CVAE‐SFA: A practical dual‐channel and multi‐index comprehensive operating performance assessment method for dynamic nonstationary industrial processes

The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process. However, the actual industrial production process often exhibits nonstationary characteristics, leading to frequent anomalies that complicate P...

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Published in:Canadian journal of chemical engineering Vol. 104; no. 1; pp. 240 - 261
Main Authors: Zhang, Zhipeng, Wei, Libin, Hao, Xiaochen, Huang, Gaolu, Hu, Jiahao, Lu, Tianqiang
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 15.07.2025
Subjects:
cement clinker production process (CCPP)
conditional variational autoencoder (CVAE)
nonstationary characteristic
process operating performance assessment (POPA)
slow feature analysis (SFA)
ISSN:0008-4034, 1939-019X
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Abstract The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process. However, the actual industrial production process often exhibits nonstationary characteristics, leading to frequent anomalies that complicate POPA. Traditional POPA methods are mostly based on limited stable working conditions and single control limits, so it is difficult to fully evaluate the anomaly degree and lack comprehensive assessment indexes. To address these issues, this paper proposes a CVAE‐SFA dual‐channel multi‐index comprehensive POPA method for nonstationary conditions. It introduces four control limits of ‘optimal, good, general, and poor’ based on the optimal reconstruction error from a conditional variational autoencoder (CVAE) and kernel density estimation (KDE). This creates a CVAE optimal grade classification channel for simultaneous qualitative and quantitative evaluations of current operating conditions. Additionally, to monitor dynamic characteristics under nonstationary conditions, a slow feature analysis (SFA) S2 statistic anomaly monitoring channel is established. By identifying slow‐changing essential features within the system, dynamic anomaly monitoring is achieved. Finally, a comprehensive scoring index (CSI) integrates dual‐channel monitoring results to evaluate quality, yield, energy consumption, and stability. The effectiveness of this method is validated through experiments in the cement clinker production process (CCPP).
AbstractList The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process. However, the actual industrial production process often exhibits nonstationary characteristics, leading to frequent anomalies that complicate POPA. Traditional POPA methods are mostly based on limited stable working conditions and single control limits, so it is difficult to fully evaluate the anomaly degree and lack comprehensive assessment indexes. To address these issues, this paper proposes a CVAE‐SFA dual‐channel multi‐index comprehensive POPA method for nonstationary conditions. It introduces four control limits of ‘optimal, good, general, and poor’ based on the optimal reconstruction error from a conditional variational autoencoder (CVAE) and kernel density estimation (KDE). This creates a CVAE optimal grade classification channel for simultaneous qualitative and quantitative evaluations of current operating conditions. Additionally, to monitor dynamic characteristics under nonstationary conditions, a slow feature analysis (SFA) S2 statistic anomaly monitoring channel is established. By identifying slow‐changing essential features within the system, dynamic anomaly monitoring is achieved. Finally, a comprehensive scoring index (CSI) integrates dual‐channel monitoring results to evaluate quality, yield, energy consumption, and stability. The effectiveness of this method is validated through experiments in the cement clinker production process (CCPP).
The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process. However, the actual industrial production process often exhibits nonstationary characteristics, leading to frequent anomalies that complicate POPA. Traditional POPA methods are mostly based on limited stable working conditions and single control limits, so it is difficult to fully evaluate the anomaly degree and lack comprehensive assessment indexes. To address these issues, this paper proposes a CVAE‐SFA dual‐channel multi‐index comprehensive POPA method for nonstationary conditions. It introduces four control limits of ‘optimal, good, general, and poor’ based on the optimal reconstruction error from a conditional variational autoencoder (CVAE) and kernel density estimation (KDE). This creates a CVAE optimal grade classification channel for simultaneous qualitative and quantitative evaluations of current operating conditions. Additionally, to monitor dynamic characteristics under nonstationary conditions, a slow feature analysis (SFA) statistic anomaly monitoring channel is established. By identifying slow‐changing essential features within the system, dynamic anomaly monitoring is achieved. Finally, a comprehensive scoring index (CSI) integrates dual‐channel monitoring results to evaluate quality, yield, energy consumption, and stability. The effectiveness of this method is validated through experiments in the cement clinker production process (CCPP).
