王欢, 王敏, 刘庆, 邢立东, 包燕平. LF精炼工艺智能控制与决策模型研究进展[J]. 工程科学学报, 2024, 46(10): 1739-1752. DOI: 10.13374/j.issn2095-9389.2023.12.19.001
引用本文: 王欢, 王敏, 刘庆, 邢立东, 包燕平. LF精炼工艺智能控制与决策模型研究进展[J]. 工程科学学报, 2024, 46(10): 1739-1752. DOI: 10.13374/j.issn2095-9389.2023.12.19.001
WANG Huan, WANG Min, LIU Qing, XING Lidong, BAO Yanping. Research progress on intelligent control and decision-making models for the ladle furnace refining process[J]. Chinese Journal of Engineering, 2024, 46(10): 1739-1752. DOI: 10.13374/j.issn2095-9389.2023.12.19.001
Citation: WANG Huan, WANG Min, LIU Qing, XING Lidong, BAO Yanping. Research progress on intelligent control and decision-making models for the ladle furnace refining process[J]. Chinese Journal of Engineering, 2024, 46(10): 1739-1752. DOI: 10.13374/j.issn2095-9389.2023.12.19.001

LF精炼工艺智能控制与决策模型研究进展

Research progress on intelligent control and decision-making models for the ladle furnace refining process

  • 摘要: LF精炼能有效控制钢水成分和温度,并且在炼钢–连铸之间起缓冲协调生产节奏的作用. 在LF精炼中运用模型进行控制与决策,可以进一步规范精炼操作,提高钢水质量和稳定性,同时结合自动控制,将有力推动智能精炼的发展,实现炼钢流程的优化和效率提升. 在钢铁行业智能制造的背景下,LF精炼工艺模型不再局限于单功能模型的建立和部署,开始朝着集成化、自动化和智能化的方向发展,同时其功能也由单一的预测和推荐转变为整体的智能控制和决策. 因此,建立集成化模型,规范现场工艺,改善数据质量,同时结合自动化控制和闭环反馈,进一步来实现智能控制模型成为LF控制模型未来研究和应用的重要方向. 本文总结了LF精炼控制与决策模型中关于合金化模型、造渣模型、温度模型、吹氩控制模型、钙处理模型等单功能模型以及智能精炼技术的发展与研究现状. 系统梳理了不同模型的建模原理和实现功能,展望了未来LF工艺智能控制与决策模型的发展方向,为后续LF智能精炼技术的开发和应用提供参考.

     

    Abstract: Ladle furnace (LF) refining can effectively control the composition and temperature of molten steel and plays a role in cushioning and coordinating the production rhythm between steelmaking and continuous casting. The use of models for control and decision-making in LF refining can further standardize the refining operations, improve the quality and stability of molten steel, and, combined with automatic control, will strongly promote the development of intelligent refining to achieve optimization of steelmaking and improve efficiency. Regarding promoting intelligent manufacturing in the steel industry, the LF refining process model is no longer limited to the establishment and deployment of single-function models and has begun to develop in the direction of integration, automation, and intelligence while its function has also changed from a single prediction and recommendation to overall intelligent control and decision-making. LF process control and decision models are mostly single-function models, but few integrate applications. Due to the complexity and uncertainty of the refining process, these models have differences in stability and accuracy. Therefore, establishing an integrated model, standardizing the field process, improving the data quality, and combining automatic control and closed-loop feedback to further realize the intelligent control model have become important directions for future research and application of LF control models. Herein, the development and research status of LF refining control and decision models are summarized, including the alloying model, slagging model, temperature model, argon blowing control model, calcium treatment model, and other single-function models, as well as intelligent refining technology. The modeling principles and functions of these models are systematically reviewed, and future development directions of LF process intelligent control and decision models are prospected, providing a reference for the subsequent development and application of LF intelligent refining technology. The establishment and real landing of LF intelligent control and decision models not only require the realization and linkage of process control and decision models but also propose higher requirements for iron and steel enterprises. The realization of LF intelligent control and decision-making models can greatly improve the consistency and qualified rate of product quality, reduce energy consumption and cost, reduce manual intervention, and shorten the smelting cycle, thus improving the competitiveness of enterprises. With the continuous upgrading and improvement of model design, automation technology, and steel mill site environment, the application and development of LF intelligent control and decision-making models show great potential in realizing green, low-carbon, and intelligent manufacturing and would make great contributions to the progress and transformation and upgrading of the steel industry in the future.

     

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