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Volumn 28, Issue , 2015, Pages 17-26

Enhancing dynamic soft sensors based on DPLS: A temporal smoothness regularization approach

Author keywords

Dynamic PLS; Dynamic soft sensor; Process control; Quality prediction; Temporal smoothness regularization

Indexed keywords

DISTILLATION; DISTILLATION EQUIPMENT; LEAST SQUARES APPROXIMATIONS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL;

EID: 84924813762     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2015.02.006     Document Type: Article
Times cited : (66)

References (33)
  • 1
    • 77956444702 scopus 로고    scopus 로고
    • The state of the art in chemical process control in Japan: Good practice and questionnaire survey
    • M. Kano, and M. Ogawa The state of the art in chemical process control in Japan: good practice and questionnaire survey J. Process Control 20 9 2010 969 982
    • (2010) J. Process Control , vol.20 , Issue.9 , pp. 969-982
    • Kano, M.1    Ogawa, M.2
  • 2
    • 79954540625 scopus 로고    scopus 로고
    • Robust processes through latent variable modeling and optimization
    • F. Yacoub, and J.F. MacGregor Robust processes through latent variable modeling and optimization AIChE J. 57 5 2011 1278 1287
    • (2011) AIChE J. , vol.57 , Issue.5 , pp. 1278-1287
    • Yacoub, F.1    Macgregor, J.F.2
  • 3
    • 84890854491 scopus 로고    scopus 로고
    • Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach
    • J. Mori, and J. Yu Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach J. Process Control 24 1 2014 57 71
    • (2014) J. Process Control , vol.24 , Issue.1 , pp. 57-71
    • Mori, J.1    Yu, J.2
  • 4
    • 67349089877 scopus 로고    scopus 로고
    • Data-driven soft sensors in the process industry
    • P. Kadlec, B. Gabrys, and S. Strandt Data-driven soft sensors in the process industry Comput. Chem. Eng. 33 4 2009 795 814
    • (2009) Comput. Chem. Eng. , vol.33 , Issue.4 , pp. 795-814
    • Kadlec, P.1    Gabrys, B.2    Strandt, S.3
  • 5
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modeling
    • S. Joe Qin Recursive PLS algorithms for adaptive data modeling Comput. Chem. Eng. 22 4 1998 503 514
    • (1998) Comput. Chem. Eng. , vol.22 , Issue.4 , pp. 503-514
    • Joe Qin, S.1
  • 6
    • 60649090799 scopus 로고    scopus 로고
    • Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process
    • P. Facco, F. Doplicher, F. Bezzo, and M. Barolo Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process J. Process Control 19 3 2009 520 529
    • (2009) J. Process Control , vol.19 , Issue.3 , pp. 520-529
    • Facco, P.1    Doplicher, F.2    Bezzo, F.3    Barolo, M.4
  • 7
    • 58449118276 scopus 로고    scopus 로고
    • Development of a new soft sensor method using independent component analysis and partial least squares
    • H. Kaneko, M. Arakawa, and K. Funatsu Development of a new soft sensor method using independent component analysis and partial least squares AIChE J. 55 1 2009 87 98
    • (2009) AIChE J. , vol.55 , Issue.1 , pp. 87-98
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 8
    • 0003288488 scopus 로고    scopus 로고
    • Neural networks for intelligent sensors and control
    • S. Joe Qin Neural networks for intelligent sensors and control Neural Syst. Control 1997 213
    • (1997) Neural Syst. Control , pp. 213
    • Joe Qin, S.1
  • 9
    • 84894261826 scopus 로고    scopus 로고
    • Data-driven soft sensor development based on deep learning technique
    • C. Shang, F. Yang, D. Huang, and W. Lyu Data-driven soft sensor development based on deep learning technique J. Process Control 24 3 2014 223 233
    • (2014) J. Process Control , vol.24 , Issue.3 , pp. 223-233
