Novel decision-making strategy for working fluid selection in Organic Rankine Cycle: A case study for waste heat recovery of a marine diesel engine

被引:16
|
作者
Gurgen, Samet [1 ]
Altin, Ismail [2 ]
机构
[1] Iskenderun Tech Univ, Dept Naval Architecture & Marine Engn, TR-31200 Antakya, Turkey
[2] Karadeniz Tech Univ, Dept Naval Architecture & Marine Engn, TR-61530 Trabzon, Turkey
关键词
Organic Rankine Cycle; Working fluid selection; Decision-making; Gray wolf algorithm; THERMOECONOMIC MULTIOBJECTIVE OPTIMIZATION; THERMODYNAMIC ANALYSIS; EXHAUST-GAS; SYSTEM; ORC; DESIGN; TECHNOLOGIES; SIMULATION; WATER; CO2;
D O I
10.1016/j.energy.2022.124023
中图分类号
O414.1 [热力学];
学科分类号
摘要
The Organic Rankine Cycle (ORC) is receiving increasing attention as a waste heat recovery system. However, it is still a major challenge to quickly and practically choose the appropriate one among dozens of working fluids, considering many criteria. In this study, the comprehensive decision-making strategy for working fluid selection in ORC applications was proposed. The selection of working fluid was eval-uated not only in terms of thermodynamics and economics but also considering safety and environ-mental concerns. A container ship was chosen for the case study. Optimization was carried out for the design condition using the Multi-Objective Gray Wolf Algorithm (MOGWA). The results obtained for 10 different working fluids were evaluated with a comprehensive decision-making strategy and R245fa was determined as the final working fluid. Moreover, off-design analyses and operational profile-based simulation were performed. The net work was calculated as 439.5772 kW and 420.1741 kW for the design condition and the overall operating condition, respectively. The electricity generation cost for the design operating condition was 0.0570 $/kWh, and this value for all conditions was calculated as 0.0596 $/kWh. Lastly, MOGWA and NSGA-II were compared to each other, and their performance was analyzed. The results showed that both algorithms had similar performance.(c) 2022 Elsevier Ltd. All rights reserved.
引用
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页数:16
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