Predicción basada en redes neuronales del tiempo de procesamiento de los trenes desbastador y laminador en una instalación de laminación en caliente

V. Colla; M. Vannucci; R. Valentini

doi:10.3989/revmetalm.0850

Authors

V. Colla Scuola Superiore Sant’Anna, Polo Sant’Anna Valdera, Laboratorio PERCRO, Steel and Industrial Automation Division
M. Vannucci Scuola Superiore Sant’Anna, Polo Sant’Anna Valdera, Laboratorio PERCRO, Steel and Industrial Automation Division
R. Valentini Università di Pisa, Facoltà di Ingegneria, Dipartimento di Ingegneria Chimica, Chimica Industriale e Scienza dei Materiali

DOI:

https://doi.org/10.3989/revmetalm.0850

Keywords:

Hot rolling steel, Slab, Coil, Mill pacing, Neural networks

Abstract

The paper presents a model based on neural networks which is able to predict the time required to pass the different gauges of a roughing and finishing mill as function of some slab features and process parameters. The final aim of the work is to increase the rolling efficiency while avoiding collisions and queues that cause time and energy losses. Neural networks are suitable to this prediction task as they are particularly able to cope with unknown non linear relationships between input and output variables. Moreover they can learn from real industrial data and therefore do not require prior assumptions or mathematical modelling of the process and transferability is ensured by the possibility to use different databases coming from different rolling mills. In the paper, two different kinds of neural network- based models have been proposed, their performances have been discussed and compared.

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