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聚光太阳能驱动下非共沸CO_(2)混合工质跨临界动力循环的性能分析与工质筛选

Performance Analysis and Working Fluid Screening of Transcritical Power CycleDriven by Concentrated Solar Energy With Non-Azeotropic CO_(2)-based Mixture
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摘要 由于其潜在的热力学性能,跨临界CO_(2)混合工质动力循环成为中低温槽式聚光太阳能热发电系统中动力循环部分的理想替代方案。本文提出了一种基于多目标优化的双层决策框架,用于选择合适的CO_(2)混合工质,并分析了不同关键参数对系统的热力学性能、经济可行性和环境性的影响。研究结果显示,CO_(2)/R32混合工质使得系统的净输出功达到最大值,为253.77k W。而CO_(2)/R161混合工质则表现出较优的热效率(9.80%)、经济性(0.6444 USD/kWh)和碳减排性(0.0329 kg/kWh)。此外,TOPSIS决策结果表明在不同的热罐温度下,选择CO_(2)/R161混合工质的优先级最高。 Owing to their favorable thermodynamic performances,transcritical power cycles utilizing carbon dioxide(CO_(2))-based mixtures have emerged as an optimal choice for the power cycle part in medium to low-temperature trough-type concentrating solar power(CSP)systems.This study introduces a two-layer decision-making framework grounded in multi-objective optimization to facilitate the selection of appropriate CO_(2)-based mixtures.The analysis assesses the effects of diverse key parameters on the thermodynamic performance,economic feasibility,and environmental dimensions of the system.The research findings reveal that the CO_(2)/R32 mixed working fuid maximizes the net power output,achieving 253.77 kW.Simultaneously,the CO_(2)/R161 mixed working fuid exhibits superior thermal efficiency(9.80%),economic viability(0.6444 USD/kWh),and reduction in carbon emissions(0.0329 kg/kWh).Moreover,TOPSIS decision-making results suggest that,across various thermal storage tank temperatures,the selection of the CO_(2)/R161 mixed working fluid is of paramount importance.
作者 胥鑫 吴闯 刘朝 XU Xin;WU Chuang;LIU Chao(Key Laboratory of Low-grade Energy Utilization Technology and System,Ministry of Education,School of Energy and Power Engineering,Chongqing University,Chongqing 400044,China)
出处 《工程热物理学报》 北大核心 2025年第6期1760-1771,共12页 Journal of Engineering Thermophysics
基金 国家自然科学基金(No.52206003) 中国博士后科学基金(No.2021M693714)。
关键词 跨临界动力循环 CO_(2)混合工质 中低温槽式聚光太阳能 多目标优化 transcritical power cycle CO_(2)-based mixture medium and low temperature troughtype concentrating solar energy multi-objective optimization
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