Waste-to-energy conversion through P-Fuzzy simulation of methane production

Landfills play a crucial role in urban solid waste management; however, challenges related to bioenergy recovery and environmental impact mitigation remain. This study presents a novel P-Fuzzy modeling approach explicitly grounded in the Tabasaran framework, representing an original integration of hierarchical fuzzy logic with environmental and biogenic modeling. The model is designed to optimize anaerobic decomposition through the integration of mathematical, chemical, and kinetic modeling. The proposed approach incorporates environmental, biogenic, and process-related dynamics, effectively capturing operational uncertainties and variability to enhance energy efficiency and reduce ecological impact. The findings indicate that small-scale landfills offer greater operational stability, whereas medium-scale facilities achieve peak biogas production efficiency during the acidogenic-methanogenic transition phase. In contrast, large-scale landfills face issues such as process instability and leachate accumulation, necessitating the adoption of advanced automation and monitoring strategies. Biogenic conversion was modeled using the Monod equation, while degradation in process efficiency was represented by a modified Michaelis-Menten kinetic model, allowing for improved predictive accuracy. Simulations demonstrated that targeted adjustments in temperature, humidity, and waste composition can enhance biogas output by up to 35 %, underscoring the importance of environmental sensors and artificial intelligence in continuous process monitoring. The core contribution of this research lies in the unprecedented hybridization of the Tabasaran model with fuzzy logic and kinetic formulations, resulting in a scalable and adaptive tool for solid waste bioenergy systems. The study demonstrates that the proposed model significantly contributes to landfill sustainability by enabling more efficient and environmentally responsible waste management practices.