基于改进PSO-KELM的高炉回旋区温度预测研究  

Prediction of the raceway temperature based on improved PSO-KELM

作　　者：崔桂梅[1] 张运强 张勇[1] CUI Guimei;ZHANG Yunqiang;ZHANG Yong(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《中国测试》2020年第4期25-30,共6页China Measurement & Test

基　　金：国家自然科学基金项目(61763039)。

摘　　要：高炉平稳运行时,喷煤量常根据回旋区温度的波动随时进行调整。相较于人工推断回旋区温度,建立回旋区温度预测模型更能及时准确地对喷煤量进行优化。针对常规PSO-KELM算法对回旋区温度预测命中率较低的问题,提出改进的PSO-KELM算法对其进行建模预测。首先,采用混沌机制调整惯性权重,并线性改变学习因子,以平衡粒子群的全局与局部搜索能力。其次,针对粒子群算法易陷入局部最优的缺点,引入遗传算法思想,将种群粒子交叉、变异,以提高种群多样性。最后,基于所提方法用某高炉运行数据建立回旋区温度预测模型,并与用常规PSOKELM算法、BP神经网络、极限学习机建立的模型作对比。仿真结果表明,用所提算法建立的模型预测回旋区温度具有最高命中率和最低均方误差。When the blast furnace runs in a stable way,the coal injection quantity is frequently adjusted on the basis of temperature fluctuation of raceway in blast furnace.Compared with deducing artificially,the coal injection can be optimized timely and precisely by establishing a raceway temperature prediction model.To improve the hitting accuracy of the traditional PSO-KELM algorithm,an improved PSO-KELM algorithm was proposed.In the first place,chaotic mechanism was adopted to adjust inertia weight and alter learning factors linearly in order that the global and local searching ability can be balanced.In addition,the genetic algorithm,which can solve the problem of local optimum in PSO was introduced to promote the diversity of population by crossing and mutating population particles.Finally,compared with traditional PSO-KELM algorithm,BP neural network and extreme learning machine,the simulation results demonstrated that the raceway temperature prediction model,which was established based upon the new method and operating data of a blast furnace,was able to predict raceway temperature with the maximum hitting accuracy and the minimum mean square error.

关 键 词：回旋区温度预测 核极限学习机 粒子群优化 高炉 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]