Comparison of data-driven prediction methods for comprehensive coke ratio of blast furnace

Abstract

The emission of blast furnace (BF) exhaust gas has been criticized by society. It is momentous to quickly predict the comprehensive coke ratio (CCR) of BF, because CCR is one of the important indicators for evaluating gas emissions, energy consumption, and production stability, and also affects composite economic benefits. In this article, 13 data-driven prediction techniques, including six conventional and seven ensemble methods, are applied to predict CCR. The result of ten-fold cross-validation indicates that multiple linear regression (MLR) and support vector regression (SVR) based on radial basis function are superior to the other methods. The mean absolute error, the root mean square error, and the coefficient of determination (R 2) of the MLR model are 1.079 kg·t−1, 1.668, and 0.973, respectively. The three indicators of the SVR model are 1.158 kg·t−1, 1.878, and 0.975, respectively. Furthermore, AdaBoost based on linear regression has also strong prediction ability and generalization performance. The three methods have important significances both in theory and in practice for predicting CCR. Moreover, the models constructed here can provide valuable hints into realizing data-driven control of the BF process.

Keywords

• blast furnace
• comprehensive coke ratio
• multiple linear regression
• support vector regression
• adaboost
• data-driven.