Development and application of a multi-objective tool for thermal design of heat exchangers using neural networks

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dc.contributor.authorAndrés Honrubia, José Luís de
dc.contributor.authorGaviria de la Puerta, José
dc.contributor.authorCortés Martínez, Fernando
dc.contributor.authorAguirre Larracoechea, Urko
dc.contributor.authorGoti Elordi, Aitor
dc.contributor.authorRetolaza, Jone
dc.date.accessioned2025-01-15T08:05:29Z
dc.date.available2025-01-15T08:05:29Z
dc.date.issued2021-05
dc.date.updated2025-01-15T08:05:29Z
dc.description.abstractThis paper presents the design of a multi-objective tool for sizing shell and tube heat exchangers (STHX), developed under a University/Industry collaboration. This work aims to show the feasibility of implementing artificial intelligence tools during the design of Heat Exchangers in industry. The design of STHX optimisation tools using artificial intelligence algorithms is a visited topic in the literature, nevertheless, the degree of implementation of this concept is uncommon in industrial companies. Thus, the challenge of this research consists of the development of a tool for the design of STHX using artificial intelligence algorithms that can be used by industrial companies. The approach is implemented using a simulated dataset contrasted with ARA TT, the company taking part in the project. The given dataset to develop a theoretical STHX calculator was modeled using MATLAB. This dataset was used to train seven neural networks (NNs). Three of them were mono-objective, one per objective to predict, and four were multi-objective. The last multi-objective NN was used to develop an inverse neural network (INN), which is used to find the optimal configuration of the STHXs. In this specific case, three design parameters, the pressure drop on the shell side, the pressure drop on the tube side and heat transfer rate, were jointly and successfully optimised. As a conclusion, this work proves that the developed tool is valid in both terms of effectiveness and user-friendliness for companies like ARA TT to improve their business activity.en
dc.identifier.citationHonrubia, J. L. d. A., de la Puerta, J. G., Cortés, F., Aguirre-Larracoechea, U., Goti, A., & Retolaza, J. (2021). Development and application of a multi-objective tool for thermal design of heat exchangers using neural networks. Mathematics, 9(10). https://doi.org/10.3390/MATH9101120
dc.identifier.doi10.3390/MATH9101120
dc.identifier.eissn2227-7390
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2225
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2021 by the authors
dc.subject.otherIndustrial application
dc.subject.otherMulti-objective optimisation
dc.subject.otherNeural networks
dc.subject.otherShell and tube heat exchangers
dc.titleDevelopment and application of a multi-objective tool for thermal design of heat exchangers using neural networksen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue10
oaire.citation.titleMathematics
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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