Diagnosis of the instability of the cooling behaviour of flat steel products through parametric characterisation, neural networks and statistics

Valentina Colla; Marco Vannucci; Antonella Dimatteo

doi:10.1016/j.isatra.2010.01.001

Diagnosis of the instability of the cooling behaviour of flat steel products through parametric characterisation, neural networks and statistics

ISA Trans. 2010 Apr;49(2):235-43. doi: 10.1016/j.isatra.2010.01.001. Epub 2010 Feb 24.

Authors

Valentina Colla¹, Marco Vannucci, Antonella Dimatteo

Affiliation

¹ Scuola Superiore Sant'Anna, Laboratorio PERCRO, Polo Sant'Anna Valdera, 56025, Pontedera (PI), Italy.

PMID: 20185127
DOI: 10.1016/j.isatra.2010.01.001

Abstract

This paper presents a mathematical model developed by means of an analytical function whose shape depends on the values of a few parameters for the run-out table cooling which is used in hot strip mills. The system relies on a first-order differential equation for describing the temperature loss along the run-out table. Neural networks have been applied in order to find correlations between the model parameters and the steel and process variables. Then, traditional statistical techniques have been applied in order to evaluate the stability of the cooling behaviour. Numerical results obtained on an industrial database are presented and discussed.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computer Simulation
Equipment Design
Metallurgy / statistics & numerical data*
Models, Statistical*
Neural Networks, Computer*
Steel*
Temperature

Substances

Steel