    • Shang, C.1    Yang, F.2    Huang, D.3    Lyu, W.4
  • 10
    • 2342567014 scopus 로고    scopus 로고
    • Soft sensing modeling based on support vector machine and Bayesian model selection
    • W. Yan, H. Shao, and X. Wang Soft sensing modeling based on support vector machine and Bayesian model selection Comput. Chem. Eng. 28 8 2004 1489 1498
    • (2004) Comput. Chem. Eng. , vol.28 , Issue.8 , pp. 1489-1498
    • Yan, W.1    Shao, H.2    Wang, X.3
  • 11
    • 27444433806 scopus 로고    scopus 로고
    • Soft-sensor development for fed-batch bioreactors using support vector regression
    • K. Desai, Y. Badhe, S.S. Tambe, and B.D. Kulkarni Soft-sensor development for fed-batch bioreactors using support vector regression Biochem. Eng. J. 27 3 2006 225 239
    • (2006) Biochem. Eng. J. , vol.27 , Issue.3 , pp. 225-239
    • Desai, K.1    Badhe, Y.2    Tambe, S.S.3    Kulkarni, B.D.4
  • 12
    • 57249097849 scopus 로고    scopus 로고
    • Dealing with irregular data in soft sensors: Bayesian method and comparative study
    • S. Khatibisepehr, and B. Huang Dealing with irregular data in soft sensors: Bayesian method and comparative study Ind. Eng. Chem. Res. 47 22 2008 8713 8723
    • (2008) Ind. Eng. Chem. Res. , vol.47 , Issue.22 , pp. 8713-8723
    • Khatibisepehr, S.1    Huang, B.2
  • 13
    • 0033874839 scopus 로고    scopus 로고
    • Inferential control system of distillation compositions using dynamic partial least squares regression
    • M. Kano, K. Miyazaki, S. Hasebe, and I. Hashimoto Inferential control system of distillation compositions using dynamic partial least squares regression J. Process Control 10 2 2000 157 166
    • (2000) J. Process Control , vol.10 , Issue.2 , pp. 157-166
    • Kano, M.1    Miyazaki, K.2    Hasebe, S.3    Hashimoto, I.4
  • 14
    • 84875586422 scopus 로고    scopus 로고
    • Modeling of soft sensor for chemical process
    • P. Cao, and X. Luo Modeling of soft sensor for chemical process CIESC J. 3 2013 004
    • (2013) CIESC J. , vol.3 , pp. 004
    • Cao, P.1    Luo, X.2
  • 15
    • 0025010309 scopus 로고
    • Use of neural nets for dynamic modeling and control of chemical process systems
    • N. Bhat, and T.J. McAvoy Use of neural nets for dynamic modeling and control of chemical process systems Comput. Chem. Eng. 14 4 1990 573 582
    • (1990) Comput. Chem. Eng. , vol.14 , Issue.4 , pp. 573-582
    • Bhat, N.1    McAvoy, T.J.2
  • 16
    • 84892975847 scopus 로고    scopus 로고
    • An iterative two-level optimization method for the modeling of Wiener structure nonlinear dynamic soft sensors
    • X. Gao, F. Yang, D. Huang, and Y. Ding An iterative two-level optimization method for the modeling of Wiener structure nonlinear dynamic soft sensors Ind. Eng. Chem. Res. 53 3 2014 1172 1178
    • (2014) Ind. Eng. Chem. Res. , vol.53 , Issue.3 , pp. 1172-1178
    • Gao, X.1    Yang, F.2    Huang, D.3    Ding, Y.4
  • 17
    • 84903308523 scopus 로고    scopus 로고
    • Modeling for soft sensor systems and parameters updating online
    • P. Cao, and X. Luo Modeling for soft sensor systems and parameters updating online J. Process Control 24 6 2014 975 990
    • (2014) J. Process Control , vol.24 , Issue.6 , pp. 975-990
    • Cao, P.1    Luo, X.2
  • 18
    • 24344446381 scopus 로고    scopus 로고
    • Discussion about dynamic soft-sensing modeling
    • Y. Ma, D. Huang, and Y. Jin Discussion about dynamic soft-sensing modeling J. Chem. Ind. Eng. 56 8 2005 1516
    • (2005) J. Chem. Ind. Eng. , vol.56 , Issue.8 , pp. 1516
    • Ma, Y.1    Huang, D.2    Jin, Y.3
  • 19
    • 78149281663 scopus 로고    scopus 로고
    • A novel calibration approach of soft sensor based on multirate data fusion technology
