基于广义动态模糊神经网络的聚乙烯分子量分布软测量

分子量分布（MWD）是聚合物的重要质量指标，但由于实时检测的困难，MWD的预测成为聚合过程先进控制和优化面临的重要挑战。为解决聚乙烯分子量分布预测的实时性和精度问题，本文结合过程信息和反应机理建立了分子量分布预测的混合模型。首先通过机理分析，在假设不同催化剂活性位个数的情况下拟合MWD，通过误差分析获得合理的催化剂个数及分布函数参数，同时操作条件与分布函数参数之间的关系通过广义动态模糊神经网络（GDFNN）描述。在GDFNN中，利用K-means初始化其网络结构，训练过程中，充分利用历史数据和新息决定是否增加新规则，减少冗余规则的频繁生成，并通过分级学习机制，前期提高全局学习率，后期提高局部学习率。最后通过实际工业数据的仿真实验证明了该混合模型的有效性。

Soft Sensing of Polyethylene Molecular Weight Distribution Based on Generalized Dynamic Fuzzy Neural Network

Molecular weight distribution (MWD) is an important quality index of polymer. However, it has been a challenging problem for advanced control and optimization of polymerization process to predict MWD, due to the difficulty of real-time detection. In order to solve the problem of real-time and accuracy on the prediction of polyethylene molecular weight distribution, this paper proposes a mixed prediction model on MWD by combining process information and reaction mechanism. First, by the mechanism analysis, MWD is fitted with the number of different active sites. And then, the number of active sites and parameters of the distribution function can be reasonably obtained via the fitting error analysis. Furthermore, the relationship between process parameters and distribution function parameters is described by generalized dynamic fuzzy neural network (GDFNN), in which the K-means clustering algorithm is used to initialize the structure of GDFNN. During the process of training, the historical data and new information will be utilized to determine whether to add new rules for avoiding the frequent generation of redundant rules. Besides, by using hierarchical learning mechanism, both the overall learning rate in the early stage and the local learning rate in the later stage can be effectively improved. Finally, the simulation experiments are made with four operating variables, i.e., rate of ethylene, rate of butane, temperature of reactor, and pressure of reactor, as input variables of the model, from which the effectiveness of the proposed model is verified.