Soft Sensor Development Based on the Hierarchical Ensemble of Gaussian Process Regression Models for Nonlinear and Non-Gaussian Chemical Processes
Author:
Affiliation:
1. Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
2. Beijing Research & Design Institute of Rubber Industry, Beijing 100143, People’s Republic of China
Funder
Ministry of Science and Technology of the People's Republic of China
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.6b00240
Reference64 articles.
1. Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models
2. Data-driven Soft Sensors in the process industry
3. Virtual Sensing Technology in Process Industries: Trends and Challenges Revealed by Recent Industrial Applications
4. Just-in-Time Kernel Learning with Adaptive Parameter Selection for Soft Sensor Modeling of Batch Processes
5. Methods for Plant Data-Based Process Modeling in Soft-Sensor Development
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