4.6 Article

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

期刊

MICROORGANISMS
卷 9, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/microorganisms9020324

关键词

demand-orientated; ADM1; self-learning; regression model; forecast; prediction

资金

  1. German Federal Ministry of Food and Agricultural, through the Fachagentur Nachwachsende Rohstoffe e.V. [22404717]

向作者/读者索取更多资源

Future biogas plants need proactive feeding management to produce biogas according to demand, with simulation of biogas production depending on substrate supply. A research investigation presents a simulation model based on time series analysis principle, using historical data of biogas formation and solid substrate supply without differentiation of individual substrates. The model demonstrates self-learning capabilities and automatic adaptability to various applications, regardless of substrate composition.
Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows 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, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14-18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate's composition.

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