基于PC-SAFT的混合工质筛选与有机朗肯循环系统优化

梁俊伟; 罗向龙; 杨智; 梁颖宗; 陈健勇; 陈颖

doi:10.12052/gdutxb.200168

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基于PC-SAFT的混合工质筛选与有机朗肯循环系统优化

综合研究 | 更新时间：2026-05-27

- 基于PC-SAFT的混合工质筛选与有机朗肯循环系统优化
- Selection of Mixtures and Optimization of Organic Rankine Cycle System Based on PC-SAFT
- 广东工业大学学报 2022年39卷第2期页码：91-98
- 作者机构：
  
  广东工业大学材料与能源学院,广东,广州,510006
- 作者简介：
  
  梁俊伟(1996–)，男，硕士研究生，主要研究方向为工质物性及有机朗肯循环优化
  罗向龙(1978–)，男，教授，博士生导师，主要研究方向为热力系统集成与优化、换热器强化与优化，E-mail：lxl-dte@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51876043, 51736005)
- DOI：10.12052/gdutxb.200168
  中图分类号： TK212
- 网络首发：2022-03-10，
  
  纸质出版：2022
- 稿件说明：
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梁俊伟, 罗向龙, 杨智, 梁颖宗, 陈健勇, 陈颖. 基于PC-SAFT的混合工质筛选与有机朗肯循环系统优化[J]. 广东工业大学学报, 2022, 39(2): 91-98.

Liang Jun-wei, Luo Xiang-long, Yang Zhi, et al. Selection of Mixtures and Optimization of Organic Rankine Cycle System Based on PC-SAFT[J]. 2022, 39(2): 91-98.
梁俊伟, 罗向龙, 杨智, 梁颖宗, 陈健勇, 陈颖. 基于PC-SAFT的混合工质筛选与有机朗肯循环系统优化[J]. 广东工业大学学报, 2022, 39(2): 91-98. DOI： 10.12052/gdutxb.200168.

Liang Jun-wei, Luo Xiang-long, Yang Zhi, et al. Selection of Mixtures and Optimization of Organic Rankine Cycle System Based on PC-SAFT[J]. 2022, 39(2): 91-98. DOI： 10.12052/gdutxb.200168.

摘要

混合工质在相变过程存在温度滑移现象，能与冷热源更好地匹配从而提高有机朗肯循环(Organic Rankine Cycle

ORC)性能，成为ORC工质研究热点。混合工质组合和数量巨大，常规方法用于混合工质筛选及与系统参数的同步优化难度较大。建立了基于Perturbed-Chain Statistical Associating Fluid Theory(PC-SAFT)状态方程计算物性的混合工质三级筛选与ORC系统同步优化模型，基于机器学习极端梯度提升预测气液平衡初值模型，采用遗传算法实现工质筛选与系统参数的同步优化。从26种常用工质组成的纯工质库组合混合工质，根据温度滑移、压力初筛合适的混合工质，有效减少搜索范围。通过混合工质筛选与ORC系统参数同步优化，以净功和㶲效率为目标得到的工质分别为R12/perfluoro-pentane和R40/R160。分析对比了不同热源进口温度下不同目标时工质组合与ORC运行参数优化结果。

Abstract

Temperature slip exists in the phase transition process of the mixtures

which can better match the cold and heat sources

so as to improve the performance of organic Rankine cycle (ORC). Due to the large number and combination of mixtures

it is difficult to use conventional methods to select mixtures and optimize them synchronously with system parameters. A synchronous optimization model of three-stage screening and ORC system of mixtures based on the calculated physical properties of Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) was established. The initial value model of gas-liquid equilibrium was predicted based on eXreme Gradient Boosting of machine learning. Genetic algorithm was used to realize the synchronous optimization of working fluid screening and system parameters. From the pure working fluid pool composed of 26 kinds of common working fluids

the suitable mixtures were selected according to the temperature slip and pressure

which effectively reduced the search range. The mixtures R12/Perfluoro-pentane and R40/R160 were obtained for net work and exergy efficiency

respectively. The optimization results of working fluid combination and ORC operation parameters under different heat source inlet temperatures and different targets were compared.

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references

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