Heat transfer dynamics modelling by means of clustering and swarm methods. (1st August 2019)

• Record Type: Journal Article
• Authors: Lamraoui, Oualid; Boudouaoui, Yassine; Habbi, Hacene
• Is Part Of: International journal of intelligent engineering informatics. Volume 7:Number 4(2019)
• Page Start: 346
• Page End: 365
• Publication Date: 2019-08-01
• Subjects: clustering -- fuzzy modelling -- swarm optimisation -- bee colony -- intelligent system -- dynamics -- prediction -- temperature -- heat transfer -- exchanger process; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

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.