水泥熟料篦冷机热量转换控制系统研究

发布时间：2018-03-19 16:28

本文选题：篦冷机　切入点：自适应粒子群算法　出处：《长春工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：篦冷机是新型干法水泥生产过程中的重要设备,在整个水泥熟料的生产过程中承担对高温熟料的冷却任务的同时,还有改善水泥熟料的易磨性、回收熟料热量等作用。目前,已经有很多预测和控制理论在篦冷机环节进行了仿真与应用,例如BP神经网络、支持向量机、最小二乘支持向量机和各种形式的PID等。这些预测和控制理论需要设定一些参数,设定的参数决定了在篦冷机环节的预测和控制效果。本研究的主要工作:1.深入分析一下新型干法水泥生产的流程,对篦冷机的发展和工作原理有一个深入的认识。篦冷机篦压的控制方式有两种,最为常用的是通过篦冷机的篦床速度控制篦下压力。制定篦冷机的篦压的控制目标,根据控制目标建立篦冷机热量转换控制系统的方案,即篦下压力设定系统和篦下压力控制系统。2.为了建立篦下压力设定系统,首先对粒子群算法和LS-SVM算法进行了深入研究,针对粒子群算法的缺点,提出一种新的粒子群算法(自适应粒子群算法),通过仿真实验证明自适应粒子群算法的优越性。篦下压力设定系统是基于自适应PSO的LS-SVM算法建立的,基于自适应粒子群的LS-SVM算法实质就是自适应粒子群算法对LS-SVM的两个参数进行优化,得到最佳的参数,并建立预测模型,预测模型的输入端是提升机的电流、三次风温、二次风温,输出端是篦下压力,并通过仿真对比实验证明本文建立的压力预测模型的实用性。3.为了建立篦冷机篦下压力控制系统,首先分析篦冷机中的比例阀原理,其次查阅文献得出篦冷机的电液伺服控制系统的数学模型,本文采用的篦下压力控制方法是基于改进PSO(自适应粒子群算法)的PID控制方法,其方法的实质就是用自适应粒子群算法对传统PID控制方法进行参数整定优化,得到最佳的优化参数,并通过仿真实验证明本文提出的方法比传统的方法控制效果更好,即调速过程更平稳和调节时间短。4.设计了篦冷机三级监控系统,即现场级、控制级、企业管理级。
[Abstract]:Grate cooler is an important equipment in the process of production of new type dry cement. It undertakes the task of cooling high temperature clinker in the whole production process of cement clinker, and at the same time, it also improves the wearability of cement clinker and reclaims the heat of clinker, etc. At present, Many prediction and control theories have been simulated and applied in grate cooler, such as BP neural network, support vector machine, least square support vector machine and various forms of PID. The parameters determined the prediction and control effect in grate cooler. The main work of this study is: 1. Deeply analyze the process of new dry cement production. There are two ways to control the grate pressure of the grate cooler, the most commonly used is to control the down pressure of the grate through the grate cooler's velocity, and to establish the control target of the grate pressure of the grate cooler. According to the control goal, the scheme of establishing the heat transfer control system of grate cooler, I. E. the system of setting down pressure of grate and the control system of pressure under grate, is established. In order to set up the system of setting down pressure of grate, the particle swarm optimization algorithm and LS-SVM algorithm are studied deeply. In view of the shortcomings of particle swarm optimization (PSO), a new PSO (Adaptive PSO) algorithm is proposed. The simulation results show the superiority of APSO. The pressure setting system of grate is based on the LS-SVM algorithm of adaptive PSO. The essence of the LS-SVM algorithm based on adaptive particle swarm optimization is to optimize the two parameters of LS-SVM, to obtain the best parameters, and to establish a prediction model. The input of the prediction model is the hoist current, the third air temperature. The secondary air temperature and the output end are grate down pressure. The simulation results show that the pressure prediction model established in this paper is practical. 3. In order to establish the pressure control system of grate cooler, the principle of proportional valve in grate cooler is analyzed. Secondly, the mathematical model of electro-hydraulic servo control system of grate cooler is obtained by referring to the literature. The method used in this paper is PID control method based on improved PSO (Adaptive Particle Swarm Optimization algorithm). The essence of the method is to optimize the parameters of the traditional PID control method by using the adaptive particle swarm optimization algorithm. The simulation results show that the proposed method is more effective than the traditional control method. That is, the speed regulation process is more stable and the adjustment time is short. 4. The three-stage monitoring system of grate cooler is designed, that is, the field level, the control level and the enterprise management level.
【学位授予单位】：长春工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TQ172.622.4;TP273

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