摘要
阳极焙烧是铝工业的重要工序之一,阳极焙烧质量的好坏将直接影响到铝电解生产的电流效率和能耗。因此,如何改进阳极焙烧技术提高阳极质量就成为铝电解工业的重要课题。而加紧对阳极焙烧炉的基础理论及控制算法进行深入系统研究就成为实现这一目的的必然途径。这也是国内具有自主知识产权新型焙烧炉控制系统开发设计的迫切需求。本文主要针对白银铝厂预焙阳极焙烧炉,展开对阳极焙烧温度控制算法的研究。
本文首先详细地叙述了阳极焙烧炉的焙烧机理,简单介绍了阳极焙烧炉的工作原理及结构组成,并对阳极焙烧炉的生产工艺进行了总结。综述了目前国内外阳极焙烧技术发展状况,分析了研究阳极焙烧炉温度控制算法的现实意义。提出了在我国开展阳极焙烧炉温度控制算法研究的综合思路,并以此作为本文的研究思路,展开了相应的研究工作。
针对预焙阳极焙烧炉温度控制系统是一个大时滞、非线性、无精确数学模型的复杂系统,系统存在扰动,且不可测量,炉室间温度存在严重的耦合。本文提出了采用基于径向基函数(RBF)神经网络自适应PID控制策略,该方法是通过神经网络的自学习能力在线调整PID控制器的参数,因而,其兼顾神经网络和传统PID控制的特点,能根据被控对象当前特征迅速地做出相应决策、克服实际控制过程稳态性和准确性之间的矛盾。为了实现预焙阳极焙烧炉的解耦控制,本文采用了神经网络分散解耦控制,利用神经网络的自学习能力、非线性映射能力和容错能力来实现系统的在线解耦。该控制算法结构简单,便于实现并行分布式实时处理,在工程上会使设计问题简化,适合预焙阳极焙烧炉温度控制。
本文在对以上两种算法研究的基础上,将RBF神经网络自适应PID控制器与神经网络分散解耦控制算法结合起来,提出了一种基于RBF神经网络自适应PID的在线解耦控制算法,以适应预焙阳极焙烧炉复杂工况和高指标的控制要求。该算法既避免了当单独采用自适应PID控制算法时,多变量被控对象耦合严重,控制效果不佳的问题;又解决了当单独采用分散解耦算法时,出现多变量被控对象模型参数发生变化,原有的控制器参数不能适应变化后的对象的问题。将其应用于预焙阳极焙烧炉温度过程控制中,实验结果表明,它具有很强的自适应能力和鲁棒性,达到了满意的控制效果。
这些工作对提高阳极焙烧质量和稳定铝产量有着重要意义,与此同时,还将促进智能控制技术的发展及其在工业过程控制中的广泛应用。
Anode baking is one of the important steps in the aluminum industry. The qualities of anode directly affect the electricity efficiency and energy consumption of aluminum electrolysis production. Therefore, how to improve the baking technology in order to improve the quantity of anode has become a very important research issue. The only approach to solve this problem is to do systematic research of the fundamental theory and control algorithm of the anode baking furnace. At the same time, it is an urgent requirement to develop and to design new baking furnaces control system independently in China. The research of temperature control algorithm is based on the anode baking furnace of Baiyin Aluminum Ltd in this thesis.
Firstly, the thesis depicts baking theory of anode baking furnace in detail, introduces the work principle and structure constitute, and summarizes the production craft of anode baking furnace. It also reviews the domestic and international anode baking technique development condition at present, and analyzes the realistic meaning of the anode baking furnace control algorithm. The thesis proposes a synthetic research method to further the temperature control algorithm of anode baking furnace in China, Based on this idea, the relevant research work follows.
An adaptive PID control strategy based on Radial Basis Function (RBF) neural network(NN) is proposed in this thesis for the temperature control system of anode pre-baking furnace, which is a complex system with the characteristics of time-delay, nonlinear and no precise mathematical model, it has immeasurable disturbance and strong temperature coupling between baking furnace room. The parameters of PID controller are tuned on-line using the self-learning ability of RBFNN. So the proposed control strategy has the advantages of neural network and conventional PID control, and the ability of making correspondingly the decisions quickly according to the current characteristic of the object and overcoming the inconsistency of the steady and veracity. A distributed decoupling control of neural network is adopted for decoupling control of anode pre-baking furnace, and the corresponding decoupling control law is achieved by the self-learning, nonlinear mapping and fault-tolerant ability of neural network. The structure of control algorithm is simple, and easy to achieve parallel distributed real-time processing, it will simplify the design problem in project. So it is suitable for the anode pre-baking furnace temperature control.
Based on the two algorithms above, an adaptive PID online decoupling control algorithm based on Radial Basis Function (RBF) neural network(NN) is proposed in this thesis, which is designed to meet the high targets of control requirements and complex conditions of anode pre-baking furnace. The algorithm not only avoids the problem of poor quality control with multivariable strong coupling plant when using parameters adaptive PID control algorithm individually. But also solves the problem of original parameters of the controller can not adapt to changes in the controlled plant. It was applied to the temperature system of the anode pre-baking furnace, the results show that the proposed controller has the adaptability, strong robustness and satisfactory control performance.
It is proved to be a useful research method in the design and process optimization of the baking furnace of aluminum industry. At the same time, it will promote the development of intelligent control technology and its extensive application in industrial process control.
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