A Next-Gen Renewable Biogas-Fueled Multi-Product System; ML (ANFIS) Decision-Making and Techno-Environmental-Economic/LCA Optimization by ARO/PSO/GWO/NSGA-II Metaheuristic Algorithms

Abstract

The growing request for multi-product and sustainable energy systems has highlighted the requirement for the effective use of renewable energy sources, such as biogas, in combined power plants (CPPs). The performance and sustainability of CPPs are limited by traditional power-generation techniques, which frequently concentrate on individual outputs or disregard the integration of thermo-enviro-economic goals. This study suggests a revolutionary biogas-fueled CPP that combines open and closed air Brayton cycles with a modified Kalina cycle, serving the purpose of waste heat recovery and thermal integration, to simultaneously provide power, cool, and heat. Having modeled and analyzed the proposed trigeneration setup, backed by life cycle assessment, an Adaptive Neuro-Fuzzy Inference System was used to support system performance prediction. A multi-objective optimization (MOO) using four metaheuristic algorithms was used to obtain the optimum operating conditions, and sensitivity analysis was used to measure the effect of the design variables. The total exergy destruction for the baseline condition was 5036 kW and the total energy and exergy efficiencies were 59.86% and 36.51%, correspondingly. Economic analysis showed a 16.67-year payback period and an energy cost of 0.1796 $/kWh. While retaining high efficiency (≈38.68%), four-objective optimization decreased economic and environmental effect (TPC ≈ 68 $/GJ, COE ≈ 0.138 $/kWh, SCE ≈ 0.206 kg/kWh). The findings show the potential of the CPP as a sustainable multi-product energy solution and provide practical tips for heat recovery, turbine optimization, and biogas management in real-world scenarios.