Online Performance Monitoring and Modeling Paradigm Based on Just-in-Time Learning and Extreme Learning Machine for a Non-Gaussian Chemical Process-Reference-Cited by-同舟云学术

Online Performance Monitoring and Modeling Paradigm Based on Just-in-Time Learning and Extreme Learning Machine for a Non-Gaussian Chemical Process

Published:2017-06-01 Issue:23 Volume:56 Page:6671-6684
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Peng Xin¹,Tang Yang¹,Du Wenli¹,Qian Feng¹^ORCID

Affiliation:

1. The Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

Funder

Shanghai Municipal Education Commission

National Natural Science Foundation of China

Shanghai Education Development Foundation

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.6b04633

Reference54 articles.

1. A non-Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process

2. Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method

3. Supervision of fed-batch fermentations

4. A Just-in-Time Learning Based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis

5. On unifying multiblock analysis with application to decentralized process monitoring

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