Improvement and optimisation of hot dip galvanising line using neural networks and genetic algorithms

Abstract

In the present article, an application is present based on the combination of genetic algorithms and neural networks, used to improve the annealing process of a hot dip galvanising line with steel coils. The main objective is to determine the best settings for a furnace in order to reduce the margin of error between the actual strip temperature and expected temperature, not only for each coil that forms the strip, but also in the zones of the strip where transitions are formed by coils with different dimensions or steel types. Basically, the methodology consists in training a multilayer perceptron (MLP), which then determines the settings of the furnace and the speed of the strip according to the type of coil that forms the same strip. Another MLP is used to predict the dynamic behaviour of the strip related to its fluctuations in speed, as well as the temperature of the furnace. In this way, using simulations and genetic algorithms, the optimum settings of the furnace are determined, as well as the speed of the strip in those zones where there are changes in the coils, namely, in dimensions and types of steel.

Keywords:

• NEURAL NETWORKS
• HOT DIP PROCESS
• GENETIC ALGORITHMS
• IMPROVING INDUSTRIAL PROCESS