基于代价敏感学习的钢铁企业转炉煤气柜柜位预测  

Cost-sensitive learning-based prediction of Linz Donawitz gas holder level in iron and steel enterprises

作　　者：王兴 赵伟 WANG Xing;ZHAO Wei(Management and Information Technology Department,Ansteel Group Co.,Ltd.,Anshan 114031,China;Anshan Iron and Steel Group Co.,Ltd.,Anshan 114031,China)

机构地区：[1]鞍钢集团有限公司管理与信息化部,辽宁鞍山114031 [2]鞍山钢铁集团有限公司,辽宁鞍山114031

出　　处：《冶金自动化》2023年第6期28-36,共9页Metallurgical Industry Automation

摘　　要：钢铁企业转炉煤气柜柜位的准确预测可为煤气系统调度提供重要依据,然而鉴于转炉煤气(Linz Donawitz gas,LDG)回收的冲击性,调度人员对柜位的超上限问题尤为关注。为此,依托大量系统运行数据,提出了一种基于代价敏感学习支持向量机(support vector machine,SVM)的转炉煤气柜柜位预测方法,可提高对柜位超上限情况的预报精度。该方法以LDG回收流量、LDG消耗流量等作为输入,以未来柜位值作为输出,利用KKT方程,将原约束条件转化为等式约束,对煤气柜位超限误报和未超限误报设定了不同的代价;最终通过最小化模型漏报误差,将原预测问题转化为一系列线性方程并求解。针对国内某钢铁厂数据的仿真实验结果表明,所提方法将LDG柜位超限状况的漏报率降低至0.16%,可为调度人员制定合理的调度策略提供更快速有效的指导。The accurate prediction of the Linz Donawitz converter gas(LDG)holder level in iron and steel enterprises can provide an important basis for gas system scheduling.Given the impact of LDG recovery,scheduling workers are particularly concerned about the problem of exceeding the upper limit of the holder level.Based on a large amount of on-site actual data,a cost-sensitive learning-based support vector machine(SVM)method for predicting the level of LDG holder was proposed,which can improve the prediction accuracy of the situation when the level exceeds the upper limit.This method takes the recovery and consumption flow of LDG as inputs,and the future holder level value as output.By using the KKT equation,the original constraint conditions are transformed into equation constraints,different costs are set for the gas holder level exceeding the limit and false alarms.Finally,by minimizing the model's false alarm error,the original prediction problem is trans-formed into a series of linear equations and solved.A simulation experiment was conducted on data from a domestic steel plant,and the results showed that the proposed method can effectively reduce the false alarm rate of converter gas holder level exceeding the limit to 0.16%,providing more rapid and effective guidance for scheduling workers to develop reasonable scheduling strategies.

关 键 词：转炉煤气柜 代价敏感学习 支持向量机回归 柜位预测 

分 类 号：TF34[冶金工程—冶金机械及自动化] TP181[自动化与计算机技术—控制理论与控制工程]