水泥篦冷机出口熟料温度自适应辨识模型

Adaptive identification model for export clinker temperature in cement grate cooler

为了优化控制系统,建立篦冷机温度熟料出口的识别模型,利用篦冷机内熟料换热机理,找出熟料冷却过程的关键影响因素;利用回声状态网络辨识篦冷机运行数据,基于递归最小二乘法推导网络的在线学习算法,实现权值自适应调整,从而建立了篦冷机出口熟料温度的自适应辨识模型.仿真实验可知,在系统发生变化时构建的模型能够自适应调整网络输出权值矩阵,使模型快速收敛.与其他离线方法相比,提出的熟料出口温度的自适应模型更加持久有效,可以作为辨识模型指导篦冷机的控制.

In order to optimize the control system and establish the identification model of the clinker outlet of the grate cooler temperature, the key influencing factors of the clinker cooling process are found out according to the heat exchange mechanism of the clinker in the grate cooler. The operating data of grate cooler is trained by echo state network(ESN).Based on the recursive least squares(RLS), the online learning algorithm of the network is derived to realize the adaptive adjustment of the weights in ESN. Then the adaptive model of the clinker temperature in the grate cooler is obtained.Simulation experiments show that when the system changes, the built model can quickly converge by adaptively adjusting the output weight matrix of the network. Compared with other off-line methods, the proposed adaptive model of the clinker temperature is more prolonged and effective. Consequently, it can be used as an identification model to guide the control to the grate cooler so as to improve the efficiency of grate coolers.