Heat transfer dynamics modelling by means of clustering and swarm methods. (1st August 2019) - Lamraoui, Oualid; Boudouaoui, Yassine; Habbi, Hacene
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.