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Evaluation and Prediction of Blast Furnace Status Based on Big Data Platform of Ironmaking and Data Mining
ISIJ International ( IF 1.6 ) Pub Date : 2020-09-05 , DOI: 10.2355/isijinternational.isijint-2020-249
Hongyang Li 1 , Xiangping Bu 2 , Xiaojie Liu 1 , Xin Li 1 , Hongwei Li 1 , Fulong Liu 3 , Qing Lyu 1
Affiliation  

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.



中文翻译:

基于炼铁和数据挖掘大数据平台的高炉状态评估与预测

大数据在钢铁行业中的应用得到了广泛的发展。炼铁是一个多部门的联合生产过程,不断产生大量数据。需要构建大数据平台,以有效组织和充分利用炼铁的生产数据。在这项工作中,我们建立了一个高炉状态综合评估和预测系统,以实现高炉高产量,低消耗,高质量和长寿命的目标。该评估系统基于大数据平台并配备了因子分析方法,该方法可以通过考虑19个状态参数来定义和提取高炉生产指标中隐藏的公共因素,并且还可以计算出高炉状态的综合指标。该预测系统采用AdaBoost模型,该模型可以提前3小时准确预测高炉状态指数。评估结果表明,建议的高炉状态指数与所选时间段内高炉的实际状态高度一致。通过因子分析,验证了不同时期高炉状态指标与实际情况的符合程度。尽管评估和预测系统在当前的生产环境中显示出很高的准确性,但从长远来看,由于高炉生产的变化,它可能仍需要定期校准和更新。高炉在线综合评价预测系统可以有效地帮助运营商优化高炉运行,保持高炉的稳定。评估结果表明,建议的高炉状态指数与所选时间段内高炉的实际状态高度一致。通过因子分析,验证了不同时期高炉状态指标与实际情况的符合程度。尽管评估和预测系统在当前的生产环境中显示出很高的准确性,但从长远来看,由于高炉生产的变化,它可能仍需要定期校准和更新。高炉在线综合评价预测系统可以有效地帮助运营商优化高炉运行,保持高炉的稳定。评估结果表明,建议的高炉状态指数与所选时间段内高炉的实际状态高度一致。通过因子分析,验证了不同时期高炉状态指标与实际情况的符合程度。尽管评估和预测系统在当前的生产环境中显示出很高的准确性,但从长远来看,由于高炉生产的变化,它可能仍需要定期校准和更新。高炉在线综合评价预测系统可以有效地帮助运营商优化高炉运行,保持高炉的稳定。通过因子分析,验证了不同时期高炉状态指标与实际情况的符合程度。尽管评估和预测系统在当前的生产环境中显示出很高的准确性,但从长远来看,由于高炉生产的变化,它可能仍需要定期校准和更新。高炉在线综合评价预测系统可以有效地帮助运营商优化高炉运行,保持高炉的稳定。通过因子分析,验证了不同时期高炉状态指标与实际情况的符合程度。尽管评估和预测系统在当前的生产环境中显示出很高的准确性,但从长远来看,由于高炉生产的变化,它可能仍需要定期校准和更新。高炉在线综合评价预测系统可以有效地帮助运营商优化高炉运行,保持高炉的稳定。

更新日期:2020-09-12
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