Variable-Scale Probabilistic Just-in-Time Learning for Soft Sensor Development with Missing Data-Reference-Cited by-同舟云学术

Variable-Scale Probabilistic Just-in-Time Learning for Soft Sensor Development with Missing Data

Published:2020-02-21 Issue:11 Volume:59 Page:5010-5021
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Huang Haojie¹^ORCID,Peng Xin¹²,Jiang Chao¹³^ORCID,Li Zhi¹,Zhong Weimin¹⁴^ORCID

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

2. The Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg 47057, Germany

3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada

4. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

the Programme of Introducing Talents of Discipline to Universities (the 111 Project)

the International Postdoctoral Exchange Fellowship Program

Shanghai Sailing Program

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.9b06113

Reference43 articles.

1. Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression

2. A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process

3. Concurrent Monitoring Strategy for Static and Dynamic Deviations Based on Selective Ensemble Learning Using Slow Feature Analysis

4. Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes

5. Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events

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