ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process  

基于ARX-NNPLS模型的优化策略及在聚合物牌号切换过程中的应用(英文)

作　　者：费正顺 胡斌 叶鲁彬 梁军 

机构地区：[1]State Key Lab of Industrial Control Technology,Institute of Industrial Control Technology,Zhejiang University

出　　处：《Chinese Journal of Chemical Engineering》2012年第5期971-979,共9页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (61174114);the National High Technology Research and Development Program of China (2007AA04Z168, 2009AA04Z154);the Research Fund for the Doctoral Program of Higher Education in China (20050335018)

摘　　要：Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.Since it is often difficult to build differential algebraic equations （DAEs） for chemical processes, a new data-based modeling approach is proposed using ARX （AutoRegressive with eXogenous inputs） combined with neural network under partial least squares framework （ARX-NNPLS）, in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares （PLS） inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.

关 键 词：partial least squares ARX-NN structure dynamic optimization grade transition POLYMERIZATION 

分 类 号：O631.3[理学—高分子化学]