Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression-Reference-Cited by-同舟云学术

Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression

Published:2019-08-07 Issue:36 Volume:58 Page:16676-16686
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Liu Hongbin¹²^ORCID,Yang Chong¹,Carlsson Bengt³,Qin S. Joe²^ORCID,Yoo ChangKyoo⁴

Affiliation:

1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China

2. Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089, United States

3. Division of Systems and Control, Department of Information Technology, Uppsala University, Uppsala, 75105, Sweden

4. Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Yongin, 446701, Korea

Funder

National Research Foundation of Korea

Nanjing Forestry University

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.9b00701

Reference34 articles.

1. Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes

2. Process modeling based on nonlinear PLS models using a prior knowledge-driven time difference method

3. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models

4. The process chemometrics approach to process monitoring and fault detection

5. Predictive Dynamic Modeling of Key Process Variables in Granulation Processes Using Partial Least Squares Approach

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