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Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

Author

Listed:
  • José Luis de Andrés Honrubia

    (Deusto Digital Industry Chair, Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48080 Bilbao, Spain)

  • José Gaviria de la Puerta

    (Deusto Digital Industry Chair, Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48080 Bilbao, Spain)

  • Fernando Cortés

    (Deusto Digital Industry Chair, Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48080 Bilbao, Spain)

  • Urko Aguirre-Larracoechea

    (Faculty of Health Sciences, University of Deusto, Avda. Universidades 24, 48080 Bilbao, Spain)

  • Aitor Goti

    (Deusto Digital Industry Chair, Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48080 Bilbao, Spain)

  • Jone Retolaza

    (Accenture Bilbao Industry X.0 Center, Parque Tecnológico de Bizkaia, Astondo Bidea, Edificio 602, 48160 Bilbao, Spain)

Abstract

This 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.

Suggested Citation

  • José Luis de Andrés Honrubia & José Gaviria de la Puerta & Fernando Cortés & Urko Aguirre-Larracoechea & Aitor Goti & Jone Retolaza, 2021. "Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-23, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1120-:d:555325
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    References listed on IDEAS

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    Cited by:

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