Optimum model for predicting temperature settings on hot dip galvanising line

Abstract

Controlling the annealing cycle in a hot dip galvanising line (HDGL) is vital if each coil treated is to be properly galvanised and the steel is to have the right properties. Current HDGL furnace control models usually take into account the dimensions of the coil to be dipped and, in some cases, the type of steel. This paper presents a new model for monitoring furnace temperature settings, which considers not just the coil dimensions but also the chemical composition of the steel. This enables the model to be adjusted more suitably to each type of steel to be dipped, so that the HDGL annealing cycle is optimised and rendered more efficient in dealing with new products. The ultimate aim is to find a model that is equally efficient for new types of steel coil that have not been processed before and whose dimensions and chemical compositions are different from coils processed previously. To find the best model, this paper compares various new and classical algorithms for developing a precise and efficient prediction model capable of determining the three temperature settings for heating on an HDGL located in Avilés (Spain) on the basis of the physical and chemical characteristics of the coils to be processed and the preset process conditions.

Keywords:

• HOT DIP GALVANISING LINE
• ANNEALING FURNACE
• DATA MINING
• ARTIFICIAL INTELLIGENCE
• MODELLING INDUSTRIAL PROCESSES