The Amount Prediction and Optimization of the Returned Ore Generated from Sintering Process Based on SHAP Value and Ensemble Learning

Abstract

Sintering is the first step of ironmaking. The amount of returned ore is related to the quality of the sinter, yield, and cost of ironmaking. Herein, a novel and interpreted method is proposed to intelligently predict and optimize the amount of returned ore, which is not the case with the traditional “black box” model. The optimal combination of input variables is determined based on the SHapley Additive exPlanation (SHAP) value and eXtreme Gradient Boosting (XGBoost) selection. The model is constructed considering an R2 of 0.91 and average error of 2.9%, which verify its feasibility. Found by SHAP value analysis, when annular cooler speed and sintering machine speed are below 1.7 m min−1, sintering machine speed is below 2.1 m min−1, coal and coke powder amount are more than 14 and 8 t h−1, respectively, and the amount of the returned ore is reduced. In the optimization process, the joint optimization method with regard to the speed of the annular cooler and sintering machine can help solve 91% of the problem of high‐amount ore return, thereby reducing the returned ore amount 13.3 t h−1 at average and the returned ore rate by 1.8–2.7%.