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

• Published in: Neural computing & applications 2018-07, Vol.30 (1), p.111-125
• Main Authors: Dhavle, Shreesh V., Kulkarni, Anand J., Shastri, Apoorva, Kale, Ishaan R.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational fluid dynamics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Design optimization; Heat exchangers; Image Processing and Computer Vision; Industrial applications; Mechanical engineering; Original Article; Probability and Statistics in Computer Science; Robustness (mathematics); Tube heat exchangers
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2683-z

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.