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

Huaiping Jin,Lixian Shi,Bin Qian,Biao Yang,Jiangang Li,Meng Wang,Zheng Li

doi:10.1155/2020/6575326

Huaiping Jin, Lixian Shi + Show 5 more

Open Access

https://doi.org/10.1155/2020/6575326

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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.

Highlights

Rubber mixing is a crucial step in rubber and tire industry. e quality of rubber products highly depends on the exact mixing of raw materials and additives. e Mooney viscosity, indicating the molecular weight and viscoelastic behavior of an elastomer, has been recognized as an important quality index for producing nonvulcanized rubbery materials [1, 2]
By considering the time instants 0 s, 14 s, 18 s, 22 s, . . ., 118 s, a total of 140 delayed and nondelayed variables are obtained as potential input variables and the Mooney viscosity is chosen as the output variable
A new soft sensor method multimodal perturbation- (MP-)EJITGPR is proposed for facilitating accurate estimations of Mooney viscosity in an industrial rubber mixing process. is method enables to enhance the diversity of base just-in-time learning (JIT) learners through the multimodal perturbation mechanism, i.e., perturbing similarity measure and input variables

Summary

Introduction

Rubber mixing is a crucial step in rubber and tire industry. e quality of rubber products highly depends on the exact mixing of raw materials and additives. e Mooney viscosity, indicating the molecular weight and viscoelastic behavior of an elastomer, has been recognized as an important quality index for producing nonvulcanized rubbery materials [1, 2]. (1) A multimodal perturbation mechanism is proposed by utilizing heterogeneous similarity measures and building diverse input subspaces, which allows enhancing the diversity of base JIT learners efficiently (2) e generation of accurate and diverse JIT learners is formulated as a multiobjective optimization problem and solved by an EMO approach (3) e combination of base JIT learners is achieved through the finite mixture mechanism, which enables adaptive assignments of weights (4) A novel EJIT soft sensor modeling framework is built by integrating the multimodal perturbation mechanism-based diversity creation, the EMO-based generation of base JIT learners, and the FMM-based adaptive combination of base JIT learners e rest of the paper proceeds as follows.

Preliminaries

Proposed MP-EJITGPR Soft Sensor

Method

Conclusions