Agricultural Waste as a Raw Material Source for Biofuel Production: Analysis of Methods, Models and Intelligent Technologies

Abstract

With growing global demand for renewable energy and the need for efficient use of organic waste, agricultural waste (including animal excrement and plant residues) is considered a key and promising feedstock for biogas production via anaerobic digestion. Despite the environmental and energy benefits of this approach, significant variability in the chemical composition and quality of the feedstock poses a serious obstacle to ensuring consistently high methane yields. This review systematizes and evaluates modern approaches aimed at overcoming this variability. Particular attention is paid to the integration of machine learning methods, particularly artificial neural networks. These methods demonstrate high accuracy in modeling and predicting methanogenesis efficiency based on analysis of input biomass characteristics. Furthermore, the review critically evaluates the role of intelligent control and automation systems using hybrid algorithms for adaptive adjustment of process parameters (carbon-to-nitrogen ratio, temperature, pH) in real time. The implementation of these intelligent technologies is key to process optimization, increased reliability, and the economic competitiveness of the biogas industry.