The increasing demand for sustainable energy sources has intensified the exploration of biogas power plants as a viable option. In this research, we present a novel approach that leverages machine learning techniques to optimize the performance of biogas power plants through the strategic placement and configuration of rotors within the fermentation vessel. Our study involves the simulation of a diverse range of biogas power plant scenarios, each characterized by varying rotor locations and rotating speeds, influencing the agitation levels of the biogas substrate. The simulation results, encompassing multiple performance metrics, serve as input data for an artificial neural network (ANN). This ANN is trained to learn the intricate relationships between rotor placement, rotor speed, agitation levels, and overall system efficiency. The trained model demonstrates predictive capabilities, enabling the estimation of plant efficiency based on specific rotor configurations. The proposed methodology provides a tool for both optimizing existing biogas power plants and guiding engineers in the design and setup of new facilities. Our model aims to offer valuable insights for engineers in the initial planning stages of new biogas power plants, enabling them to make informed decisions that contribute to sustainable and efficient energy generation.
Heller et al. (Thu,) studied this question.