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Finite-Time Thermodynamic Modeling and Optimization of Short-Chain Hydrocarbon Polymerization-Catalyzed Synthetic Fuel Process.
Yu, Yajie; Xia, Shaojun; Jin, Qinglong; Rong, Lei.
Afiliación
  • Yu Y; College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
  • Xia S; College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
  • Jin Q; College of Power Engineering, Naval University of Engineering, Wuhan 430033, China.
  • Rong L; College of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China.
Entropy (Basel) ; 24(11)2022 Nov 15.
Article en En | MEDLINE | ID: mdl-36421513
ABSTRACT
The short-chain hydrocarbon polymerization-catalyzed synthetic fuel technology has great development potential in the fields of energy storage and renewable energy. Modeling and optimization of a short-chain hydrocarbon polymerization-catalyzed synthetic fuel process involving mixers, compressors, heat exchangers, reactors, and separators are performed through finite-time thermodynamics. Under the given conditions of the heat source temperature of the heat exchanger and the reactor, the optimal performance of the process is solved by taking the mole fraction of components, pressure, and molar flow as the optimization variables, and taking the minimum entropy generation rate (MEGR) of the process as the optimization objective. The results show that the entropy generation rate of the optimized reaction process is reduced by 48.81% compared to the reference process; among them, the component mole fraction is the most obvious optimization variable. The research results have certain theoretical guiding significance for the selection of the operation parameters of the short-chain hydrocarbon polymerization-catalyzed synthetic fuel process.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Entropy (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Entropy (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China