Application of techniques of dimension reduction to predict the steel quality at the end of the secondary steelmaking

Nowadays, there is not any model capable to predict the steel quality before the end of the secondary steelmaking where corrective actions are still possible. In this paper it is presented a modelization by SOM (self organizing map) of a Secondary steelmaking facility able to estimate the steel carbon content at the end of the treatment considering process variables. Different network configurations were tested for two different input variables sets in order to obtain the best result, which is measured as the minimum error committed in the estimation.