Melt index prediction with a mixture of Gaussian process regression with embedded clustering and variable selections-Reference-Cited by-同舟云学术

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Melt index prediction with a mixture of Gaussian process regression with embedded clustering and variable selections

Published:2017-06-28 Issue:40 Volume:134 Page:45237
ISSN:0021-8995
Container-title:Journal of Applied Polymer Science
language:en
Short-container-title:J. Appl. Polym. Sci.

Author:

Chan Lester Lik Teck¹,Chen Junghui¹

Affiliation:

1. Department of Chemical Engineering; Chung-Yuan Christian University; Chung-Li, Taoyuan Taiwan 32023 Republic of China

Funder

Ministry of Science and Technology of the Republic of China

Publisher

Wiley

Subject

Materials Chemistry,Polymers and Plastics,Surfaces, Coatings and Films,General Chemistry

Reference44 articles.

1. Challenges in particulate polymerization reactor modeling and optimization: A population balance perspective

2. Quality control of polymer production processes

3. Measurement and control of polymerization reactors

4. Inferential sensors for estimation of polymer quality parameters: Industrial application of a PLS-based soft sensor for a LDPE plant

5. A soft sensor based on adaptive fuzzy neural network and support vector regression for industrial melt index prediction

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1. Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data;Polymers;2022-08-26

2. Hybrid modeling approach for polymer melt index prediction;Journal of Applied Polymer Science;2022-08-15

3. Soft Sensor for Melt Index Prediction Based on Long Short-Term Memory Network;IFAC-PapersOnLine;2022

4. Soft Sensor Development Based on Quality-Relevant Slow Feature Analysis and Bayesian Regression with Application to Propylene Polymerization;Journal of Sensors;2021-12-30

5. Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor;IEEE Transactions on Cybernetics;2021-07

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