Neural, fuzzy and Grey-Box modelling for entry temperature prediction in a hot strip mill

In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill therefore they have to be estimated. Estimation of process variables, particularly temperature, is of crucial importance for the bar front section to fulfil quality requirements and it must be performed in the shortest possible time to keep heat. Currently, temperature estimation is performed by physical modelling, however it is highly affected by measurement uncertainties, variations in the incoming bar conditions and final product changes. In order to overcome these problems, artificial intelligence techniques as artificial neural networks and fuzzy logic have been proposed. In this paper, several neural networks, neural based Grey-Box models, fuzzy inference systems, and fuzzy based Grey-Box models are designed and tested with experimental data to estimate scale breaker entry temperature given the relevance of this variable. Their performances are compared against that of the physical model used in plant. Some of the systems presented in this work were proved to have better performance indexes and hence better prediction capabilities than the current physical models used in plant.