Fast and reliable computational strategy for developing a rigorous model-driven soft sensor of dynamic molecular weight distribution

被引：12

作者：

Kang, Jiayuan ^{[1
]}

Shao, Zhijiang ^{[1
]}

Chen, Xi ^{[1
]}

Gu, Xueping ^{[2
]}

Feng, Lianfang ^{[2
]}

机构：

[1] Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

[2] Zhejiang Univ, Coll Chem & Biol Engn, State Key Lab Chem Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

JOURNAL OF PROCESS CONTROL | 2017年 / 56卷

基金：

中国国家自然科学基金;

关键词：

Soft sensor; Grade transition; Molecular weight distribution; Finite elements; Collocation method; POLYMERIZATION PROCESS; PREDICTIVE CONTROL; INDUSTRIAL; QUALITY; TRANSITION; EQUATION; STATE; PLS;

D O I：

10.1016/j.jprocont.2017.05.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Key polymer properties are substantially directly related to the polymer molecular weight distribution (MWD). On-line monitoring and prediction of dynamic MWD profiles are highly important for on-line quality control of polymerization processes. In this study, a fast and reliable computational strategy for an equation-oriented model-based soft sensor for the high-density polyethylene grade transition process is developed. The simultaneous collocation approach is adopted to discretize the dynamic model. A novel moving finite element method is proposed to improve the on-line performance of the derived large-scale nonlinear equation systems. The sensitivity information of the nonlinear equation systems contributes to a convergence enhancement strategy for the sensor. The prediction accuracy and computational efficiency are demonstrated using industrial data.A potential application to extend the polymerization process with changeable flowsheet is also tested through simulation. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：79 / 99

页数：21