<![CDATA[The increasing population and changing lifestyles have led to significant solid waste accumulation, necessitating efficient waste management to prevent environmental and health issues. Supercritical water gasification (SCWG) is an effective method for converting high-moisture biomass into hydrogen-rich syngas, operating at temperatures above 374°C and pressures above 490MPa. The objective of this study was to develop and validate an integrated modeling and multi-objective optimization framework, combining Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Multi-Objective Genetic Algorithm (MOGA) to maximize hydrogen-rich syngas production from municipal solid waste through SCWG. The research models and predicts the effects of feed concentration, residence time, and reaction temperature on hydrogen yield, lower heating value (LHV), and gas yield. The integrated RSM and ANN models demonstrated high predictive accuracy with R² values exceeding 0.95. Optimization results from MOGA identified optimal parameters: a feed concentration of 2%, a reaction temperature between 490-495°C, and a residence time of 80 minutes. These conditions achieved H2 selectivity of 84.73%, an LHV of 6.95 MJ/Nm³, and a gas yield of 29.7%. The findings highlight the dominant influence of reaction temperature and residence time on hydrogen production, while feed concentration requires careful balance for optimal syngas quality. This study demonstrates that the combined use of RSM, ANN, and MOGA provides an effective framework for optimizing SCWG processes, offering practical insights for industrial-scale applications. Future research should explore additional variables such as biomass composition, pressure, and catalysts to enhance the efficiency and sustainability of hydrogen production from solid waste, supporting SCWG as a viable technology for sustainable energy production and effective waste management.]]>
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