Evaluation and Prediction Models for Blast Furnace Operating Status Based on Big Data Mining

Abstract

Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

Keywords

• evaluation
• blast furnace operating status
• data warehouse
• prediction
• features engineering
• big data mining