrate. This work combines a system model estimation and feedback controller to provide an intuitive yet precise feedback control system. The system model estimation represents the biogas plant mathematically, and a total of six distinct approaches have been compared and evaluated. A PT1 model most accurately approximated the step-down and the step-up by the time percentage method, with the Akaike Information Criterion as the primary evaluation criterion for selecting the best model. The downward model was controlled by a discrete PI controller modified with the Root Locus Method and an Anti-Windup scheme, and the upward model was controlled by a state space controller. The quality of the controller was evaluated in both simulation and at the actual biogas plant in Grub, and the controller was able to reduce the biogas production rate approaching the setpoint in the expected period. Furthermore, the developed feedback control system is effortless enough to be installed in many biogas plants.
... The mathematical description of the synergistic effects of different substrate mixing ratios can be a useful tool for selecting the appropriate substrate streams taking into account the treatment plant capacity and its total operational cost [12]. Many different mathematical models have been used to describe such processes, including Anaerobic Digestion Model 1 (ADM1) and sigmoid algebraic equations. ...
... Finally, the total productivity of the continuous reactor was estimated from the difference between inlet and outlet organic matter according to Equation (12). ...
... The coefficient of determination was calculated to be 82.9% on average over 6 iterations. This value is in a similarly good range as the results of the model presented by Lafratta [3], which were in the range of 72 and 87% within 24 h or the results by Poggio [34] using the ADM1 simulating gas production from food and green wastes (74.0-93.3%). The deep learning models introduced by Jeong et al. [11] achieved a coefficient of determination of 38 to 76%. ...
Anaerobic digestion, including the gas storage and the CHP unit plays a key role in energy management of water ressource recovery facilities (WRRF). By demand-driven feeding management, a higher degree of utilization of the gas storage tanks can be achieved, which means that a greater decoupling of energy demand and energy generation is taking place. In this context, the predictability of the produced gas volume plays an important role. In this study, a concept was suggested to forecast gas production rates without the need for any analytical substrate characterization. Substrates lead to specific patterns in the course of the gas production rates, which can be fitted using Gaussian functions. As the parameters describing the Gaussian functions have a good correlation to the amount of substrate fed to the digester a prediction of the rates is possible. Within this study a coefficient of determination 82.9% over a period of 5 days was achieved.
... In this study, the density, the elementary fractions (CHNSO), the ash, phosphor, potassium and VS contents were fed into an ADM1-type model allowing calculating the final biogas yield through mass and charge balance (Figure 3). [57] combined ADM1 with a biochemical fractionation. The carbohydrates, proteins and lipids fractions were obtained by realizing nitrogen, COD and mass balances. ...
... The batch test data of green waste and food waste were used for the model calibration. The model was then validated using a semi-continuous dataset (cf. Figure 8 of [57]). A good prediction of various variables like biogas production, VFA, total nitrogen and alkalinity were achieved. ...
Anaerobic digestion (AD) is a promising way to produce renewable energy. The solid-state anaerobic digestion (SSAD) with a dry matter content more than 15% in the reactors is seeing its increasing potential in biogas plant deployment. The relevant processes involve multiple of evolving chemical and physical phenomena that are not crucial to conventional liquid-state anaerobic digestion processes (LSAD). A good simulation of SSAD is of great importance to better control and operate the reactors. The modeling of SSAD reactors could be realized either by theoretical or statistical approaches. Both have been studied to a certain extent but are still not sound. This paper introduces the existing mathematical tools for SSAD simulation using theoretical, empirical and advanced statistical approaches and gives a critical review on each type of model. The issues of parameter identifiability, preference of modeling approaches, multiscale simulations, sensibility analysis, particularity of SSAD operations and global lack of knowledge in SSAD media evolution were discussed. The authors call for a stronger collaboration of multidisciplinary research in order to further developing the numeric simulation tools for SSAD.
... Bien évidemment, la qualité du modèle ainsi identifié sera réduite, mais comme indiqué précédemment, l'objectif principal est de disposer d'un outil de simulation pour les besoins de l'étude théorique d'automatisation du procédé. Différentes études dans la littérature ont considéré le calibrage d'un modèle ADM1 pour un procédé de digestion anaérobie [48,99,14,22,47]. Dans ce chapitre, un modèle ADM1 pour le procédé étudié sera mis au point, à partir des données expérimentales présentées dans le chapitre 2 (voir B qui décrit les équations de l'ADM1 ...
