转炉煤气回收蒸发冷却器控制策略研究

发布时间：2019-04-01 10:38

【摘要】：转炉烟气干法除尘技术由于具有粉尘排放低、煤气回收效率高等优点。因而得到了日益广泛的应用。但由于除尘系统蒸发冷却器控制对象具有非线性、大时滞、强耦合等特点,使得常规PID控制难以有效应用。因此,开展此项课题的研究具有重要的工程价值。论文首先介绍了转炉干法除尘技术的研究和发展现状,在分析系统工艺流程的基础上,总结了蒸发冷却器的对象特性,研究了烟气温度控制过程的参数关系,归纳了控制系统的技术指标。提出了基于支持向量回归机的非线性模型预测控制方法,并利用简化粒子群算法对其参数进行寻优求解。具体工作内容如下:1)在分析基本粒子群算法(Particle Swarm Optimization,PSO)易陷入局部最优以及寻优精度低的基础上,采用小步长线性递减惯性权重和增加极值扰动算子方式,改进简化粒子群算法,改善粒子群算法寻优速度和精度,提高算法的求解效率。2)针对非线性系统的建模问题,考虑不同算法的非线性拟合能力,引入支持向量回归算法,利用简化的粒子群算法对其参数进行寻优求解,构建sPSO-SVR模型预测控制器,并将单步和多步模型预测控制算法进行仿真分析比较,结果表明基于sPSO-SVR的多步模型预测控制器具有良好的控制性能,可有效应用于非线性系统的预测控制中。3)针对蒸发冷却器对象具有的时变性和参数模型不确定性等特点,提出了基于sPSO-SVR的多步模型预测控制策略的优化控制方案,利用其算法的滚动优化、反馈校正等功能,展开蒸发冷却器出口温度控制研究,在此基础上,通过Matlab仿真来分析和验证该控制方法的有效性,并达到了理想的控制效果。4)最后课题以宝钢梅山钢铁公司第二炼钢厂250t转炉干法除尘系统为对象,采用西门子自动化产品设计蒸发冷却器控制系统的DCS控制方案,并进行了部分硬件的配置和选型、PLC系统的硬件组态以及Step7和WinCC软件程序的开发。
[Abstract]:The dry dust removal technology of converter flue gas has the advantages of low dust emission and high gas recovery efficiency. As a result, it has been widely used. However, due to the characteristics of nonlinear, large time delay and strong coupling of evaporative cooler in dust removal system, conventional PID control is difficult to be applied effectively. Therefore, it has important engineering value to carry out the research of this subject. Firstly, this paper introduces the research and development status of converter dry dedusting technology. On the basis of analyzing the technological process of the system, the object characteristics of evaporative cooler are summarized, and the parameter relationship of flue gas temperature control process is studied. The technical indexes of the control system are summarized. A nonlinear model predictive control (MPC) method based on support vector regression (SVR) is proposed, and its parameters are optimized by using simplified particle swarm optimization (PSO) algorithm. The main contents are as follows: 1) on the basis of the analysis of (Particle Swarm Optimization,PSO (basic Particle Swarm Optimization) which is easy to fall into local optimization and low precision of optimization, small step linear decreasing inertia weight and increasing extreme value perturbation operator are adopted. Improve the simplified particle swarm optimization algorithm, improve the speed and accuracy of particle swarm optimization algorithm, and improve the efficiency of the algorithm. 2) considering the nonlinear fitting ability of different algorithms, support vector regression algorithm is introduced to solve the modeling problem of nonlinear system. The simplified particle swarm optimization algorithm is used to optimize its parameters, and the sPSO-SVR model predictive controller is constructed, and the single-step and multi-step model predictive control algorithms are simulated and compared. The results show that the multi-step model predictive controller based on sPSO-SVR has good control performance and can be effectively applied to the predictive control of nonlinear systems. 3) for the evaporative cooler plant it has the characteristics of time-varying and parameter model uncertainty. The optimal control scheme of multi-step model predictive control strategy based on sPSO-SVR is proposed. Based on the functions of rolling optimization and feedback correction of the algorithm, the study of evaporative cooler outlet temperature control is carried out. The validity of the control method is analyzed and verified by Matlab simulation, and the ideal control effect is achieved. 4) finally, the paper takes the 250 t converter dry dust removal system of Baosteel Meishan Iron and Steel Company as the object. The DCS control scheme of evaporative cooler control system is designed by using Siemens automation products. Some hardware configuration and selection, hardware configuration of PLC system and the development of Step7 and WinCC software programs are carried out.
【学位授予单位】：安徽工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TF345

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