Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach Article Swipe
B. Venkatesh
,
Ajmeera Kiran
,
Mudassir Khan
,
Mohammad Khalid Imam Rahmani
,
Laxmi Upadhyay
,
J. Chinna Babu
,
T. Lakshmi Narayana
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0304097
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0304097
In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0304097
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0304097&type=printable
- OA Status
- gold
- Cited By
- 6
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399485425
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399485425Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0304097Digital Object Identifier
- Title
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Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-10Full publication date if available
- Authors
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B. Venkatesh, Ajmeera Kiran, Mudassir Khan, Mohammad Khalid Imam Rahmani, Laxmi Upadhyay, J. Chinna Babu, T. Lakshmi NarayanaList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0304097Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0304097&type=printableDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0304097&type=printableDirect OA link when available
- Concepts
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Multi-objective optimization, Computer science, Heat exchanger, Response surface methodology, Design of experiments, Mass flow rate, Genetic algorithm, Mathematical optimization, Process engineering, Mechanical engineering, Mathematics, Engineering, Machine learning, Thermodynamics, Statistics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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