Design, performance assessment, and machine learning-driven optimization of a novel low-carbon urban waste-to-x polygeneration system: multi-scenario analysis of hydrogen and methane production

Growing energy demand, waste accumulation, and greenhouse gas emissions necessitate integrated, low-carbon energy options. This study proposes a novel waste-to-x polygeneration system uniquely integrating biomass gasification with gas turbine, supercritical CO 2 , Kalina, organic Rankine, and steam Rankine cycles, coupled with advanced wastewater treatment, carbon capture, a proton exchange membrane (PEM) electrolysis, and methanation. The system simultaneously produces electricity, district heat, oxygen, hydrogen, and methane, advancing beyond typical waste-to-energy approaches by combining multi-vector fuel production with near-zero emissions. Under baseline operation, the system attains overall energy and exergy efficiencies of 35.0% and 39.9%, delivering 3510 kW net power and 1310 kW heating, and daily outputs of 131.6 kg hydrogen, 2106 kg oxygen, and 296.3 kg methane, while capturing 87% of CO 2 emissions (177.7 t/day) and treating 116.6 t/day wastewater. Exergy analysis identifies the biomass gasifier as the primary exergy destruction source (8014 kW), whereas mixers and splitters achieve the highest exergy efficiencies (>99.0%). Employing a machine-learning-assisted multi-objective grey wolf optimizer (MOGWO), for dual fuel production scenario, enhances energy and exergy efficiencies to 49.5% and 53.6%, respectively; boosts hydrogen, oxygen, and methane production by 23.0%; reduces net power by 6.9%; and increases heating output by up to 29.1%. Among fuel-production modes at the optimum, the hydrogen-only case achieves the highest efficiencies (49.7% energy, 53.6% exergy). This integrated approach offers a comprehensive and flexible option for sustainable urban resource management. • Novel urban waste-to-x system achieves near-zero emissions • Optimization raises energy efficiency to 49.5% and exergy to 53.6% • Optimal design produces 296.3 kg/day CH 4 and 161.6 kg/day H 2 • System captures 87% CO 2 (177.7 t/day) and treats 116.6 t/day wastewater • Optimization uses ML-based multi-objective grey wolf optimization