Pattern recognition and classification of blast furnace wall temperatures

Abstract

A model for classification, visualisation, and interpretation of blast furnace wall temperature distributions is presented. The model is based on an unsupervised learning method and depicts the results on a two-dimensional feature map, which is used as an operation diagram when the evolution of the wall temperatures is studied. The classifier has been implemented in the automation system of two Finnish blast furnaces and has proved to be a useful tool for operator guidance in daily practice. The model has been further extended by correlating the wall temperature classes with important performance indices of the furnace, which provides an interpretation of the temperature patterns in terms of process variables that are better understood. The theory of the model is described in the paper and some examples are presented to illustrate its features and use.