Author Hu, Jiahao
Hao, Xiaochen
Huang, Gaolu
Zhang, Zhipeng
Wei, Libin
Lu, Tianqiang
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Cites_doi 10.1016/j.jprocont.2021.07.007
10.1016/j.jprocont.2022.01.001
10.1021/acs.iecr.8b05005
10.1016/j.eswa.2022.117919
10.1016/j.jprocont.2019.04.001
10.1016/j.eswa.2019.113105
10.1016/j.psep.2024.04.124
10.1109/TII.2023.3242816
10.1016/j.ins.2023.118988
10.1109/TCYB.2021.3071665
10.1371/journal.pone.0157567
10.1021/ie801870g
10.1016/j.compchemeng.2020.107156
10.1002/aic.14888
10.1016/j.conengprac.2019.104187
10.1016/j.isatra.2021.01.002
10.1126/science.1205438
10.1109/JSEN.2022.3221282
10.1016/j.jprocont.2024.103209
10.1016/j.cherd.2015.03.008
10.1021/ie9019648
10.1002/cjce.24193
10.1109/TII.2023.3342411
10.1016/j.isatra.2021.10.024
10.1109/TASE.2022.3230687
10.1109/TII.2019.2934757
10.1214/aoms/1177728190
10.1016/j.jprocont.2023.103058
10.1002/cjce.23159
10.1162/089976602317318938
10.1109/TASE.2012.2187892
10.1214/aoms/1177704472
10.3390/pr10020335
10.1109/TCYB.2021.3140065
10.1016/j.compind.2024.104131
10.1016/j.conengprac.2020.104698
10.1016/j.asoc.2022.109889
10.1016/j.jprocont.2022.06.011
10.1016/j.jprocont.2015.12.004
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References 2022; 111
2023; 53
2002; 14
2011; 334
2019; 93
2019; 77
2015; 97
2021; 105
2023; 19
2021; 107
2024; 162
2020; 16
2024; 187
2020; 145
2021; 144
2023; 129
2022; 22
1962; 33
2016; 39
2022; 116
2009; 48
202; 100
2016; 11
1985; 39
2010; 49
2024; 138
2015; 61
2021; 114
2023; 132
2013; 255
1956; 27
2024; 20
2022; 52
2018; 96
2024; 21
2022; 10
2022; 33
2022; 207
2022; 128
2023; 639
2018; 58
2012; 9
Ji C. (e_1_2_10_3_1) 2022; 10
Zhao C. (e_1_2_10_2_1) 2022; 116
Parzen E. (e_1_2_10_39_1) 1962; 33
Tornqvist L. (e_1_2_10_45_1) 1985; 39
CanoIzquierdo J. M. (e_1_2_10_6_1) 2012; 9
Shang C. (e_1_2_10_26_1) 2015; 61
Zhang J. (e_1_2_10_40_1) 2023; 53
Ji H. (e_1_2_10_29_1) 2022; 22
Bu K. (e_1_2_10_20_1) 2024; 20
Rosenblatt M. (e_1_2_10_44_1) 1956; 27
Qin Y. (e_1_2_10_27_1) 2019; 77
Du S. (e_1_2_10_13_1) 2022; 52
Wang Y. (e_1_2_10_17_1) 2021; 144
Gao X. (e_1_2_10_28_1) 2021; 105
Reshef D. N. (e_1_2_10_41_1) 2011; 334
Bu K. (e_1_2_10_19_1) 2022; 128
Niu D. (e_1_2_10_12_1); 100
Tang P. (e_1_2_10_36_1) 2023; 19
Zhang C. (e_1_2_10_22_1) 2024; 187
e_1_2_10_35_1
Wang K. (e_1_2_10_8_1) 2023; 129
e_1_2_10_34_1
Wang H. (e_1_2_10_30_1) 2022; 111
Zhang Z. (e_1_2_10_31_1) 2024; 138
Tang P. (e_1_2_10_32_1) 2021; 114
Peng C. (e_1_2_10_33_1) 2022; 207
Shang C. (e_1_2_10_38_1) 2016; 39
Zou X. (e_1_2_10_23_1) 2018; 58
Zou X. (e_1_2_10_24_1) 2020; 16
Chen Y. (e_1_2_10_42_1) 2016; 11
