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<p>This study presents a comprehensive analysis of a novel biomass-driven polygeneration system for simultaneous production of electricity, cooling, heating, hydrogen, and freshwater. The integrated system incorporates a gas turbine Brayton cycle, steam Rankine cycle, dual organic Rankine cycles (ORC1 and ORC2), polymer electrolyte membrane electrolyzer (PEMEC), liquefied natural gas turbine (LNGT), and lithium bromide-water absorption chiller. A comprehensive modeling framework was developed encompassing thermodynamic analysis, exergo-economic evaluation, and an exergo-risk assessment methodology adapted. The exergo-risk framework integrates thermodynamic irreversibilities with operational hazards through quantitative analysis of jet fire, combustion gas jet, and explosion scenarios. System optimization employed a tri-objective genetic algorithm (non-dominated sorting genetic algorithm II; NSGA-II) targeting simultaneous maximization of exergy efficiency, minimization of product cost rate, and reduction of total system exergo-risk. Model validation demonstrated excellent agreement with experimental data, achieving less than 2% deviation for PEM electrolyzer and errors below 5% for all subsystems. Exergo-risk analysis identified heat exchanger 2 (HE2) as the highest risk contributor due to handling high-temperature flue gas and liquefied methane streams. Tri-objective optimization achieved substantial improvements: 16.64% increase in total exergy efficiency (54.54% to 63.61%), 20.82% increase in product cost rate (2.22 to 2.68 $/s), and 8.78% reduction in total exergo-risk. Optimal configuration featured reduced air compressor (AC) pressure ratio (12 to 10.56) and elevated gas turbine (GT) inlet temperature (990&#xa0;°C to 1017&#xa0;°C). The study demonstrates integration of safety risk assessment within thermodynamic optimization, showing that traditional energy-centric approaches may overlook critical safety vulnerabilities. The proposed exergo-risk metric provides unified assessment for designing inherently safer polygeneration systems.</p>

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Tri-Objective Optimization of a Biomass-Driven Polygeneration System: Integrated Exergo-Risk Assessment Framework for Power, Cooling, Heating, Hydrogen, and Freshwater Production

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Mojtaba Babaelahi,
  • Mohammad Hasan Ghiasi

摘要

This study presents a comprehensive analysis of a novel biomass-driven polygeneration system for simultaneous production of electricity, cooling, heating, hydrogen, and freshwater. The integrated system incorporates a gas turbine Brayton cycle, steam Rankine cycle, dual organic Rankine cycles (ORC1 and ORC2), polymer electrolyte membrane electrolyzer (PEMEC), liquefied natural gas turbine (LNGT), and lithium bromide-water absorption chiller. A comprehensive modeling framework was developed encompassing thermodynamic analysis, exergo-economic evaluation, and an exergo-risk assessment methodology adapted. The exergo-risk framework integrates thermodynamic irreversibilities with operational hazards through quantitative analysis of jet fire, combustion gas jet, and explosion scenarios. System optimization employed a tri-objective genetic algorithm (non-dominated sorting genetic algorithm II; NSGA-II) targeting simultaneous maximization of exergy efficiency, minimization of product cost rate, and reduction of total system exergo-risk. Model validation demonstrated excellent agreement with experimental data, achieving less than 2% deviation for PEM electrolyzer and errors below 5% for all subsystems. Exergo-risk analysis identified heat exchanger 2 (HE2) as the highest risk contributor due to handling high-temperature flue gas and liquefied methane streams. Tri-objective optimization achieved substantial improvements: 16.64% increase in total exergy efficiency (54.54% to 63.61%), 20.82% increase in product cost rate (2.22 to 2.68 $/s), and 8.78% reduction in total exergo-risk. Optimal configuration featured reduced air compressor (AC) pressure ratio (12 to 10.56) and elevated gas turbine (GT) inlet temperature (990 °C to 1017 °C). The study demonstrates integration of safety risk assessment within thermodynamic optimization, showing that traditional energy-centric approaches may overlook critical safety vulnerabilities. The proposed exergo-risk metric provides unified assessment for designing inherently safer polygeneration systems.