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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2018

14.11.2016 | Original Article

Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm

verfasst von: Shreesh V. Dhavle, Anand J. Kulkarni, Apoorva Shastri, Ishaan R. Kale

Erschienen in: Neural Computing and Applications | Ausgabe 1/2018

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Abstract

Heat exchangers play a key role in wide industrial applications. Due to their complex design and high manufacturing cost, their efficient operation and optimum design are quite important for overall cost minimization. There have been several optimization algorithms developed so far for the optimum design of the shell-and-tube heat exchanger (STHE). In this paper, the ability to emerge AI-based optimization method referred to as cohort intelligence (CI) is demonstrated by solving the design and economic optimization of the STHEs. Three cases were solved. These three cases include fluids, which are different at both the shell side and tube side with different inlet and outlet temperatures at the shell side and tube side. The associated key variables such as tube outside diameter, baffle spacing, pitch size, shell inside diameter and number of tube passes that decide the total cost of the heat exchanger were optimized. The performance of the CI method is compared with existing algorithms. The quality and robustness of the CI solution at reasonable computational cost highlighted its applicability by solving real-world problems from mechanical engineering domain.
Vorheriger Artikel Cluster merging based on a decision threshold
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Metadaten
Titel
Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm
verfasst von
Shreesh V. Dhavle
Anand J. Kulkarni
Apoorva Shastri
Ishaan R. Kale
Publikationsdatum
14.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2683-z

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