Zhang C. (e_1_2_10_14_1) 2023; 132
Song P. (e_1_2_10_25_1) 2022; 33
Liu Y. (e_1_2_10_10_1) 2015; 97
Wiskott L. (e_1_2_10_37_1) 2002; 14
Ahsan M. (e_1_2_10_43_1) 2020; 145
Zou X. (e_1_2_10_16_1) 2018; 96
Zhang C. (e_1_2_10_4_1) 2024; 162
Ye L. (e_1_2_10_15_1) 2009; 48
Zhang C. (e_1_2_10_18_1) 2021; 107
Zou X. (e_1_2_10_9_1) 2023; 639
Li L. (e_1_2_10_11_1) 2019; 93
Palma L. B. (e_1_2_10_7_1) 2013
Chu F. (e_1_2_10_21_1) 2024; 21
Pariyani A. (e_1_2_10_5_1) 2010; 49
References_xml – volume: 21
  start-page: 593
  year: 2024
  publication-title: IEEE Trans. Autom. Sci. Eng.
– volume: 114
  start-page: 444
  year: 2021
  publication-title: ISA Trans.
– volume: 33
  start-page: 3416
  year: 2022
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 128
  start-page: 357
  year: 2022
  publication-title: ISA Trans.
– volume: 16
  start-page: 2776
  year: 2020
  publication-title: IEEE Trans. Ind. Inform.
– volume: 144
  year: 2021
  publication-title: Comput. Chem. Eng.
– volume: 187
  start-page: 195
  year: 2024
  publication-title: Process Saf. Environ. Prot.
– volume: 162
  year: 2024
  publication-title: Comput. Ind.
– volume: 255
  year: 2013
– volume: 52
  start-page: 10529
  year: 2022
  publication-title: IEEE Trans. Cybern.
– volume: 39
  start-page: 21
  year: 2016
  publication-title: J. Process Control
– volume: 138
  year: 2024
  publication-title: J. Process Control
– volume: 33
  start-page: 1065
  year: 1962
  publication-title: Ann. Math. Stat.
– volume: 53
  start-page: 4841
  year: 2023
  publication-title: IEEE Trans. Cybern.
– volume: 97
  start-page: 77
  year: 2015
  publication-title: Chem. Eng. Res. Des.
– volume: 100
  start-page: 811
  year: 202
  publication-title: Can. J. Chem. Eng.
– volume: 11
  year: 2016
  publication-title: PLoS One
– volume: 61
  start-page: 3666
  year: 2015
  publication-title: AIChE J.
– volume: 20
  start-page: 6336
  year: 2024
  publication-title: IEEE Trans. Ind. Inform.
– volume: 207
  year: 2022
  publication-title: Expert Syst. Appl.
– volume: 93
  year: 2019
  publication-title: Control Eng. Pract.
– volume: 111
  start-page: 27
  year: 2022
  publication-title: J. Process Control
– volume: 19
  start-page: 10987
  year: 2023
  publication-title: IEEE Trans. Ind. Inform.
– volume: 96
  start-page: 2395
  year: 2018
  publication-title: Can. J. Chem. Eng.
– volume: 334
  start-page: 1518
  year: 2011
  publication-title: Science
– volume: 9
  start-page: 377
  year: 2012
  publication-title: IEEE Trans. Autom. Sci. Eng.
– volume: 48
  start-page: 10912
  year: 2009
  publication-title: Ind. Eng. Chem. Res.