    • Y. Wu, and X. Luo A novel calibration approach of soft sensor based on multirate data fusion technology J. Process Control 20 10 2010 1252 1260
    • (2010) J. Process Control , vol.20 , Issue.10 , pp. 1252-1260
    • Wu, Y.1    Luo, X.2
  • 20
    • 84903307642 scopus 로고    scopus 로고
    • Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response
    • C. Shang, X. Gao, F. Yang, and D. Huang Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response IEEE Trans. Control Syst. Technol. 22 4 2014 1550 1557
    • (2014) IEEE Trans. Control Syst. Technol. , vol.22 , Issue.4 , pp. 1550-1557
    • Shang, C.1    Gao, X.2    Yang, F.3    Huang, D.4
  • 22
    • 79953832419 scopus 로고    scopus 로고
    • A reduced order soft sensor approach and its application to a continuous digester
    • H.J. Galicia, Q.P. He, and J. Wang A reduced order soft sensor approach and its application to a continuous digester J. Process Control 21 4 2011 489 500
    • (2011) J. Process Control , vol.21 , Issue.4 , pp. 489-500
    • Galicia, H.J.1    He, Q.P.2    Wang, J.3
  • 23
    • 0035965460 scopus 로고    scopus 로고
    • Some theoretical aspects of partial least squares regression
    • I.S. Helland Some theoretical aspects of partial least squares regression Chemom. Intell. Lab. Syst. 58 2 2001 97 107
    • (2001) Chemom. Intell. Lab. Syst. , vol.58 , Issue.2 , pp. 97-107
    • Helland, I.S.1
  • 27
    • 84858077893 scopus 로고    scopus 로고
    • The smooth-lasso and other l1 + l2-penalized methods
    • M. Hebiri, and S. van de Geer The smooth-lasso and other l1 + l2-penalized methods Electron. J. Stat. 5 2011 1184 1226
    • (2011) Electron. J. Stat. , vol.5 , pp. 1184-1226
    • Hebiri, M.1    Van De Geer, S.2
  • 28
    • 0002815256 scopus 로고    scopus 로고
    • Improved PLS algorithms
    • B. Dayal, and J.F. MacGregor Improved PLS algorithms J. Chemom. 11 1 1997 73 85
    • (1997) J. Chemom. , vol.11 , Issue.1 , pp. 73-85
    • Dayal, B.1    Macgregor, J.F.2
  • 29
    • 77953232934 scopus 로고    scopus 로고
    • Segmentation of ARX-models using sum-of-norms regularization
    • H. Ohlsson, L. Ljung, and S. Boyd Segmentation of ARX-models using sum-of-norms regularization Automatica 46 6 2010 1107 1111
    • (2010) Automatica , vol.46 , Issue.6 , pp. 1107-1111
    • Ohlsson, H.1    Ljung, L.2    Boyd, S.3
  • 30
    • 84875226586 scopus 로고    scopus 로고
    • Kernel spectral clustering with memory effect
    • R. Langone, C. Alzate, and J.A.K. Suykens Kernel spectral clustering with memory effect Physica A 392 10 2013 2588 2606
    • (2013) Physica A , vol.392 , Issue.10 , pp. 2588-2606
    • Langone, R.1    Alzate, C.2    Suykens, J.A.K.3
  • 31
    • 21244436700 scopus 로고    scopus 로고
    • Performance of some variable selection methods when multicollinearity is present
    • I.-G. Chong, and C.-H. Jun Performance of some variable selection methods when multicollinearity is present Chemom. Intell. Lab. Syst. 78 1 2005 103 112
    • (2005) Chemom. Intell. Lab. Syst. , vol.78 , Issue.1 , pp. 103-112
    • Chong, I.-G.1    Jun, C.-H.2
  • 32
    • 0027561446 scopus 로고
    • A plant-wide industrial process control problem
    • J.J. Downs, and E.F. Vogel A plant-wide industrial process control problem Comput. Chem. Eng. 17 3 1993 245 255
    • (1993) Comput. Chem. Eng. , vol.17 , Issue.3 , pp. 245-255
    • Downs, J.J.1    Vogel, E.F.2
  • 33
    • 0030217795 scopus 로고    scopus 로고
    • Decentralized control of the Tennessee Eastman challenge process
    • N. Lawrence Ricker Decentralized control of the Tennessee Eastman challenge process J. Process Control 6 4 1996 205 221
    • (1996) J. Process Control , vol.6 , Issue.4 , pp. 205-221
    • Lawrence Ricker, N.1


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