Optimization study of biomass and HDPE co-gasification-SOFC system based on SSA-BP neural network

Ying Zhao, Xiaohong Wang, Chen Li, Ruitong Guo and Weibin Zhang

Energy, 2025, vol. 335, issue C

Abstract: In this study, a solid oxide fuel cell (SOFC) multi-generation system driven by co-gasification of biomass and high-density polyethylene (HDPE) is proposed. The system simultaneously integrates a gas turbine, an organic Rankine cycle, multistage flash desalination, and LNG cold energy to capture CO2 technology. After modeling and validation of the system, eight biomass and HDPE co-gasification systems were evaluated from energy, exergy, economic and environmental perspectives, and the net power generation, desalination and carbon capture were calculated in detail, while the electrical efficiency, exergy efficiency and LCOE of the eight systems were analyzed. On this basis, the nonlinear mapping relationship between four-dimensional decision variables and three-dimensional key output variables is established by SSA-BP neural network model. Multi-objective optimization is carried out using NSGA-II and the optimal working condition is found from the Pareto frontier by TOPSIS entropy weighting method, at which time the system net power generation, exergy efficiency and LCOE reach 1952.6 kW, 60.15 % and 0.1032$/kWh, respectively.

Keywords: Co-gasification; Solid oxide fuel cell; Machine learning; Multi-objective optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225038307

DOI: 10.1016/j.energy.2025.138188

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