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首页> 外文期刊>Energy Conversion & Management >Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm
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Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm

机译：利用进化算法对低等级废热回收的有机朗肯循环（ORC）进行多目标优化

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摘要

Organic Rankine cycle (ORC) can effectively recover low grade waste heat due to its excellent thermody-namic performance. Based on the examinations of the effects of key thermodynamic parameters on the exergy efficiency and overall capital cost, multi-objective optimization of the ORC with R134a as working fluid is conducted to achieve the system optimization design from both thermodynamic and economic aspects using Non-dominated sorting genetic algorithm-II (NSGA-II). The exergy efficiency and overall capital cost are selected as two objective functions to maximize the exergy efficiency and minimize the overall capital cost under the given waste heat conditions. Turbine inlet pressure, turbine inlet temperature, pinch temperature difference, approach temperature difference and condenser temperature difference are selected as the decision variables owing to their significant effects on the exergy efficiency and overall capital cost. A Pareto frontier obtained shows that an increase in the exergy efficiency can increase the overall capital cost of the ORC system. The optimum design solution with their corresponding decision variables is selected from the Pareto frontier. The optimum exergy efficiency and overall capital cost are 13.98% and 129.28 × 10~4 USD, respectively, under the given waste heat conditions.
机译：有机朗肯循环（ORC）具有出色的热力学性能，可以有效地回收低品位废热。基于对关键热力学参数对火用效率和总投资成本的影响的检验，以R134a为工作流体对ORC进行了多目标优化，从而使用非支配性从热力学和经济方面实现了系统优化设计排序遗传算法-II（NSGA-II）。选择火用效率和总资本成本作为两个目标函数，以在给定的余热条件下最大化火用效率并最小化总资本成本。由于涡轮入口压力，涡轮入口温度，夹点温度差，进近温度差和冷凝器温度差之所以被选择为决策变量，是因为它们对火用效率和总投资成本有重大影响。获得的帕累托边界表明，（火用）效率的提高会增加ORC系统的总体资本成本。从帕累托边界中选择具有相应决策变量的最佳设计解决方案。在给定的余热条件下，最佳的火用效率和总投资成本分别为13.98％和129.28×10〜4 USD。

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  • 来源
    《Energy Conversion & Management》 |2013年第7期|146-158|共13页
  • 作者

    Jiangfeng Wang; Zhequan Yan; Man Wang; Maoqing Li; Yiping Dai;

  • 作者单位

    Institute of Turbomachinery, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Institute of Turbomachinery, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Institute of Turbomachinery, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Institute of Turbomachinery, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    Institute of Turbomachinery, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ORC; Multi-objective optimization; Low grade waste heat; Genetic algorithm;

    机译：ORC;多目标优化;低品位废热;遗传算法;

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