Adaptive least squares support vector machine predictor for blast furnace ironmaking process

Abstract

Blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction. For this reason, based on recursive updating algorithm, an adaptive least squares support vector machine (LS-SVM) predictor is presented for prediction task of silicon content in blast furnace (BF) hot metal. The predicator employs recursive updating algorithm to get the precise solution of the latest LS-SVM model and avoid the long process of running through the whole model. Theoretically, the computational complexity is reduced significantly from O(n 3 m + m 4) to O(n 3 + m 3). Experiments on two different BF data sets demonstrate that the proposed adaptive LS-SVM predicator is suitable for the task of predicting BF ironmaking process for its high hitting percentage and time saving.