Data Based Modeling Approach to Iron and Steel Making Processes

Abstract

Iron and steel making processes are very complex in nature and we need prediction tools which can act as a guideline to control them. Various modeling techniques have been adopted in order to develop good prediction models. These models are the part of automation control systems in a steel plant. These models could be fundamental in nature based upon physical and chemical laws of the process on one hand and empirical approach on the other hand. Subject to the condition that there could be lot of variations due to error in input measurements and other uncertain factors beyond control, the actual process will always have some degree of uncertainty. Therefore models which are based upon actual plant data are more reliable as compared to the fundamental models. Even fundamental models could also be used in association with data based models where various relationships and coefficients of uncertainty are evaluated based upon actual plant data. In this paper data based modeling approach is demonstrated for BOF steelmaking process in particular. A comparative study has been done for combination of various approaches like ANN (Artificial Neural Networks), MTS (Mahalanobis Taguchi systems) and PCA (Principal Component Analysis) and MLR (multivariate regression analysis) to develop prediction models based upon industrial data.