Evaluation and Prediction of Blast Furnace Status Based on Big Data Platform of Ironmaking and Data Mining

摘要

The applications of big data in the steel industry are widely developed. Ironmaking is a multi-sectoral joint-operation production process that generates massive data constantly. It is required to build the big data platform to efficiently organize and fully utilize the production data of the ironmaking. In this work, we build a comprehensive status evaluation and prediction system for the blast furnace (BF) to achieve the goal of high production, low consumption, high quality and long life of the BF. The evaluation system is based on the big data platform and equipped with the factor analysis method, which can define and extract the hidden common factors in the production index of the BF by considering 19 state parameters and can calculate the comprehensive BF status index as well. The prediction system employs the AdaBoost model which can accurately predict the BF status index 3 hours in advance. Evaluation results show that the proposed BF status index is highly consistent with the actual status of the BF in the selected time period. The coincidence degree between BF status index in different time periods and the actual situation is also verified by factor analysis. Although the evaluation and prediction system demonstrates high accuracy in current production environment, it may still need calibrate and update regularly due to the changing of the BF production in the long run. The online comprehensive evaluation and prediction system for BF can effectively assist operators to optimize the BF operation and maintain the stabilization of BF.