Feature Selection and Long-term Modeling for the Blast Furnace Iron- making Process Based on Random Forests

So far, the accurate modeling and control of blast furnace iron-making process (BFIP) is still an open problem due to its excessive complexity. Aiming at the issue of long time-delay and strong cross-coupling characteristics of BFIP, the random forests (RF) algorithm is introduced for predicting the silicon content in hot metal, which is the most key indicator of inner state of blast furnace. In the proposed model, both short and long-term BFIP features are adopted as inputs, without variable pre-selecting, to modeling the long-term dynamics of BFIP. Simulation results show that the RF algorithm can successfully identify the importance of different features (the latest silicon content in hot metal obtains the largest value of importance), can effectively decrease the effect of the redundancy and cross-coupling among variables. The RF model also can achieve similar or better prediction performance compared with support vector machines (SVM), which indicates that it is potential to modeling such BFIP-type complex industrial process using RF algorithm.