基于神经网络的回转窑建模及其优化控制设计

实现水泥回转窑温度稳定性控制,水泥回转窑熟料煅烧是一个涉及传质、传热和物理化学反应的复杂多变量、多扰动非线性过程。为了稳定回转窑烧成温度以提高孰料烧成质量,降低能耗,传统的控制方法,存在干扰大,稳定时间长等问题。在分析水泥回转窑工艺的基础上,采用Elman神经网络建立回转窑系统的模型,提出BP神经网络的PID控制方法,根据系统的运行状态,调节PID控制器的参数,以达到性能指标,设计了回转窑温度优化控制器,具有超调量小、动态性好、收敛速度快和控制精度高等优点。进行仿真的结果表明,回转窑烧成带温度逐渐趋于稳定,实现了对水泥回转窑的优化控制。

Model of Rotary Kiln Based on Neural Network and Design of Optimization Control

Calcination process of cement clinker is a complex multi-variable large-disturbances and nonlinear system which is full of mass transfer,heat transfer,physical and chemical reactions.In order to reduce energy consumption and ensure the quality of cement clinker burning,it is necessary to explore methods superior to the traditional PID to stabilize the temperature of rotary kiln.PID control methods based on BP nerual network can adjust the control parameters of PID according to the operational status of the system,and achieve a performance optimization.It has many advantages such as small overshoot,good dynamics,fast convergence rate,high controlled resolution and so on.In this paper,rotary kiln model was established by Elman neural network,and an optimize controller was designed with the PID control methods based on BP nerual network.The results show that,after the fluctuations in the early control period,the temperature of cement rotary kiln tends to be stabilized and realize the simulation control of cement rotary kiln.