基于PSO-BP神经网络的回转窑喷煤量预测  

Prediction of coal injection rate in rotary kiln based on PSO-BP neural network

作　　者：阳诚平 刘奇 刘曙 劳逸 汤启宙 文爽 周德军 赵士林 YANG Chengping;LIU Qi;LIU Shu;LAO Yi;TANG Qizhou;WEN Shuang;ZHOU Dejun;ZHAO Shilin(Chengchao Mining Company Limited,WISCO Resources Group,Ezhou 436051,Hubei,China;School of Energy Science and Engineering,Central South University,Changsha 410083,Hunan,China)

机构地区：[1]武钢资源集团程潮矿业有限公司,湖北鄂州436051 [2]中南大学能源科学与工程学院,湖南长沙410083

出　　处：《烧结球团》2024年第5期47-55,102,共10页Sintering and Pelletizing

基　　金：长沙市“揭榜挂帅”重大科技项目(kq2301007)。

摘　　要：喷煤量调节是回转窑内各段温度控制的主要途径和手段,但由于回转窑内球团的煅烧过程具有非线性、时滞性、多变量和不确定性等特点,该部分温度难以有效控制。本文构建一种基于粒子群算法优化BP神经网络(PSO-BP)的回转窑喷煤量预测模型,通过仿真试验对比喷煤量的预测值与实际值,分析误差和辨识模型的拟合效果。结果表明:PSO-BP神经网络预测模型的精度优于原始BP神经网络,具有较高的寻优效率,总体拟合效果较好,其线性拟合度约为0.95,绝对误差约为0.09 t/h,相对误差约为2.4%。基于PSO-BP神经网络模型预测预热段的温度平均相对误差为0.8%,总体平均温度波动在±10℃以内,说明构建的回转窑喷煤量预测模型准确可靠且适用性强,将建立的神经网络作为前馈控制模块与传统的PID控制相结合构成预热段温度自动控制系统,可实现回转窑温度场稳定控制。Coal injection rate adjustment is the main way and means to control the temperature of each section in the rotary kiln,but it is difficult to effectively control the temperature of each section due to such characteristics as nonlinearity,time delay,multivariate and uncertainty of the calcination process of pellets in the rotary kiln.A prediction model of coal injection rate in rotary kiln based on particle swarm optimization BP neural network(PSO-BP) is constructed,and the predicted value and actual value of coal injection rate are compared through simulation experiments,and the error and fitting effect of the identification model are analyzed.The results show that the precision of the PSO-BP neural network prediction model is better than that of the original BP neural network,with high optimization efficiency and good overall fitting effect,with a linear fitting degree of about 0.95,an absolute error of about 0.09 t/h,and a relative error of about 2.4%.Based on the PSO-BP neural network model,the average relative error of the temperature prediction section is 0.8%,and the overall average temperature fluctuation is within ±10 ℃.It shows that the constructed prediction model of coal injection rate in rotary kiln is accurate,reliable and applicable,and the established neural network is combined with the traditional PID control to form an automatic temperature control system for the preheating section,which can realize the stable control of the temperature field of the rotary kiln.

关 键 词：回转窑 球团 粒子群算法 BP神经网络 喷煤量 温度控制 

分 类 号：TF046.6[冶金工程—冶金物理化学] TP183[自动化与计算机技术—控制理论与控制工程]