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

Autor: Kiran A; Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India., Nagaraju C; Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India., Babu JC; Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India., Venkatesh B; Department of Mechanical Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India., Kumar A; School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India., Khan SB; Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon., Albuali A; Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia., Basheer S; Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Mar 25; Vol. 19 (3), pp. e0298731. Date of Electronic Publication: 2024 Mar 25 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0298731
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Kiran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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