Integration of Mathematical Modeling and Artificial Intelligence to Predict Thermal Conditions and Optimize the Operation of Biogas Plants
Abstract
Modern biogas plants face a number of challenges related to precise control of thermal conditions, which significantly affect the stability of processes and the overall efficiency of energy production. The complexity lies in the nonlinear and dynamic nature of biochemical reactions occurring under anaerobic conditions, as well as in a large number of factors affecting heat transfer inside the reactor. In this regard, there is a need to develop adaptive and highly accurate models capable of predicting thermal processes and optimizing the operation of biogas systems. In this study, we propose a hybrid modeling framework that integrates physics-based mathematical models with machine learning algorithms, enabling real-time prediction and adaptive control of thermal processes. Unlike prior models focused solely on thermodynamics or empirical learning, our approach synergistically combines mechanistic equations (e.g., Navier-Stokes, reaction kinetics) with artificial intelligence (AI) techniques (e.g., neural networks, gradient boosting), yielding improved accuracy, lower energy loss, and higher biogas output. The results obtained demonstrate an increase in the plant's productivity, a decrease in energy losses and an improvement in environmental indicators, which makes this model a promising tool for managing sustainable energy systems.