SIMPLIFYING THE MODEL OF A COMPLEX INDUSTRIAL PROCESS USING INPUT VARIABLE SELECTION

Abstract

This paper deals with experience gained from building a neural model of a Linz-Donawitz (LD) steel converter. The complexity of the process makes this task difficult because many variables affect the quality of the resulted steel. The paper details the simplification of the neural model using input variable selection (IVS) methods. Three types of models were investigated: one using the originally measured physical parameters, and two types using transformations, namely independent component analysis and principal component analysis. Transformations were applied to derive new parameter spaces where the importance of parameters shows higher differences. The relevance of the original and the transformed parameters were measured by different ways.