Ensemble pattern trees for predicting hot metal temperature in blast furnace

Abstract

In steel industry, it is crucial to predict hot metal temperature (HMT), which is strongly related to the product quality and the thermal state, to keep high productivity of the blast furnace. The present work proposes a novel ensemble pattern trees model to predict HMT. Ensemble pattern trees is a robust nonlinear modeling method, which aggregates a set of pattern trees models into a single predictive model via the bagging technique. Ensemble pattern trees overcomes the drawback of single pattern trees which may not be robust enough against the random variations such as process perturbations and noises in the blast furnace. In addition, a novel variable importance measure derived from the ensemble pattern trees is proposed to understand which process variables affect the final hot metal quality. The proposed method was validated through an industrial blast furnace ironmaking process, and the results have demonstrated its superiority to several conventional methods.