工程科学学报.第43卷，第12期：1689-1697.2021年12月 Chinese Journal of Engineering,Vol.43,No.12:1689-1697,December 2021 https://doi.org/10.13374/j.issn2095-9389.2021.10.07.004;http://cje.ustb.edu.cn 炼钢合金减量化智能控制模型及其应用 郑瑞轩，包燕平四，王仲亮 北京科技大学钢铁治金新技术国家重点实验室，北京100083 ☒通信作者，E-mail:baoyp(@ustb.edu.cn 摘要基于K均值聚类法对转炉出钢过程的合金损耗进行了研究，分析了影响合金损耗的关键因索，并将其分为3个聚 类，得到转炉出钢合金损耗最低的工艺模式.在此基础上，开发了基于PCA-BP神经网络和混合整数线性规划的合金减量化 智能控制系统，并以某炼钢厂为例进行了实际应用.通过对模型进行在线运行，验证了模型的准确性和实用性.使用该模型 后，提高了合金化钢液成分准确度，减少由传统人工经验计算配料造成的成本浪费和成分超标等情况.优化了合金配料方案， 降低了炼钢合金化成本，不同钢种铁合金加入总成本降低5.95%~14.74%，平均降幅11.72%. 关键词炼钢：铁合金：减量化：智能控制：成本 分类号TF741 Intelligent control model of steelmaking using ferroalloy reduction and its application ZHENG Rui-xuan,BAO Yan-ping,WANG Zhong-liang State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,Beijing 100083,China Corresponding author,E-mail:baoyp@ustb.edu.cn ABSTRACT The steel industry is a major energy consumer in China.As an effective measure for energy saving,cost and emission reduction,and higher efficiency among enterprises,ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality.In the steelmaking process,the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties.The chemical composition of molten steel is mainly adjusted using ferroalloys.With the development of ferroalloy smelting technology,ferroalloys of various types are developed.These ferroalloys show major gaps in cost performance and composition.Before ferroalloy addition,it is essential to determine an appropriate and cost- effective type and its amount for cost-saving purposes.However,the traditional method of offering a manually determined amount cannot meet the above requirement.Therefore,it is necessary to explore an intelligent ferroalloy addition method without human intervention.Based on the K-means clustering algorithm,this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process.The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process.Using this model,an intelligent control system for alloy reduction was developed.The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming.This system was implemented in a steelmaking plant,in which the accuracy and practicability of this model were verified by running it online.This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition,which are frequently seen in traditional calculations with a manual experience.The ferroalloy dosing scheme is also optimized,and the alloying cost of steelmaking is reduced.The total cost of adding ferroalloys of various types is reduced by 5.95%to 14.74%,with an average reduction of 11.72%. KEY WORDS steelmaking;ferroalloy;reduction;intelligent control;cost 收稿日期：2021-10-07 基金项目：国家自然科学基金资助项目(51874021)：钢铁冶金新技术国家重点实验室基金资助项目(41620020)炼钢合金减量化智能控制模型及其应用 郑瑞轩，包燕平苣，王仲亮 北京科技大学钢铁冶金新技术国家重点实验室，北京 100083 苣通信作者， E-mail: baoyp@ustb.edu.cn 摘    要    基于 K 均值聚类法对转炉出钢过程的合金损耗进行了研究，分析了影响合金损耗的关键因素，并将其分为 3 个聚 类，得到转炉出钢合金损耗最低的工艺模式. 在此基础上，开发了基于 PCA-BP 神经网络和混合整数线性规划的合金减量化 智能控制系统，并以某炼钢厂为例进行了实际应用. 通过对模型进行在线运行，验证了模型的准确性和实用性. 使用该模型 后，提高了合金化钢液成分准确度，减少由传统人工经验计算配料造成的成本浪费和成分超标等情况，优化了合金配料方案， 降低了炼钢合金化成本，不同钢种铁合金加入总成本降低 5.95%～14.74%，平均降幅 11.72%. 关键词    炼钢；铁合金；减量化；智能控制；成本 分类号    TF741 Intelligent control model of steelmaking using ferroalloy reduction and its application ZHENG Rui-xuan，BAO Yan-ping苣 ，WANG Zhong-liang State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China 苣 Corresponding author, E-mail: baoyp@ustb.edu.cn ABSTRACT    The steel industry is a major energy consumer in China. As an effective measure for energy saving, cost and emission reduction, and higher efficiency among enterprises, ferroalloy reduction has attracted increased attention in our work to reduce carbon dioxide emissions and realize carbon neutrality. In the steelmaking process, the chemical composition of molten steel is required to meet the target ratio to maintain certain metallurgical and mechanical properties. The chemical composition of molten steel is mainly adjusted using ferroalloys. With the development of ferroalloy smelting technology, ferroalloys of various types are developed. These ferroalloys show major gaps in cost performance and composition. Before ferroalloy addition, it is essential to determine an appropriate and cost￾effective type and its amount for cost-saving purposes. However, the traditional method of offering a manually determined amount cannot meet the above requirement. Therefore, it is necessary to explore an intelligent ferroalloy addition method without human intervention. Based on the K-means clustering algorithm, this paper studied ferroalloy loss in the basic oxygen furnace (BOF) steelmaking process. The key factors affecting the alloy loss were analyzed and divided into three clusters to obtain a process model of the lowest loss amount in the BOF steelmaking process. Using this model, an intelligent control system for alloy reduction was developed. The system is based on the principal component analysis and backpropagation neural network and mixed-integer linear programming. This system was implemented in a steelmaking plant, in which the accuracy and practicability of this model were verified by running it online. This model helped improve the accuracy of alloyed steel composition and reduce the unnecessary cost and extra composition, which are frequently seen in traditional calculations with a manual experience. The ferroalloy dosing scheme is also optimized, and the alloying cost of steelmaking is reduced. The total cost of adding ferroalloys of various types is reduced by 5.95% to 14.74%, with an average reduction of 11.72%. KEY WORDS    steelmaking；ferroalloy；reduction；intelligent control；cost 收稿日期: 2021−10−07 基金项目: 国家自然科学基金资助项目（51874021）；钢铁冶金新技术国家重点实验室基金资助项目（41620020） 工程科学学报，第 43 卷，第 12 期：1689−1697，2021 年 12 月 Chinese Journal of Engineering, Vol. 43, No. 12: 1689−1697, December 2021 https://doi.org/10.13374/j.issn2095-9389.2021.10.07.004; http://cje.ustb.edu.cn