Effective agitation is critical for the performance of agricultural biogas power plants, while quantifying mixing efficiency for a given system remains challenging. Direct measurement of substrate flow fields in full-scale digesters is costly and intrusive, and relying on computational fluid dynamics simulations (CFD) alone can be computationally expensive for routine design exploration. This work addresses this gap by integrating computational fluid dynamics with supervised machine learning to optimize mixing efficiency through strategic rotor placement and configuration. We simulate a diverse range of biogas digester scenarios, with varying rotational speeds and agitator locations, capturing how these factors influence the mixing/agitation of the substrate. We then train an artificial neural network (ANN) to predict the agitation efficiency, for previously unseen configurations. During validation, the proposed model indicates a mixing energy level (MEL) below the EPA guideline range (5 W m−3 to 8 W m−3). Overall, the hybrid CFD-ML methodology demonstrated here provides a scalable pathway for data-informed optimization of mixing in biogas plants, with potential to improve energy efficiency and support design and retrofit decisions in renewable energy infrastructure.

Biogas is an important driver in carbon-neutral energy sources. Many biogas digester setups, however, are not well optimized and waste energy or fail to maximize their gas output potential. To optimize these systems, a framework was developed to measure and predict digester systems’ efficiencies by closely monitoring fluid movements. This framework includes a numerical calculation of fluid behavior (Computational Fluid Dynamics (CFD)), and Deep Learning to estimate the fluid shear-rates introduced by the agitator’s action. Additionally, a novel measurement system is presented that can measure the same metrics, as simulated, in real-world environments. Lastly, an outlook is given that presents the options and extensions of the presented setup to reduce prediction error, minimize measuring efforts further, and recommend optimization approaches to the operator.

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.