Title: Heat transfer dynamics modelling by means of clustering and swarm methods

Authors: Oualid Lamraoui; Yassine Boudouaoui; Hacene Habbi

Addresses: Applied Automation Laboratory, FHC, M'hamed Bougara University of Boumerd'es, Boumerd'es, Algeria ' Applied Automation Laboratory, FHC, M'hamed Bougara University of Boumerd'es, Boumerd'es, Algeria ' Applied Automation Laboratory, FHC, M'hamed Bougara University of Boumerd'es, Boumerd'es, Algeria

Abstract: This paper deals with the modelling problem of heat transfer dynamics in thermal exchanger process by using fuzzy prediction approaches. Clustering and swarm-based optimisation methods are used to derive heat transfer dynamical models to predict temperature variations of hot and cold fluids in the exchanger. The clustering method relies on a one-shot potential calculating strategy to extract the fuzzy sets distribution from the data space. However, the swarm optimisation method employs a subject function to optimise the premise and conclusion parameters of the fuzzy structure. Experimental data extracted from a pilot exchanger process is used to learn the fuzzy models, and their performances are compared on both training and testing measurement data.

Keywords: clustering; fuzzy modelling; swarm optimisation; bee colony; intelligent system; dynamics; prediction; temperature; heat transfer; exchanger process.

DOI: 10.1504/IJIEI.2019.101550

International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.4, pp.346 - 365

Received: 11 Jun 2018
Accepted: 05 Jan 2019
Published online: 12 Aug 2019 *

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