作　　者： (李爱莲）; (聂宇航）;

机构地区： 内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处： 《中国科技论文》 2017年第11期1290-1293,共4页

摘　　要： 为准确建立焦炉火道温度模型,保证焦炭质量,节约能耗,提出了基于回声状态网络(echo state network,ESN)模型的焦炉火道温度的预测方法。针对炼焦过程中强耦合、大滞后的特点,以及实际生产中大量炼焦数据未被合理利用的现状,运用数据驱动与非线性建模相结合的方法,首先,对采集的数据进行数据处理,保证了数据的真实有效性;然后,分别建立了焦炉火道温度系统的BP神经网络预测模型和ESN预测模型;最后,在Matlab环境下进行仿真实验,并对2种预测模型的平均相对误差和命中率进行对比。实验表明:ESN模型与BP神经网络模型相比,平均相对误差减小了0.66%,命中率提高了6.39%,说明在结合数据驱动的前提下,ESN模型更能准确预测火道温度,为下一步火道温度的优化控制奠定基础。 To accurately establish the temperature model of coke oven flue,ensure coke quality and save energy consumption,the temperature prediction method of coke oven flue based on echo state network（ESN）was proposed.According to the characteristics of coke oven heating process such as large lag,strong coupling,and the status of large amount of production data have not been reasonably utilized,the method of data-driven combined with nonlinear modeling was employed for the study.Firstly,the collected data were analyzed to ensure the validity of the data.Then,the BP neural network prediction model and ESN prediction model of coke oven flue temperature system were established,respectively.Finally,simulation experiments in Matlab were carried out,and the average relative error and hits rate of two prediction models were compared.Experiment results show that comparing with BP neural network model,the average relative error of ESN model decreases 0.66%,and the hits rate increases6.39%,indicating the ESN prediction model can predict flue temperature precisely combining data-driven,which lays a foundation for further optimization control.

关 键 词： 焦炉 火道温度 数据驱动 回声状态网络 预测