– volume: 22
  start-page: 24299
  year: 2022
  publication-title: IEEE Sens. J.
– volume: 105
  start-page: 27
  year: 2021
  publication-title: J. Process Control
– volume: 49
  start-page: 8062
  year: 2010
  publication-title: Ind. Eng. Chem. Res.
– volume: 639
  year: 2023
  publication-title: Inf. Sci.
– volume: 77
  start-page: 141
  year: 2019
  publication-title: J. Process Control
– volume: 129
  year: 2023
  publication-title: J. Process Control
– volume: 39
  start-page: 43
  year: 1985
  publication-title: Am. Stat.
– volume: 107
  year: 2021
  publication-title: Control Eng. Pract.
– volume: 27
  start-page: 832
  year: 1956
  publication-title: Ann. Math. Stat.
– volume: 116
  start-page: 255
  year: 2022
  publication-title: J. Process Control
– volume: 58
  start-page: 1341
  year: 2018
  publication-title: Ind. Eng. Chem. Res.
– volume: 10
  year: 2022
  publication-title: Processes
– volume: 14
  start-page: 715
  year: 2002
  publication-title: Neural Comput.
– volume: 132
  year: 2023
  publication-title: Appl. Soft Comput.
– volume: 145
  year: 2020
  publication-title: Expert Syst. Appl.
– volume: 105
  start-page: 27
  year: 2021
  ident: e_1_2_10_28_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2021.07.007
– volume: 111
  start-page: 27
  year: 2022
  ident: e_1_2_10_30_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2022.01.001
– volume: 58
  start-page: 1341
  year: 2018
  ident: e_1_2_10_23_1
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/acs.iecr.8b05005
– volume: 207
  year: 2022
  ident: e_1_2_10_33_1
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.117919
– volume: 77
  start-page: 141
  year: 2019
  ident: e_1_2_10_27_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2019.04.001
– volume: 145
  year: 2020
  ident: e_1_2_10_43_1
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2019.113105
– volume: 187
  start-page: 195
  year: 2024
  ident: e_1_2_10_22_1
  publication-title: Process Saf. Environ. Prot.
  doi: 10.1016/j.psep.2024.04.124
– volume: 19
  start-page: 10987
  year: 2023
  ident: e_1_2_10_36_1
  publication-title: IEEE Trans. Ind. Inform.
  doi: 10.1109/TII.2023.3242816
– ident: e_1_2_10_35_1
– volume: 639
  year: 2023
  ident: e_1_2_10_9_1
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2023.118988
– volume: 52
  start-page: 10529
  year: 2022
  ident: e_1_2_10_13_1
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2021.3071665
– volume: 11
  year: 2016
  ident: e_1_2_10_42_1
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0157567
– volume: 48
  start-page: 10912
  year: 2009
  ident: e_1_2_10_15_1
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie801870g
– volume: 144
  year: 2021
  ident: e_1_2_10_17_1
  publication-title: Comput. Chem. Eng.
  doi: 10.1016/j.compchemeng.2020.107156
– start-page: 235
  volume-title: Hybrid Approach for Control Loop Performance Assessment
  year: 2013
  ident: e_1_2_10_7_1
– volume: 61
  start-page: 3666
  year: 2015
  ident: e_1_2_10_26_1
  publication-title: AIChE J.
  doi: 10.1002/aic.14888
– volume: 93
  year: 2019
  ident: e_1_2_10_11_1
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2019.104187
– volume: 33
  start-page: 3416
  year: 2022
  ident: e_1_2_10_25_1
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
– volume: 114
  start-page: 444
  year: 2021
  ident: e_1_2_10_32_1
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2021.01.002
– volume: 334
  start-page: 1518
  year: 2011
  ident: e_1_2_10_41_1
  publication-title: Science
  doi: 10.1126/science.1205438
– volume: 22
  start-page: 24299
  year: 2022
  ident: e_1_2_10_29_1
  publication-title: IEEE Sens. J.
