chemical process monitoring Latest Research Papers

A Novel Auto-regressive Dynamic Slow Feature Analysis Method for Dynamic chemical Process Monitoring

Chemical Engineering Science ◽

10.1016/j.ces.2021.117236 ◽

2021 ◽

pp. 117236

Author(s):

Yuemei Xu ◽

Mingxing Jia ◽

Zhizhong Mao

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Feature Analysis ◽

Analysis Method ◽

Slow Feature Analysis ◽

Auto Regressive ◽

Chemical Process Monitoring

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Optimized principal component analysis and multi-state Bayesian network integrated method for chemical process monitoring and variable state prediction

Chemical Engineering Journal ◽

10.1016/j.cej.2021.132617 ◽

2021 ◽

pp. 132617

Author(s):

Nan Liu ◽

Ji Wang ◽

Suli Sun ◽

Chuankun Li ◽

Wende Tian

Keyword(s):

Principal Component Analysis ◽

Bayesian Network ◽

Process Monitoring ◽

Chemical Process ◽

Principal Component ◽

Component Analysis ◽

Integrated Method ◽

State Prediction ◽

Chemical Process Monitoring

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Deep model based on mode elimination and Fisher criterion combined with self-organizing map for visual multimodal chemical process monitoring

Information Sciences ◽

10.1016/j.ins.2021.01.036 ◽

2021 ◽

Vol 562 ◽

pp. 13-27

Author(s):

Weipeng Lu ◽

Xuefeng Yan

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Self Organizing Map ◽

Fisher Criterion ◽

Model Based ◽

Deep Model ◽

Chemical Process Monitoring ◽

Self Organizing

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Real-Time Chemical Process Monitoring with UMAP

31st European Symposium on Computer Aided Process Engineering - Computer Aided Chemical Engineering ◽

10.1016/b978-0-323-88506-5.50321-1 ◽

2021 ◽

pp. 2077-2082

Author(s):

Zachary webb ◽

José Romagnoli

Keyword(s):

Real Time ◽

Process Monitoring ◽

Chemical Process ◽

Chemical Process Monitoring

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Machine Learning-Based Reduced Kernel PCA Model for Nonlinear Chemical Process Monitoring

Journal of Control Automation and Electrical Systems ◽

10.1007/s40313-020-00604-w ◽

2020 ◽

Vol 31 (5) ◽

pp. 1196-1209 ◽

Cited By ~ 1

Author(s):

M.-F. Harkat ◽

A. Kouadri ◽

R. Fezai ◽

M. Mansouri ◽

H. Nounou ◽

...

Keyword(s):

Machine Learning ◽

Process Monitoring ◽

Chemical Process ◽

Kernel Pca ◽

Chemical Process Monitoring

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Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models

Soft Computing ◽

10.1007/s00500-020-04673-6 ◽

2020 ◽

Vol 24 (16) ◽

pp. 12289-12302

Author(s):

Zhichao Li ◽

Xuefeng Yan

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Chemical Process Monitoring

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Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23437 ◽

2019 ◽

Vol 97 (6) ◽

pp. 1848-1857 ◽

Cited By ~ 1

Author(s):

Junping Huang ◽

Shifu Yan ◽

Xuefeng Yan

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Principal Component ◽

Chemical Process Monitoring

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Parallel quality-related dynamic principal component regression method for chemical process monitoring

Journal of Process Control ◽

10.1016/j.jprocont.2018.08.009 ◽

2019 ◽

Vol 73 ◽

pp. 33-45 ◽

Cited By ~ 15

Author(s):

Yang Tao ◽

Hongbo Shi ◽

Bing Song ◽

Shuai Tan

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Principal Component Regression ◽

Principal Component ◽

Regression Method ◽

Chemical Process Monitoring

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Nonlinear chemical process monitoring using decentralized kernel principal component analysis and Bayesian inference

2017 11th Asian Control Conference (ASCC) ◽

10.1109/ascc.2017.8287393 ◽

2017 ◽

Author(s):

Wentao Cang ◽

Yujia Fu ◽

Li Xie ◽

Hongfeng Tao ◽

Huizhong Yang

Keyword(s):

Principal Component Analysis ◽

Bayesian Inference ◽

Process Monitoring ◽

Chemical Process ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Chemical Process Monitoring

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Mixture of D-Vine copulas for chemical process monitoring

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2017.08.002 ◽

2017 ◽

Vol 169 ◽

pp. 19-34 ◽

Cited By ~ 6

Author(s):

Wenjing Zheng ◽

Xiang Ren ◽

Nan Zhou ◽

Da Jiang ◽

Shaojun Li

Keyword(s):

Process Monitoring ◽

Chemical Process ◽

Chemical Process Monitoring ◽

Vine Copulas

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chemical process monitoring
Recently Published Documents

TOTAL DOCUMENTS

H-INDEX

A Novel Auto-regressive Dynamic Slow Feature Analysis Method for Dynamic chemical Process Monitoring

Optimized principal component analysis and multi-state Bayesian network integrated method for chemical process monitoring and variable state prediction

Deep model based on mode elimination and Fisher criterion combined with self-organizing map for visual multimodal chemical process monitoring

Real-Time Chemical Process Monitoring with UMAP

Machine Learning-Based Reduced Kernel PCA Model for Nonlinear Chemical Process Monitoring

Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Parallel quality-related dynamic principal component regression method for chemical process monitoring

Nonlinear chemical process monitoring using decentralized kernel principal component analysis and Bayesian inference

Mixture of D-Vine copulas for chemical process monitoring

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chemical process monitoringRecently Published Documents

TOTAL DOCUMENTS

H-INDEX

A Novel Auto-regressive Dynamic Slow Feature Analysis Method for Dynamic chemical Process Monitoring

Optimized principal component analysis and multi-state Bayesian network integrated method for chemical process monitoring and variable state prediction

Deep model based on mode elimination and Fisher criterion combined with self-organizing map for visual multimodal chemical process monitoring

Real-Time Chemical Process Monitoring with UMAP

Machine Learning-Based Reduced Kernel PCA Model for Nonlinear Chemical Process Monitoring

Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models

Robust chemical process monitoring based on CDC‐MVT‐PCA eliminating outliers and optimally selecting principal component

Parallel quality-related dynamic principal component regression method for chemical process monitoring

Nonlinear chemical process monitoring using decentralized kernel principal component analysis and Bayesian inference

Mixture of D-Vine copulas for chemical process monitoring

chemical process monitoring
Recently Published Documents