分子-过程-过程网络的逆向设计：HEN-ORC 系统案例研究

IF 3.5 3区工程技术 Q2 ENGINEERING, CHEMICAL

AIChE Journal Pub Date : 2024-11-12 DOI:10.1002/aic.18643

Xiaodong Hong, Xuan Dong, Zuwei Liao, Jingyuan Sun, Jingdai Wang, Yongrong Yang

{"title":"分子-过程-过程网络的逆向设计：HEN-ORC 系统案例研究","authors":"Xiaodong Hong, Xuan Dong, Zuwei Liao, Jingyuan Sun, Jingdai Wang, Yongrong Yang","doi":"10.1002/aic.18643","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"43 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reverse design of molecule-process-process networks: A case study from HEN-ORC system\",\"authors\":\"Xiaodong Hong, Xuan Dong, Zuwei Liao, Jingyuan Sun, Jingdai Wang, Yongrong Yang\",\"doi\":\"10.1002/aic.18643\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":120,\"journal\":{\"name\":\"AIChE Journal\",\"volume\":\"43 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AIChE Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1002/aic.18643\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIChE Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/aic.18643","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}

引用次数: 0

摘要

热交换器网络（HEN）和采用新型工作流体的有机郎肯循环（ORC）系统的集成设计是一个复杂的优化问题。它涉及到在工作流体分子、有机郎肯循环过程和网络的巨大设计空间中进行导航。本文开发了一种新的两阶段逆向策略。在第一阶段，通过无状态方程（EOS）HEN-ORC 模型确定最佳 HEN-ORC 配置和工作条件，以及假设工作流体的热力学特性。在第二阶段，利用所开发的两个小组贡献人工神经网络热力学性质预测模型，从包含 43 万多种氢氟烯烃（HFOs）的数据库中筛选出工作流体分子。所介绍的方法在两个案例中得到了应用，在这两个案例中都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%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Reverse design of molecule-process-process networks: A case study from HEN-ORC system

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

AIChE Journal 工程技术-工程：化工

CiteScore

7.10

自引率

10.80%

发文量

411

审稿时长

3.6 months

期刊介绍： The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering. The AIChE Journal is indeed the global communications vehicle for the world-renowned researchers to exchange top-notch research findings with one another. Subscribing to the AIChE Journal is like having immediate access to nine topical journals in the field. Articles are categorized according to the following topical areas: Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food Inorganic Materials: Synthesis and Processing Particle Technology and Fluidization Process Systems Engineering Reaction Engineering, Kinetics and Catalysis Separations: Materials, Devices and Processes Soft Materials: Synthesis, Processing and Products Thermodynamics and Molecular-Scale Phenomena Transport Phenomena and Fluid Mechanics.