Soft sensor development for Mooney viscosity prediction in rubber mixing process based on GMMDJITGPR algorithm

2017 ◽  
Author(s):  
Kai Yang ◽  
Xiangguang Chen ◽  
Li Wang ◽  
Huaiping Jin
Keyword(s):  
Soft Sensor ◽  
Mixing Process ◽  
Sensor Development ◽  
Viscosity Prediction ◽  
Mooney Viscosity

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

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huaiping Jin ◽  
Jiangang Li ◽  
Meng Wang ◽  
Bin Qian ◽  
Biao Yang ◽  
...  
Keyword(s):  
Gaussian Process Regression ◽  
Soft Sensor ◽  
Viscosity Measurement ◽  
Finite Mixture ◽  
Just In Time ◽  
Mixing Process ◽  
Industrial Rubber ◽  
Viscosity Prediction ◽  
Mooney Viscosity ◽  
Input Variables

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.

Ensemble Correntropy-Based Mooney Viscosity Prediction Model for an Industrial Rubber Mixing Process

2016 ◽  
Vol 39 (10) ◽  
pp. 1804-1812 ◽  
Author(s):  
Yi Liu ◽  
Yu Fan ◽  
Lichun Zhou ◽  
Fujiang Jin ◽  
Zengliang Gao
Keyword(s):  
Prediction Model ◽  
Mixing Process ◽  
Industrial Rubber ◽  
Viscosity Prediction ◽  
Mooney Viscosity

Soft-sensor development with adaptive variable selection using nonnegative garrote

2013 ◽  
Vol 21 (9) ◽  
pp. 1157-1164 ◽  
Author(s):  
Jian-Guo Wang ◽  
Shi-Shang Jang ◽  
David Shan-Hill Wong ◽  
Shyan-Shu Shieh ◽  
Chan-Wei Wu
Keyword(s):  
Variable Selection ◽  
Soft Sensor ◽  
Sensor Development ◽  
Nonnegative Garrote

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

2020 ◽  
Vol 99 (1) ◽  
pp. 334-344 ◽  
Author(s):  
Jianlin Wang ◽  
Kepeng Qiu ◽  
Yongqi Guo ◽  
Rutong Wang ◽  
Xinjie Zhou
Keyword(s):  
Soft Sensor ◽  
Batch Processes ◽  
Just In Time ◽  
Sensor Development
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