ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process<sup>*</sup>

费正顺; 胡斌; 叶鲁彬; 梁军

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

Updated：2026-01-06

- ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^*
- ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^*
- 中国化学工程学报（英文版） 2012年20卷第5期页码：971-979
- Affiliations：
  
  State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
  
  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)
- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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费正顺, 胡斌, 叶鲁彬, 等. ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^*[J]. 中国化学工程学报（英文版）, 2012,20(5):971-979.

[J]. Chinese Journal of Chemical Engineering, 2012, 20(5): 971-979.
费正顺, 胡斌, 叶鲁彬, 等. ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^*[J]. 中国化学工程学报（英文版）, 2012,20(5):971-979. DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(5): 971-979. DOI：

摘要

Abstract

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.

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