主成分分析结合极限学习机的高炉炉温预测模型  

Prediction model of blast furnace temperature with principal component analysis combined with extreme learning machine

作　　者：袁冬芳 曹富军 李德荣[1] 

机构地区：[1]内蒙古科技大学理学院,内蒙古包头014010

出　　处：《内蒙古科技大学学报》2017年第4期327-332,共6页Journal of Inner Mongolia University of Science and Technology

基　　金：国家自然科学基金资助项目(61663035);内蒙古自然科学基金资助项目(2017MS(LH)0104;2017MS(LH)0105)

摘　　要：炉温控制是高炉过程控制的基础与核心技术,炉温的走势最直接地反应了高炉的运行状况.因此,建立合理的炉温控制模型至关重要.铁水中硅的含量与炉温成正比例关系,而冶炼过程中状态变量、控制变量及入炉基本条件等都会影响炉温,如果考虑全部的相关因素,势必会因信息冗余降低模型的性能.为此,首先采用主成分分析(PCA)方法对多维输入变量进行降维,同时回避了变量间的多重共线性问题.其次,将PCA处理得到的相互独立的主成分用于网络训练,建立了基于极限学习机(ELM)的炉温预测模型,该模型克服了前馈神经网络训练速度慢、容易陷入局部极小的缺点.最后比较了传统的BP学习算法、ELM算法和PCA结合ELM算法的预测效率,试验证明本文算法具有较高的命中率,可以用来指导高炉实际生产.Furnace temperature control is the basis and core technology of blast furnace process control,and the trend of the furnace temperature is the most direct response to the operation of the high furnacc. Therefore, it is very important to establish a reasonable fur-nace temperature control model. The content of silicon in molten iron is proportional to the furnace temperature, while the state varia-bles, control variables and the basic conditions in the process of smelting will afect the furnace temperature. If all the relevant factors are considered , it is bound to reduce the performance of the model due to information redundancy. For this reason , firstly , principal component analysis （PCA） method was applied to reduce the dimension of the multidimensional input variables to avoid the multiplecollinearity between variables simultaneously. Secondly , the independent principal components of PCA processing were used for net-work training , and a furnace temperature prediction model based on extreme learning machine （ELM） was established. The model over-comes the shortcomings of slow training speed and easy falling into the local minimum of the feedfor＇ward neural network. Finally,the prediction efficiency of the traditional BP algorithm , ELM algorithm and PCA combined with the ELM algorithm were compared. Experiments show that this algorithm has a high hit,rate, which can be used to guide the actual production of blast furnace.

关 键 词：硅含量 炉温控制 主成分分析 极限学习机 

分 类 号：TF543[冶金工程—钢铁冶金] TP273.4[自动化与计算机技术—检测技术与自动化装置]