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Economic/sustainability optimization/analysis of an environmentally friendly trigeneration biomass gasification system using advanced machine learning

Author

Listed:
  • Zhang, Luyao
  • Wang, Xueke
  • Abed, Azher M.
  • Yin, Hengbin
  • Abdullaev, Sherzod
  • Fouad, Yasser
  • Dahari, Mahidzal
  • Mahariq, Ibrahim

Abstract

The present research presents an innovative thermal integration model for a biomass-based power plant to generate power, coolant, and liquefied hydrogen. This integrated arrangement comprises biomass gasification, gas turbine cycle, organic flash-bi-evaporator refrigeration cycle, multi-effect desalination, solid oxide electrolyzer, and Claude cycle. The selection of an eco-friendly fluid for the electricity-cooling production cycle is conduced relying on a comparative analysis. The subsequent examination evaluates the economic-sustainability performance criteria through sensitivity and contour analyses. The comparative assessment identifies R1234ze(Z) as the most suitable working fluid. Four objective functions are scrutinized, and two optimization scenarios are devised based on a machine learning approach using artificial neural networks and a multi-objective gray wolf optimization technique. In Scenario A, the focused objectives are the total destructed exergy and the cost of producing liquefied hydrogen. Meanwhile, Scenario B integrates the sustainability index and the overall system cost rate as primary criteria. Scenario A exhibits more favorable operational conditions and outcomes than scenario B, showing the objectives mentioned as 9955 kW and 3.25 $/kg, respectively. In this scenario, the sustainability index, the overall investment cost rate, and the net present value are obtained to be 1.74, 236 $/h, and 205 M$, correspondingly.

Suggested Citation

  • Zhang, Luyao & Wang, Xueke & Abed, Azher M. & Yin, Hengbin & Abdullaev, Sherzod & Fouad, Yasser & Dahari, Mahidzal & Mahariq, Ibrahim, 2024. "Economic/sustainability optimization/analysis of an environmentally friendly trigeneration biomass gasification system using advanced machine learning," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224024770
    DOI: 10.1016/j.energy.2024.132703
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