Semi-supervised learning for data-driven soft-sensing of biological and chemical processes-Reference-Cited by-同舟云学术

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Semi-supervised learning for data-driven soft-sensing of biological and chemical processes

Published:2022-04 Issue: Volume:251 Page:117459
ISSN:0009-2509
Container-title:Chemical Engineering Science
language:en
Short-container-title:Chemical Engineering Science

Author:

Esche Erik,Talis Torben,Weigert Joris,Brand Rihm Gerardo,You Byungjun,Hoffmann Christian,Repke Jens-Uwe

Publisher

Elsevier BV

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Reference45 articles.

1. Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes;Alexander;Processes,2020

2. Performance Evaluation of Real Industrial RTO Systems;Câmara;Processes,2016

3. Integrating dynamic slow feature analysis with neural networks for enhancing soft sensor performance;Corrigan;Comput. Chem. Eng.,2020

4. Current and future requirements to industrial analytical infrastructure-part 1: process analytical laboratories;Eisen;Anal. Bioanal. Chem.,2020

5. Feedback control for optimal process operation;Engell;J. Process Control,2007

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1. Laplacian regularization of linear regression model for semi-supervised industrial soft sensor development;Expert Systems with Applications;2024-11

2. Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning;Chemical Engineering Science;2024-10

3. Hybrid modeling for carbon monoxide gas-phase catalytic coupling to synthesize dimethyl oxalate process;Chinese Journal of Chemical Engineering;2024-06

4. Noise adaptive filtering model integrating spatio-temporal feature for soft sensor;Expert Systems with Applications;2024-04

5. Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes;Sensors;2024-03-24

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