Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

Dittmer, Celina; Krümpel, Johannes; Lemmer, Andreas

doi:10.3390/microorganisms9020324

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…Future biogas plants would need to be designed to meet demand, through proactive feeding management. 48 This forms the basis of the design on which this simulation model was developed, not minding the substrate supplied. Most biogas production simulation processes are based on the complex ADM1, covering a large number of parameters for all biochemical and physicochemical process steps.…”

Section: Methodsmentioning

confidence: 99%

System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment

Momodu

Adepoju

2021

Science Progress

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Introduction: This preliminary assessment of a grey-box model, was predicated on system dynamics principles and developed using Vensim® DSS software. The purpose is to predict biogas production under anaerobic conditions for energy utilization at the design stage. Objective: To describe the process of a developed system dynamics model to predict biogas production under anaerobic conditions. Methods: This method involves two-stage kinetics of the biogas production process in anaerobic conditions using the first-order and Gompertz functions. The model is depicted in two parts: causal loop diagram and stock–flow diagram. The causal loop diagram describes the anaerobic digestion process a substrate undergoes for the production of biogas, while stock–flow diagram depicts basic building blocks of the dynamic behavior of an anaerobic digestion process. Primary data is from a laboratory-scale experiment of biogas production using vegetal wastes, while the secondary one is from the literature on studies using similar substrates. Results: Primary and secondary data are used to validate and stimulate the developed model. The kinetic model shows the substrate being reduced exponentially with increasing time; consumption of substrate and production of methane and carbon dioxide follows exponential growth and decay pattern, with carbon dioxide production starting early compared to methane, and was produced at a rate faster due to the strong and resilient characteristics of fermentative microorganisms. Discussion: Comparing data from empirical and model simulation shows some close relationship, though not too perfectly. Both results reflect signs of inhibitions occurring within the substrates in the digester under anaerobic conditions explaining the low methane yield or instability.

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Section: Methodsmentioning

confidence: 99%

System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment

Momodu

Adepoju

2021

Science Progress

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“…Other authors [17] tested a different approach, avoiding the use of ADM1. They investigated a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply.…”

Section: Ju ¨Rgen Wiesementioning

confidence: 99%

Model‐Based Analysis to Increase the Substrate Efficiency of a Biogas Plant

2022

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A biogas plant is fed with agricultural substrates at an average organic loading rate of 6 kg VS per m 3 fermenter volume per day and substrates with relatively high nitrogen content. This situation results in a below-average methane yield, so the aim is to increase the substrate efficiency. An analysis of the actual condition of the plant was performed and compared with reference values. A dynamic simulation model of the biogas plant was created and adjusted with data over an operating year. The model adjustment provided a good approximation of the simulation results to the key operating variables and thus allows the evaluation of different operating variations. The effect of various measures was analyzed by simulation and it showed a potential increase in substrate efficiency of up to 15 %.

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“…Dittmer provided a linear regression model to predict gas production rates as a function of the amount of substrate. The influence of previous substrate additions is taken into account in the regression model, based on a cross-correlation analysis [15]. This model does not require any analytical effort.…”

Section: Introductionmentioning

confidence: 99%

Patterns in the Course of Gas Production Rates in Anaerobic Digestion—Prediction of Gas Production Rates Based on Deconvolution and Linear Regression

Hubert

Krause

Schaum

2023

Water

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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.

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Modeling and Simulation of Biogas Production in Full Scale with Time Series Analysis

Cited by 12 publications

References 22 publications

System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment

System dynamics kinetic model for predicting biogas production in anaerobic condition: Preliminary assessment

Model‐Based Analysis to Increase the Substrate Efficiency of a Biogas Plant

Patterns in the Course of Gas Production Rates in Anaerobic Digestion—Prediction of Gas Production Rates Based on Deconvolution and Linear Regression

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