Modeling and simulation of anaerobic digestion-gasification integration using MADRL–FAHP

The growing demand for clean and efficient energy necessitates advanced solutions in waste-to-energy systems. Traditional anaerobic digestion (AD) and gasification processes suffer from this study and proposes a novel hybrid optimization framework that combines multi-agent deep reinforcement learning (MADRL) with the fuzzy analytic hierarchy process (FAHP). The proposed MADRL–FAHP framework is chosen over existing methods for its ability to combine adaptive, real-time control with expert-driven decision making. Unlike conventional models, it effectively handles dynamic feedstock conditions, process uncertainties, and multi-criteria trade-offs, ensuring greater efficiency, stability, and scalability in waste-to-energy systems. MADRL enables decentralized, real-time control of process variables by learning optimal policies through continuous interaction with the system, making it ideal for dynamic and uncertain environments. FAHP complements this by incorporating expert judgment into a structured multi-criteria decision-making model, optimizing key parameters such as feedstock quality, environmental impact, and economic feasibility. Additionally, a kinetic model is applied to simulate the biological and thermochemical reactions, while equilibrium modeling refines syngas composition. Results show a 90% improvement in energy efficiency and a 30% reduction in greenhouse gas emissions, with consistent biogas and syngas yields. This integrated AI-expert system provides a scalable and intelligent pathway for advancing next-generation sustainable bioenergy solutions.