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Machine learning algorithms provide detailed description of the anaerobic digestion process, but the impact of data preparation procedures and hyperparameter optimization has rarely been investigated. A genetic algorithm was developed for optimizing data preparation and model hyperparameters to simulate dynamic methane production from steady-state anaerobic digestion of agricultural residues at full-scale. A long short-term memory neural network was used as prediction model. Results indicate that batch size, learning rate and number of neurons are the most important model parameters for accurate description of methane production rates, whereas combination of hyperparameter and data preparation optimization shows best model efficiencies, with a root mean square scaled error of 76.5 %. Mass of solid feed, time and mass of volatile solids are the most relevant input features. This study provides fundamental steps for optimal prediction of dynamic biomethane production, as a reliable basis for improving bioconversion efficiency during anaerobic digestion of agricultural residues., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Flexible biogas production can enable demand-oriented energy supply without the need for expensive gas storage expansions, but poses challenges to the stability of the anaerobic digestion (AD) process. In this work, biogas production of laboratory-scale AD of maize silage and sugar beets was optimized to cover the residual load of an electricity self-sufficient community using a simple process model based on first-order kinetics. Experiments show a good agreement between biogas demand, predicted, and measured biogas production. By optimizing biogas conversion schedules based on the measured gas production, a gas storage capacity of 7-8 h was identified for maximum flexibility, which corresponds to typical gas storage sizes at industrial biogas plants in Germany. Various stability indicators were continuously monitored and proved resilient process conditions. These results demonstrate that demand-oriented biogas production using model predictive control is a promising approach to enable existing biogas plants to provide balancing energy., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Ensiling of sugarcane trash (SCT) and sugarcane stalks (SCS) was studied to assess the effects of molasses (MOL) and lactic acid bacteria (LAB) inoculant on methane potential. The experiment was run for 70 days and monitoring parameters were analyzed at days 0, 5, 15 and 70. Biochemical methane potential (BMP) tests performed with fresh and ensiled material at day 70 showed an increase in methane potential by 24.0%, 23.4%, 1.7% and 71.1% for SCS ctr , SCT ctr , SCT mol and SCT mol + lab , respectively. Such improved performance is explained by the formation of organic acids (mostly acetate and lactate) which were able to decrease the pH of the silages from 5.7-5.9 to 3.8-4.2 for all SCT treatments and from 5.9 up to 3.4 for SCS treatment. Thus, the ensiling process provided similar effects to a pre-treatment at low acid concentrations, which in turn improved the digestibility of the cellulosic biomass for methane production., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Anaerobic digestion of sugarcane straw co-digested with sugarcane filter cake was investigated with a special focus on macronutrients supplementation for an optimized conversion process. Experimental data from batch tests and a semi-continuous experiment operated in different supplementation phases were used for modeling the conversion kinetics based on continuous stirred-tank reactors. The semi-continuous experiment showed an overall decrease in the performance along the inoculum washout from the reactors. By supplementing nitrogen alone or in combination to phosphorus and sulfur the specific methane production significantly increased (P<0.05) by 17% and 44%, respectively. Although the two-pool one-step model has fitted well to the batch experimental data (R 2 >0.99), the use of the depicted kinetics did not provide a good estimation for process simulation of the semi-continuous process (in any supplementation phase), possibly due to the different feeding modes and inoculum source, activity and adaptation., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Different methods for optimization the anaerobic digestion (AD) of sugarcane filter cake (FC) with a special focus on volatile fatty acids (VFA) production were studied. Sodium hydroxide (NaOH) pretreatment at different concentrations was investigated in batch experiments and the cumulative methane yields fitted to a dual-pool two-step model to provide an initial assessment on AD. The effects of nitrogen supplementation in form of urea and NaOH pretreatment for improved VFA production were evaluated in a semi-continuously operated reactor as well. The results indicated that higher NaOH concentrations during pretreatment accelerated the AD process and increased methane production in batch experiments. Nitrogen supplementation resulted in a VFA loss due to methane formation by buffering the pH value at nearly neutral conditions (∼ 6.7). However, the alkaline pretreatment with 6g NaOH/100g FCFM improved both the COD solubilization and the VFA yield by 37%, mainly consisted by n-butyric and acetic acids., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Different model structures were compared to simulate the characteristic process variables of the anaerobic digestion of maize, sugar beet and grain silage. Depending on the type and number of the required components, it can be shown that in comparison to the complex Anaerobic Digestion Model No. 1 (ADM1) different simplified model structures can describe the gas production rate, ammonia nitrogen and acetate concentration or pH value equally well. Since the reduction of the predominantly fast kinetics of the methanogenesis, acetogenesis or acidogenesis will only have little effect on the simulation of the specific gas production, it can be proven that the hydrolysis is the rate-limiting step during the uninhibited anaerobic digestion of complex particulate substrates. However, the stoichiometric comparison reveals that the model protein gelatine is not suitable for a representative characterization of agricultural energy crops., (Copyright © 2014 Elsevier Ltd. All rights reserved.)