Integrating Autoencoder and Heteroscedastic Noise Neural Networks for the Batch Process Soft-Sensor Design
Author:
Affiliation:
1. Department of Chemical Engineering and Analytical Science, University of Manchester, Oxford Road, Manchester M1 3AL, U.K.
2. Unilever Research Port Sunlight, Quarry Road East, Bebington CH63 3JW, U.K.
Funder
Engineering and Physical Sciences Research Council
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.2c01789
Reference33 articles.
1. Data-driven methods for batch data analysis – A critical overview and mapping on the complexity scale
2. Data-Driven Soft Sensor Approach for Quality Prediction in a Refining Process
3. Data-driven Soft Sensors in the process industry
4. Mixture robust semi-supervised probabilistic principal component regression with missing input data
5. A Survey on Deep Learning for Data-Driven Soft Sensors
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