【作者】 王婷；

【导师】 常玉清；

【作者基本信息】 东北大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 利用连续搅拌反应釜(Continuous Stirred Tank Reactor, CSTR)来进行聚合化学反应是目前广泛采用的一种生产方式,该过程具有复杂工业生产过程的典型特性,如：非线性、大惯性、时变性等等。反应物浓度是该过程中一个关键的化学参数,但是由于经济或技术的原因,对于该参数很难实现在线测量,通常只能通过采用人工采样离线化验分析的方法获得其采样值,这样的测量信息存在较大的测量滞后,这就给生产过程的在线监测以及质量控制带来了很大的困难。针对上述问题,本文对反应物浓度的软测量方法展开深入的研究。软测量技术的核心思想是：通过建立无法在线测量关键过程变量和与其密切相关的过程可测信息之间的数学模型,实现对不可测变量的预估。本文以连续搅拌反应釜中的聚合化学反应为背景,从软测量技术的角度,对其反应过程中重要参数(即反应物浓度)的软测量问题展开了较为深入研究。本文主要研究内容包括：根据多模型理论,提出了一种基于多神经网络的连续搅拌反应釜反应物浓度的软测量建模方法。将输入信息进行分类得到一系列的子模型,然后再对这些子模型进行融合,形成最终的多模型结构。多模型建模方法在一定程度上提高了模型的精度与泛化能力。将机理建模与智能建模相结合,提出了混合建模方法用以实现CSTR内反应物浓度的软测量。用串行和并行两种方法分别对CSTR内反应物浓度进行混合建模,这种混合建模方法降低了模型对训练数据的依赖程度,并提高了模型的泛化能力。最后针对软测量模型的在线校正问题做了简单的讨论。更多还原

【Abstract】 Continuous Stirred Tank Reactor is a production method of polymeric chemical reaction, which is adopted widely in industry now. There are many typical characteristics of this method just like in the complicated manufacturing process, such as nonlinearity, great inertia, time-variant and so on. The concentration of the reactant is a key chemical parameter in this process, but it is very difficult to be measured on-line because of poor economy or technology. In general, we use the manual sampling analyses method to obtain the sampled value of this parameter. This method would cause much delay in result information, which brings great difficulty in the online supervision and quality control in the manufacturing process. Based on the problems discussed above, this paper focuses on Soft Sensing method of the concentration of reactant.The main idea of Soft-Sensing technology is described as following:through building mathematical modeling between immeasurable key process variables and their related measurable process information, the immeasurable process variables could be predicted. Based on the background of the polymeric chemical reaction in Continuous Stirred Tank Reactor, this paper focuses on the Soft Sensing of the concentration of reactant.This paper mainly includes the following aspects.Firstly, a concentration soft-sensing model is developed based on a multi-neural network. At the beginning, this input information is classified to get a series of sub-models. Then these sub-models are inner connected on a multiple model structure. This multiple modeling method could improve the precision and generalization performance of the model to some extent.Secondly, a hybrid modeling method is presented by combining mechanism modeling and intelligence modeling to estimate the parameters of the concentration of reactant in Continuous Stirred Tank Reactor. It builds the hybrid models of the concentration of reactant in Continuous Stirred Tank Reactor using serial and parallel methods. This hybrid modeling method causes the result that this model reduces the dependence degree of the training data and improves the generalization performance. Finally, the problems of online adjustment of Soft Sensing model are discussed.更多还原