Work

Reinforcement learning-based control system for biogas plants in laboratory scale

Year: 2025

Type: conference-paper

Abstract: Reinforcement learning techniques can be used to learn effective policies for complex tasks, but they are rarely applied for control of biogas plants.While control of the anaerobic process is necessary for optimal plant operation, process complexity and instability prevent the usage of advanced control mechanisms in industrial settings.In this study, a proximal-policy optimization algorithm has been applied on the feeding schedule of a lab-scale biogas reactor for biomethane conversion to electrical energy depending on dynamic energy prices.The algorithm effectively optimizes feeding and selling strategies, outperforming traditional methods.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-187

Authors: Alberto Meola, Sören Weinrich, Oliver Kiefner, Félix Delory

Institutions: Deutsches Biomasseforschungszentrum, Leipzig University, FH Münster University of Applied Sciences

Language: English

Topic: Anaerobic Digestion and Biogas Production

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4409480029

FWCI: 10.39

Cites:

Cited by: 3

Related to: 10