基于Bootstrap的高炉铁水硅含量二维预报  

Two-dimensional Prediction for Silicon Content of Hot Metal of Blast Furnace Based on Bootstrap

作　　者：蒋朝辉[1] 董梦林 桂卫华[1] 阳春华[1] 谢永芳[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《自动化学报》2016年第5期715-723,共9页Acta Automatica Sinica

基　　金：国家自然科学基金重大项目(61290325);国家自然科学基金创新研究群体科学基金(61321003);中南大学中央高校基本科研业务费专项资金(2013zzts226)资助~~

摘　　要：高炉铁水硅含量的实时准确预报对调控高炉炉温和稳定炉况具有重要作用,但其预报结果一直存在准确度不高和缺乏可信度表征等问题,特别是在炉况不稳、运行数据波动较大时,预报结果的准确度和可信度急速下降,不利于现场操作人员根据预报结果进行生产操作.为此本文融合神经网络和Bootstrap预报区间方法,构建高炉铁水硅含量的二维预报模型,实现在预报硅含量值的同时给出了该预测值的可信度.应用实例表明,本文提出的方法提高了硅含量点预测结果的准确度,且预测区间宽度能正确地表征点预测结果的可信度,对实际生产操作具有较好的指导意义.Accurate real-time forecasting of silicon content in hot metal of blast furnace plays a significant role in furnace temperature regulation, but the prediction results have a low hit rate and lack any indication of accuracy. Especially when furnace conditions are unstable and the data fluctuate frequently, the hit rate and the reliability decrease so sharply that workers cannot use the prediction results for manufacturing operation. Therefore, a two-dimensional prediction model of silicon content in hot metal based on the integration of Bootstrap method and neural network is constructed to predict the silicon content. Meanwhile, the reliability of point prediction is also provided. Application results show that not only does the model improve the prediction accuracy of silicon content in point shooting, but also the prediction interval width correctly characterizes the reliability of point prediction. The proposed method will be beneficial to the practical production process.

关 键 词：高炉 BOOTSTRAP 二维预报 预测区间 可信度 

分 类 号：TF53[冶金工程—钢铁冶金]