Authors:
A. Moghadassi Arak University Department of Chemical Engineering, Faculty of Engineering Arak Iran

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F. Parvizian Arak University Department of Chemical Engineering, Faculty of Engineering Arak Iran

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B. Abareshi Arak University Department of Chemical Engineering, Faculty of Engineering Arak Iran

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F. Azari Arak University Department of Chemical Engineering, Faculty of Engineering Arak Iran

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I. Alhajri College of Technological Studies Chemical Engineering Department Shuwaikh Kuwait

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Abstract  

This article determines the operating conditions leading to maximum work in a regenerative cycle with an open feed water heater through a procedure that combines the use of artificial neural networks (ANNs) and genetic algorithms (GAs). Water is an active fluid in the thermodynamical cycle; an objective function is obtained by using vapor enthalpy (a nonlinear function of operating conditions). Utilizing classical methods for maximizing the objective function usually leads to suboptimal solutions. Therefore, this article uses ANNs to estimate the steam properties as a function of operating conditions and GAs to optimize the thermodynamical cycle. The operating conditions are chosen with the aim of gaining maximum work in a boiler for a specific heat. To estimate the thermodynamic properties, an ANN was used to provide the necessary data required in the GA calculation.

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Journal of Thermal Analysis and Calorimetry
Language English
Size A4
Year of
Foundation
1969
Volumes
per Year
1
Issues
per Year
24
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1388-6150 (Print)
ISSN 1588-2926 (Online)

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