Optimising annealing process on hot dip galvanising line based on robust predictive models adjusted with genetic algorithms

Abstract

This paper describes the process for optimising the annealing cycle on a hot dip galvanising line based on a combination of the techniques of artificial intelligence and genetic algorithms for creating two types of regression models. The first model can predict the furnace operating temperature for each coil and is trained to learn from the experience of the plant operators when the process has been correctly adjusted in ‘manual mode’ and from the control system when it has been properly operated in ‘automatic mode’. Once the scheduling has been optimised, and using the two predictive models, a computer simulation is made of the galvanising process in order to optimise the target settings when there are sudden transitions in the steel strip. This substantially improves the thermal treatment, as these sudden transitions may occur when there are two welded coils differing in size and type of steel, whereby a drastic change in strip specifications leads to irregular thermal treatments that may affect the steel’s coating or properties in that part of the coil.

Keywords:

• Scheduling
• Annealing process
• Genetic algorithms

The authors thank the ‘Dirección General de Investigación’ of the Spanish Ministry of Education and Science for the financial support of project no. DPI2007‐61090 and the European Union for project no. RFS‐PR‐06035.Finally, the authors also thank the Autonomous Government of La Rioja for its support through the 3° Plan Riojano de I+D+i.