Xiaodong Hong, Xuan Dong, Zuwei Liao, Jingyuan Sun, Jingdai Wang, Yongrong Yang
{"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}
引用次数: 0
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.
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.