基于工业大数据的智能高炉炼铁技术关键问题与进展

石泉; 唐珏; 储满生

doi:10.1007/s12613-023-2636-3

Volume 30 Issue 9

Sep. 2023

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文章导航 > 矿物冶金与材料学报（英文） > 2023 > 30(9): 1651-1666

Quan Shi, Jue Tang, and Mansheng Chu, Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1651-1666. https://doi.org/10.1007/s12613-023-2636-3

Cite this article as:

Quan Shi, Jue Tang, and Mansheng Chu, Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology, Int. J. Miner. Metall. Mater., 30(2023), No. 9, pp. 1651-1666. https://doi.org/10.1007/s12613-023-2636-3

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特约综述

基于工业大数据的智能高炉炼铁技术关键问题与进展

1)
东北大学冶金学院，沈阳110819，中国
2)
东北大学低碳钢铁前沿技术研究院，沈阳110819，中国
3)
低碳钢铁前沿技术教育部工程研究中心，沈阳 110819，中国

通讯作者:
唐珏 E-mail: tangj@smm.neu.edu.cn

储满生 E-mail: chums@smm.neu.edu.cn

文章亮点

（1）对钢铁行业数字化、智能化转型的意义进行了详细阐述。

（2）从数据到模型对智能化高炉炼铁技术存在的问题和发展进行了系统性分析。

（3）以东北大学研究基础为引，为智能化技术在高炉炼铁中的应用提供了借鉴。

中文摘要

高炉冶炼过程是最典型的“黑箱”过程，其复杂性和不确定性对炉况顺行带来了巨大挑战。但高炉炼铁拥有丰富的数据资源，数据科学、智能技术的快速发展，为解决高炉炼铁过程中不确定性问题提供了有效手段。本文围绕人工智能技术在高炉炼铁中的应用，从高炉数据治理、时滞性与关联性分析、高炉关键变量预测、高炉炉况评价和高炉多目标智能优化五个方面对现阶段智能化高炉炼铁技术的发展和存在问题进行总结与分析。在高炉数据预处理方面，应综合考虑数据问题和算法特性，科学选择数据处理方法，才能使高炉数据质量得到有效改善；在高炉重要特征分析方面，需要先消除高炉参数间时滞性的影响，才能保证高炉参数与炉况经济指标之间逻辑关系的准确性；在高炉参数预测与炉况评价方面，需要构建数据信息与工艺机理融合的高炉智慧模型，才能够实现高炉关键指标的精准预测与高炉炉况的科学评价。在高炉参数优化方面，应该以低风险、低成本、高回报作为优化准则，追求优化效果的同时还应综合考虑现场操作的可行度和操作成本。本文内容有助于提高工艺操作者对智能化高炉技术的整体认识和理解。此外，将大数据技术与高炉工艺相结合，有利于提高数据模型在实际生产中的实用性，促进智能化技术在高炉炼铁中的应用。

关键词:

Invited Review

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

+ Author Affiliations

Abstract

Abstract

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation, a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively. This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.
- BF ironmaking;
- intelligent BF;
- industrial big data;
- machine learning;
- integrated mechanism and data

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