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Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

Author

Listed:
  • Ajmeera Kiran
  • Ch Nagaraju
  • J Chinna Babu
  • B Venkatesh
  • Adarsh Kumar
  • Surbhi Bhatia Khan
  • Abdullah Albuali
  • Shakila Basheer

Abstract

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.

Suggested Citation

  • Ajmeera Kiran & Ch Nagaraju & J Chinna Babu & B Venkatesh & Adarsh Kumar & Surbhi Bhatia Khan & Abdullah Albuali & Shakila Basheer, 2024. "Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-30, March.
    • Handle: RePEc:plo:pone00:0298731
    DOI: 10.1371/journal.pone.0298731
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    References listed on IDEAS

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    1. Mohammed Algarni & Mashhour A. Alazwari & Mohammad Reza Safaei, 2021. "Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling," Mathematics, MDPI, vol. 9(24), pages 1-30, December.
    2. Adel Ghazikhani & Iman Babaeian & Mohammad Gheibi & Mostafa Hajiaghaei-Keshteli & Amir M. Fathollahi-Fard, 2022. "A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations," Sustainability, MDPI, vol. 14(11), pages 1-27, May.
    3. Azad, Abazar Vahdat & Amidpour, Majid, 2011. "Economic optimization of shell and tube heat exchanger based on constructal theory," Energy, Elsevier, vol. 36(2), pages 1087-1096.
    4. Florides, Georgios & Kalogirou, Soteris, 2007. "Ground heat exchangers—A review of systems, models and applications," Renewable Energy, Elsevier, vol. 32(15), pages 2461-2478.
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