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Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Authors :
Huaiping Jin
Jiangang Li
Meng Wang
Bin Qian
Biao Yang
Zheng Li
Lixian Shi
Source :
Advances in Polymer Technology, Vol 2020 (2020)
Publication Year :
2020
Publisher :
Hindawi-Wiley, 2020.

Abstract

The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

Details

Language :
English
ISSN :
07306679 and 10982329
Volume :
2020
Database :
Directory of Open Access Journals
Journal :
Advances in Polymer Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.6f01dafdb3a44f75ad12d28c5d1f35ef
Document Type :
article
Full Text :
https://doi.org/10.1155/2020/6575326