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

IF 5.6 2区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Quan Shi, Jue Tang, Mansheng Chu
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引用次数: 3

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.

基于工业大数据的智能高炉炼铁技术关键问题与进展
高炉炼铁是最典型的“黑箱”过程,其复杂性和不确定性给炉况判断和高炉运行带来了极大的挑战。高炉炼铁具有丰富的数据资源,数据科学和智能技术的快速发展将为解决高炉炼铁过程中的不确定性问题提供有效手段。本文主要研究了人工智能技术在高炉炼铁中的应用。从五个方面对目前高炉智能炼铁技术进行了总结和分析。这些方面包括高炉数据管理、时滞和相关性分析、高炉关键变量预测、高炉状态评估、高炉运行多目标智能优化等。对目前进展中存在的问题提出了解决方案和建议,并对未来前景和技术突破进行了展望。为有效提高BF数据质量,综合考虑数据问题和算法特点,科学选择数据处理方法。在分析高炉重要特性时,消除了时滞的影响,保证了高炉参数与经济指标之间的准确逻辑关系。在高炉参数预测和高炉状态评价方面,建立了集数据信息和过程机制于一体的高炉智能模型,有效实现了高炉关键指标的准确预测和高炉状态的科学评价。高炉参数优化以低风险、低成本、高回报为优化准则,在追求优化效果的同时,综合考虑可行性和现场运行成本。这项工作将有助于提高工艺操作员对智能高炉技术的整体认识和理解。此外,将大数据技术与工艺相结合,将提高数据模型在实际生产中的实用性,促进智能技术在高炉炼铁中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.30
自引率
16.70%
发文量
205
审稿时长
2 months
期刊介绍: International Journal of Minerals, Metallurgy and Materials (Formerly known as Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material) provides an international medium for the publication of theoretical and experimental studies related to the fields of Minerals, Metallurgy and Materials. Papers dealing with minerals processing, mining, mine safety, environmental pollution and protection of mines, process metallurgy, metallurgical physical chemistry, structure and physical properties of materials, corrosion and resistance of materials, are viewed as suitable for publication.
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