Fast property prediction in an industrial rubber mixing process with local ELM model

2017 ◽  
Vol 134 (41) ◽  
pp. 45391 ◽  
Author(s):  
Weiya Jin ◽  
Yi Liu ◽  
Zengliang Gao
Keyword(s):  
Mixing Process ◽  
Property Prediction ◽  
Industrial Rubber

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

Evaluation of axial fibre strain based on the stimulated four-photon mixing process

1985 ◽  
Vol 21 (20) ◽  
pp. 935
Author(s):  
T. Nakashima ◽  
M. Shibata ◽  
C.A. Ohashi ◽  
S. Seikai
Keyword(s):  
Mixing Process ◽  
Fibre Strain

Enhanced Mechanical Properties of Graphene/Copper Nanocomposites Using a Molecular-Level Mixing Process

2013 ◽  
Vol 25 (46) ◽  
pp. 6724-6729 ◽  
Author(s):  
Jaewon Hwang ◽  
Taeshik Yoon ◽  
Sung Hwan Jin ◽  
Jinsup Lee ◽  
Taek-Soo Kim ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Molecular Level ◽  
Mixing Process ◽  
Copper Nanocomposites

Mixing process of two streams within a steam ejector from the perspectives of mass, momentum and energy transfer

2020 ◽  
pp. 116358
Author(s):  
Yongzhi Tang ◽  
Zhongliang Liu ◽  
Yanxia Li ◽  
Fei Zhao ◽  
Pengyan Fan ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Mixing Process ◽  
Steam Ejector

Enhancing the mixing process of two miscible fluids: A review

2021 ◽  
Author(s):  
Swagatika Acharya ◽  
Vijay Kumar Mishra ◽  
Jitendra Kumar Patel
Keyword(s):  
Mixing Process ◽  
Miscible Fluids
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