The integrated design of the heat exchanger network (HEN) and organic Rankine cycle (ORC) system with new working fluids is a complex optimization problem. It involves navigating a vast design space across working fluid molecules, ORC processes, and networks. In this article, a new two‐stage reverse strategy is developed. The optimal HEN‐ORC configurations and operating conditions, and the thermodynamic properties of the hypothetical working fluid are identified by an equation of state (EOS) free HEN‐ORC model in the first stage. With two developed group contribution‐artificial neural network thermodynamic property prediction models, working fluid molecules are screened out in the second stage from a database containing more than 430,000 hydrofluoroolefins (HFOs). The presented method is employed in two cases, where new working fluids are found. The total annual cost of Case 1 is 12%–22% lower than the literature, and the power output of Case 2 is 5%–8% higher than the literature.

中文翻译：

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分子-过程-过程网络的逆向设计：HEN-ORC 系统的案例研究


换热器网络 （HEN） 和有机朗肯循环 （ORC） 系统与新工作流体的集成设计是一个复杂的优化问题。它涉及在工作流体分子、ORC 工艺和网络中导航广阔的设计空间。在本文中，开发了一种新的两阶段反向策略。最佳 HEN-ORC 配置和操作条件以及假设工作流体的热力学特性在第一阶段通过状态方程 （EOS） 自由 HEN-ORC 模型确定。利用两个开发的组贡献-人工神经网络热力学性质预测模型，在第二阶段从包含超过 430,000 个氢氟烯烃 （HFO） 的数据库中筛选出工作流体分子。所提出的方法用于两种情况，其中发现了新的工作流体。案例 1 的年总成本比文献低 12%–22%，案例 2 的功率输出比文献高 5%–8%。