4.4 Article

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

Journal

ISIJ INTERNATIONAL
Volume 61, Issue 1, Pages 108-118

Publisher

IRON STEEL INST JAPAN KEIDANREN KAIKAN
DOI: 10.2355/isijinternational.ISIJINT-2020-249

Keywords

big data platform of ironmaking; factor analysis; AdaBoost model; BF comprehensive status

Funding

  1. key program of national nature science foundation of china [U1360205]
  2. Hebei province higher education technology research project [QN2019200]

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Big data applications in the steel industry have been widely developed, with a focus on building a comprehensive evaluation and prediction system for blast furnaces. This system utilizes factor analysis and the AdaBoost model to accurately assess and forecast the blast furnace status index. Regular calibration and updates are necessary for long-term adaptability to changes in blast furnace production.
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.

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