Neural Net based Quality Modeling and Control Systems for Polymerization Processes

在这项研究中，提出了一个在线推理计划，以预测聚合物的性能，熔体指数，离线，过去的测量值，此属性和可用的在线测量过程变量。在在线推理方案中，L无穷波网络，这是一个人工神经网络的基函数从家庭的小波，是利用结合化学工程的基本原理。多步预测器，制定在一个级联结构的相同的一步前波网络为基础的预测器，是自适应学习的聚合物性能和工艺变量之间的函数关系。该方案的应用说明了使用工业高密度聚乙烯工艺的操作数据。所提出的方案的结果表明，基于Wave-Net的推理方案可以成功预测MI，并证明其作为在线监测方案的潜力，以将聚合物质量保持在最高水平。

In this study, an on-line inferential scheme is proposed in order to predict a polymer property, the Melt Index, form off-line, past measured values of this property and available on-line measured process variables. In the on-line inferential scheme, the L infinity wave-net, which is an artificial nenural network with basis functions drawn from the family of wavelets, is utilized by incorporating fundamentals of chemical engineering. The multistep predictor, formulated in a cascaded structure of identical one-step-ahead Wave-Net based predictors, is adaptively learning the functional relationship between the polymer property and process variables. The application of the scheme is illustrated using operating data for an industrial High Density Polyethylene Process. The results of the proposed scheme show that the inferential scheme built upon the Wave-Net can successfully predict the MI and prove its potential as an on-line monitoring scheme to maintain the polymer quality at the highest level.