Dans le contexte actuel de lutte contre le réchauffement climatique, le développement des énergies renouvelables représente un enjeu majeur. La digestion anaérobie apparaît comme une solution très prometteuse pour atteindre l'objectif de favoriser la production d'une énergie propre (le biogaz), tout en générant un engrais naturel et propre pour des débouchés en agriculture (le digestat). L'objectif finale est d'optimiser ce procédé via une loi de pilotage via un système automatisé.Cependant, le procédé est complexe, incertain, faisant intervenir un grand nombre de bactéries et de composition chimique du substrat.Ainsi, les enjeux sont multiples pour atteindre les objectifs visés. Premièrement, il s'agit de mettre au point et de valider des modèles simples pour des phénomènes complexes ayant lieu au sein du procédé. Deuxièmement, il faut pallier au manque de capteurs physiques intrinsèque à la digestion anaérobie.Cet aspect est d'autant plus important qu'il représente un des verrous de l'exploitation industrielle de la digestion anaérobie dans le traitement de déchets. Enfin, il s'agit de mettre en place des lois de commande performantes et robustes pour optimiser le fonctionnement de ce procédé.Dans ces travaux de thèse, le cas d'un digesteur pilote à l'IST de Mamou (Guinée) a été étudié. Une première étape a consisté à caractériser la composition physico-chimique de différents déchets (animaux et déchets municipaux et industriels). Ensuite, un modèle mathématique a été déterminé à partir des données expérimentales obtenues sur le procédé pilote (modèle dit ADM1 et modèles réduits). Enfin, la conception d'un estimateur du taux de croissance des bactéries dans le digesteur a été proposée à partir des mesures du débit de biogaz produit.
... Despite the complexity of the ADM1 model, it focuses on the optimization of the reaction conditions and kinetics but neglects the impact of the biodegradation of different organic forms of waste. Poggio et al. [20] proposed a simplified model derived from standard ADM no.1 to predict the methane production from organic waste with organic matter and volatile fatty acid as the predictors and the measured and model methane yield gave a determination coefficient of 0.91. ...
Biogas technology is rapidly gaining market penetration, and the type of digesters employed in the harnessing of the biogas from biodegradable waste is crucial in enhancing the total viable bacteria counts. This study focused on the exploration of input parameter (number of days, daily slurry temperature, and pH) and target (total viable bacteria counts) datasets from anaerobic balloon digester charged with cow manure using data acquisition system and standard methods. The predictors were ranked according to their weights of importance to the desired targets using the reliefF test. The complete dataset was randomly partitioned into testing and validated samples at a ratio of 60% and 40%, respectively. The developed non-linear regression model applied on the testing samples was capable of predicting the yield of the total viable bacteria counts with better accuracy as the determination coefficient, mean absolute error, and p-value were 0.959, 0.180, and 0.602, respectively, as opposed to the prediction with the multiple linear regression model that yielded 0.920, 0.206, and 0.514, respectively. The 2D multi-contour surface plots derived from the developed models were used to simulate the variation in the desired targets to each predictor while the others were held constant.
... Other researchers modelled a start-up phase where the initial coefficient values for ADM1 were taken from a digester at steady state, with the same inoculum and substrate additions, as the digester in their system (Fatolahi et al., 2020). Poggio et al. (2016) set up an experimental 2.4 L semi-continuous digester where they gradually increased the organic loading rate (OLR), for the first 80 days to allow the digester to settle, before starting to record biogas yields. The experiment was ran for 142 days in total. ...
... When the experiment was modelled in ADM1, a similar approach was followed where the OLR was gradually increased, to calibrate ADM1 for 80 days, before comparing the biogas yields with the experimental values. A similar approach to Poggio et al. (2016) was used in this study where the substrate feeding rate in the ADM1 model was gradually increased for 30 days, for both March and July, until the required substrate feeding rates on the first days of those months was reached. After these initial 30 days of adjusting the model to the flowrates used in the case study plant, the model results were compared with the measured data on the first days of both March and July. ...
This paper presents a method to model and optimise the substrate feeding rate of an anaerobic digestion (AD) system. The method is demonstrated for a case study plant in Bangalore, India, using onsite kitchen waste to provide biogas for cooking. The AD system is modelled using Anaerobic Digestion Model No. 1 (ADM1) and a genetic algorithm (GA) is applied to control the substrate feeding rate in order to simultaneously minimise the volume of flared biogas, unmet gas demand and energy cost. Our results show that ADM1 can predict biogas yield from a continuously operated digester well with mean percentage error between daily predicted and measured data values of only 5.7% for March 2017 and 17.8% for July 2017. When biogas flaring and unmet gas demand were minimised, the amount of biogas flared reduced from 886.62 m³ to 88.87 m³ in March and from 73.79 m³ to 68.49 m³ in July. When the energy cost was also considered within an objective function, the biogas flared reduced from 886.62 m³ to 281.27 m³ for March, but increased from 73.79 m³ to 180.11 m³ for July. The amount of flaring increased in July as the energy cost function increased biogas yield without considering surplus gas production beyond demand and storage capacity. As AD systems are often operated to maximise biogas production, these results highlight the need for multi-objective optimisation, particularly for off-grid AD systems.
