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چکیده
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Thermodynamic efficiency enhancement of energy conversion systems is crucial to decrease fuel consumption and corresponding emissions. Multi-generation systems (MGSs) operate multiple thermodynamic cycles using a single energy source, which reduces energy waste. Biomass is a renewable energy source that reduces reliance on fossil fuels. The aim of this study is to explore a biomass-based MGS. The configuration features a Brayton cycle, a combined power and ejector cooling cycle, three ORCs (organic Rankine cycles), a supercritical Brayton cycle, and an electrolyzer. Power, hydrogen and cooling are generated simultaneously in the system. Energy, exergy and economic analyses were conducted. The results indicate an energy efficiency of 36.45% and exergy efficiency of 31.41%. The net output power was 5626 kW. The hydrogen production reached 264.7 kg/day and the cooling capacity was increased to 87.23 kW. Total exergy destruction was 10471 kW, with the gasifier accounting for 3043 kW. Investment cost rate stood at 74.57 $/h and power generation cost at 293.7 $/h. A sensitivity study examined how changing thermodynamic properties at different points affects system performance. For optimization, a Random Forest surrogate model replaced the expensive thermodynamic simulations, cutting computational time by orders of magnitude. A genetic algorithm performed tri-objective optimization: minimize power generation cost, maximize exergy efficiency, and maximize net power. Seven weighting scenarios captured different design priorities. Across the seven weighting scenarios, the algorithm converged to four distinct compromise solutions, with the efficiency-oriented and strong-efficiency scenarios sharing one optimum, and the power-oriented and strong-power scenarios sharing another. These points span a narrow design frontier, with power generation cost rate ranging from 249.06 to 250.86 $/h, exergy efficiency from 36.20% to 36.38%, and net power from 6546 to 6587 kW. The cost-oriented and strong-cost scenarios achieved the lowest power generation cost rate, while the power-oriented and strong-power scenarios attained the highest net output power. The surrogate-assisted optimization framework proved fast, accurate (R2 > 0.96 for all three objectives), and effective for suggested biomass MGS.
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