  doi: 10.1109/JSEN.2022.3221282
– volume: 138
  year: 2024
  ident: e_1_2_10_31_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2024.103209
– volume: 97
  start-page: 77
  year: 2015
  ident: e_1_2_10_10_1
  publication-title: Chem. Eng. Res. Des.
  doi: 10.1016/j.cherd.2015.03.008
– volume: 49
  start-page: 8062
  year: 2010
  ident: e_1_2_10_5_1
  publication-title: Ind. Eng. Chem. Res.
  doi: 10.1021/ie9019648
– volume: 100
  start-page: 811
  ident: e_1_2_10_12_1
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.24193
– ident: e_1_2_10_34_1
– volume: 20
  start-page: 6336
  year: 2024
  ident: e_1_2_10_20_1
  publication-title: IEEE Trans. Ind. Inform.
  doi: 10.1109/TII.2023.3342411
– volume: 128
  start-page: 357
  year: 2022
  ident: e_1_2_10_19_1
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2021.10.024
– volume: 39
  start-page: 43
  year: 1985
  ident: e_1_2_10_45_1
  publication-title: Am. Stat.
– volume: 21
  start-page: 593
  year: 2024
  ident: e_1_2_10_21_1
  publication-title: IEEE Trans. Autom. Sci. Eng.
  doi: 10.1109/TASE.2022.3230687
– volume: 16
  start-page: 2776
  year: 2020
  ident: e_1_2_10_24_1
  publication-title: IEEE Trans. Ind. Inform.
  doi: 10.1109/TII.2019.2934757
– volume: 27
  start-page: 832
  year: 1956
  ident: e_1_2_10_44_1
  publication-title: Ann. Math. Stat.
  doi: 10.1214/aoms/1177728190
– volume: 129
  year: 2023
  ident: e_1_2_10_8_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2023.103058
– volume: 96
  start-page: 2395
  year: 2018
  ident: e_1_2_10_16_1
  publication-title: Can. J. Chem. Eng.
  doi: 10.1002/cjce.23159
– volume: 14
  start-page: 715
  year: 2002
  ident: e_1_2_10_37_1
  publication-title: Neural Comput.
  doi: 10.1162/089976602317318938
– volume: 9
  start-page: 377
  year: 2012
  ident: e_1_2_10_6_1
  publication-title: IEEE Trans. Autom. Sci. Eng.
  doi: 10.1109/TASE.2012.2187892
– volume: 33
  start-page: 1065
  year: 1962
  ident: e_1_2_10_39_1
  publication-title: Ann. Math. Stat.
  doi: 10.1214/aoms/1177704472
– volume: 10
  year: 2022
  ident: e_1_2_10_3_1
  publication-title: Processes
  doi: 10.3390/pr10020335
– volume: 53
  start-page: 4841
  year: 2023
  ident: e_1_2_10_40_1
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2021.3140065
– volume: 162
  year: 2024
  ident: e_1_2_10_4_1
  publication-title: Comput. Ind.
  doi: 10.1016/j.compind.2024.104131
– volume: 107
  year: 2021
  ident: e_1_2_10_18_1
  publication-title: Control Eng. Pract.
  doi: 10.1016/j.conengprac.2020.104698
– volume: 132
  year: 2023
  ident: e_1_2_10_14_1
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2022.109889
– volume: 116
  start-page: 255
  year: 2022
  ident: e_1_2_10_2_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2022.06.011
– volume: 39
  start-page: 21
  year: 2016
  ident: e_1_2_10_38_1
  publication-title: J. Process Control
  doi: 10.1016/j.jprocont.2015.12.004
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Snippet The process operating performance assessment (POPA) of process industry plays a key role in the safe, stable, and efficient operation of production process....
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SubjectTerms cement clinker production process (CCPP)
conditional variational autoencoder (CVAE)
nonstationary characteristic
process operating performance assessment (POPA)
slow feature analysis (SFA)
Title CVAE‐SFA: A practical dual‐channel and multi‐index comprehensive operating performance assessment method for dynamic nonstationary industrial processes
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