A comprehensive thermodynamic, economic, and environmental analysis of a novel parabolic trough solar-driven multigeneration system for generating power, hydrogen, and freshwater

被引:13
作者
Bai, Yongfeng [1 ]
Wang, Bo [2 ]
Cheng, Liang [3 ]
机构
[1] Handan Univ, Off Acad Res, Handan 056005, Hebei, Peoples R China
[2] Handan Univ, Educ & Teaching Res Ctr, Handan 056005, Hebei, Peoples R China
[3] Handan Univ, Coll Mechatron, Handan 056005, Hebei, Peoples R China
关键词
Solar Energy; Zeotropic mixtures; Multigeneration system; Exergo-environmental analysis; Multi-objective optimization; ORGANIC RANKINE-CYCLE; INTEGRATED ENERGY SYSTEM; ZEOTROPIC MIXTURES; MULTIOBJECTIVE OPTIMIZATION; MULTICRITERIA OPTIMIZATION; PERFORMANCE ASSESSMENT; WORKING FLUIDS; COMBINED STEAM; KALINA CYCLE; DESALINATION;
D O I
10.1016/j.psep.2024.01.086
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The depletion of conventional energy sources and associated environmental challenges drive the imperative shift towards renewable alternatives. Among these, solar energy emerges as a prominent, abundant, and cost-effective resource. This study introduces an innovative solar-driven multigeneration system, meticulously evaluated through energetic, exergetic, exergoeconomic, and exergo-environmental perspectives. The proposed system aims to concurrently generate power, produce heat, and yield hydrogen and freshwater for diverse applications. In a detailed case study, the system demonstrates a total output power of 14.86 MW, a freshwater production rate of 40.22 kg/s, and an energy efficiency of 19.81%. Exergy analysis reveals a total destruction of 81.48 MW, in which the parabolic trough solar collectors contribute to a significant share of 85.8%. The exergoeconomic assessment yields a payback period of 4.01 years, while the product exergo-environmental impact stands at 185.89 Pts/h. Also, the mass fraction of the high-temperature organic Rankine cycle has the main impact on the system's performance indexes. Furthermore, a comprehensive parametric study explores the impact of key design variables on system performance, employing a multi-objective particle swarm optimization algorithm and proper decision-maker to identify optimal operating conditions.
引用
收藏
页码:227 / 247
页数:21
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