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

José Luis de Andrés Honrubia, José Gaviria de la Puerta, Fernando Cortés, Urko Aguirre-Larracoechea, Aitor Goti and Jone Retolaza
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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

Mathematics, 2021, vol. 9, issue 10, 1-23

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.

Keywords: shell and tube heat exchangers; neural networks; multi-objective optimisation; industrial application (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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