控制理论与应用2016，Vol.33Issue(6)：727-734,8.DOI:10.7641/CTA.2016.50857
高炉炼铁过程多元铁水质量指标多输出支持向量回归建模
Multi-output support vector regression modeling for multivariate molten iron quality indices in blast furnace ironmaking process
摘要
Abstract
Molten iron temperature as well as Si, P, and S contents are the most essential molten iron quality (MIQ) indices in the blast furnace (BF) ironmaking, while difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. Focusing on this practical challenge, a data-driven multi-output support vector regression (M–SVR) dynamic model is established to estimate the MIQ indices online, with the help of the pro-posed comprehensive evaluation on modeling accuracy and genetic optimization on model parameters. Different from the conventional single output SVR, the M–SVR can calculate multiple classification by one training process, so as to realize multi-output regression modeling of multivariate MIQ indices. With the proposed comprehensive evaluation index on mod-eling accuracy, the modeling performance can be evaluated from the aspects of model estimation trend as well as estimation error. By taking this comprehensive evaluation index as the fitness function, the genetic algorithm (GA) is to find the opti-mal values of the telescopic vector and the penalty factor for the M–SVR model, so that the GA–M–SVR dynamic model with optimal parameters can be obtained. Finally, industrial experiments have been carried out on the 2# blast furnace in an Iron&Steel Group Co. of China, where it has been demonstrated that the GA–M–SVR model produces satisfied modeling and estimating accuracy.关键词
高炉炼铁/铁水质量/多输出支持向量回归(M-SVR)/模型精度综合评价/遗传优化/数据驱动建模Key words
blast furnace ironmaking/molten iron quality (MIQ)/multi-output support vector regression (M-SVR)/comprehensive evaluation of modeling accuracy/genetic optimization/data-driven modeling分类
信息技术与安全科学引用本文复制引用
周平,李瑞峰,郭东伟,王宏,柴天佑..高炉炼铁过程多元铁水质量指标多输出支持向量回归建模[J].控制理论与应用,2016,33(6):727-734,8.基金项目
国家自然科学基金项目(61473064,61290323,61333007),中央高校基本科研业务费项目(N130108001),国家“863”计划项目(2015AA043802),辽宁省教育厅科技项目(L20150186)资助 （61473064,61290323,61333007）
