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Volumn 54, Issue 30, 2015, Pages 7320-7345

Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; FERMENTATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES; MIXTURES; PROCESS CONTROL; SOCIAL NETWORKING (ONLINE); STATISTICAL TESTS;

EID: 84938633868     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/acs.iecr.5b01495     Document Type: Article
Times cited : (74)

References (92)
  • 2
    • 84984100048 scopus 로고
    • The use of secondary measurements to improve control
    • Weber, R.; Brosilow, C. The use of secondary measurements to improve control AIChE J. 1972, 18, 614-623 10.1002/aic.690180323
    • (1972) AIChE J. , vol.18 , pp. 614-623
    • Weber, R.1    Brosilow, C.2
  • 3
    • 0017972806 scopus 로고
    • Inferential control of processes: Part I. Steady state analysis and design
    • Joseph, B.; Brosilow, C. B. Inferential control of processes: Part I. Steady state analysis and design AIChE J. 1978, 24, 485-492 10.1002/aic.690240313
    • (1978) AIChE J. , vol.24 , pp. 485-492
    • Joseph, B.1    Brosilow, C.B.2
  • 4
    • 0017972807 scopus 로고
    • Inferential control of processes: Part II. The structure and dynamics of inferential control systems
    • Brosilow, C.; Tong, M. Inferential control of processes: Part II. The structure and dynamics of inferential control systems AIChE J. 1978, 24, 492-500 10.1002/aic.690240314
    • (1978) AIChE J. , vol.24 , pp. 492-500
    • Brosilow, C.1    Tong, M.2
  • 5
    • 0017971592 scopus 로고
    • Inferential control of processes: Part III. Construction of optimal and suboptimal dynamic estimators
    • Joseph, B.; Brosilow, C. Inferential control of processes: Part III. Construction of optimal and suboptimal dynamic estimators AIChE J. 1978, 24, 500-509 10.1002/aic.690240315
    • (1978) AIChE J. , vol.24 , pp. 500-509
    • Joseph, B.1    Brosilow, C.2
  • 6
    • 0002111519 scopus 로고
    • Soft-sensors for process estimation and inferential control
    • Tham, M. T.; Montague, G. A.; Morris, A. J.; Lant, P. A. Soft-sensors for process estimation and inferential control J. Process Control 1991, 1, 3-14 10.1016/0959-1524(91)87002-F
    • (1991) J. Process Control , vol.1 , pp. 3-14
    • Tham, M.T.1    Montague, G.A.2    Morris, A.J.3    Lant, P.A.4
  • 7
    • 0028460607 scopus 로고
    • Development of inferential process models using PLS
    • Kresta, J.; Marlin, T.; MacGregor, J. Development of inferential process models using PLS Comput. Chem. Eng. 1994, 18, 597-611 10.1016/0098-1354(93)E0006-U
    • (1994) Comput. Chem. Eng. , vol.18 , pp. 597-611
    • Kresta, J.1    Marlin, T.2    MacGregor, J.3
  • 8
    • 0034661135 scopus 로고    scopus 로고
    • A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns
    • Park, S.; Han, C. A nonlinear soft sensor based on multivariate smoothing procedure for quality estimation in distillation columns Comput. Chem. Eng. 2000, 24, 871-877 10.1016/S0098-1354(00)00343-4
    • (2000) Comput. Chem. Eng. , vol.24 , pp. 871-877
    • Park, S.1    Han, C.2
  • 10
    • 33847162850 scopus 로고    scopus 로고
    • A systematic approach for soft sensor development
    • Lin, B.; Recke, B.; Knudsen, J. K.; Jørgensen, S. B. A systematic approach for soft sensor development Comput. Chem. Eng. 2007, 31, 419-425 10.1016/j.compchemeng.2006.05.030
    • (2007) Comput. Chem. Eng. , vol.31 , pp. 419-425
    • Lin, B.1    Recke, B.2    Knudsen, J.K.3    Jørgensen, S.B.4
  • 11
    • 35548968908 scopus 로고    scopus 로고
    • Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry
    • Kano, M.; Nakagawa, Y. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry Comput. Chem. Eng. 2008, 32, 12-24 10.1016/j.compchemeng.2007.07.005
    • (2008) Comput. Chem. Eng. , vol.32 , pp. 12-24
    • Kano, M.1    Nakagawa, Y.2
  • 12
    • 67349089877 scopus 로고    scopus 로고
    • Data-driven soft sensors in the process industry
    • Kadlec, P.; Gabrys, B.; Strandt, S. Data-driven soft sensors in the process industry Comput. Chem. Eng. 2009, 33, 795-814 10.1016/j.compchemeng.2008.12.012
    • (2009) Comput. Chem. Eng. , vol.33 , pp. 795-814
    • Kadlec, P.1    Gabrys, B.2    Strandt, S.3
  • 13
    • 84872920533 scopus 로고    scopus 로고
    • Virtual sensing technology in process industries: Trends and challenges revealed by recent industrial applications
    • Kano, M.; Fujiwara, K. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications J. Chem. Eng. Jpn. 2013, 46, 1-17 10.1252/jcej.12we167
    • (2013) J. Chem. Eng. Jpn. , vol.46 , pp. 1-17
    • Kano, M.1    Fujiwara, K.2
  • 15
    • 68049143320 scopus 로고    scopus 로고
    • Soft-sensor development using correlation-based just-in-time modeling
    • Fujiwara, K.; Kano, M.; Hasebe, S.; Takinami, A. Soft-sensor development using correlation-based just-in-time modeling AIChE J. 2009, 55, 1754-1765 10.1002/aic.11791
    • (2009) AIChE J. , vol.55 , pp. 1754-1765
    • Fujiwara, K.1    Kano, M.2    Hasebe, S.3    Takinami, A.4
  • 16
    • 84889685311 scopus 로고    scopus 로고
    • External analysis-based regression model for robust soft sensing of multimode chemical processes
    • Ge, Z.; Song, Z.; Kano, M. External analysis-based regression model for robust soft sensing of multimode chemical processes AIChE J. 2014, 60, 136-147 10.1002/aic.14253
    • (2014) AIChE J. , vol.60 , pp. 136-147
    • Ge, Z.1    Song, Z.2    Kano, M.3
  • 17
    • 84892445860 scopus 로고    scopus 로고
    • Mixture semisupervised principal component regression model and soft sensor application
    • Ge, Z.; Huang, B.; Song, Z. Mixture semisupervised principal component regression model and soft sensor application AIChE J. 2014, 60, 533-545 10.1002/aic.14270
    • (2014) AIChE J. , vol.60 , pp. 533-545
    • Ge, Z.1    Huang, B.2    Song, Z.3
  • 18
    • 0035965476 scopus 로고    scopus 로고
    • PLS-regression: A basic tool of chemometrics
    • Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics Chemom. Intell. Lab. Syst. 2001, 58, 109-130 10.1016/S0169-7439(01)00155-1
    • (2001) Chemom. Intell. Lab. Syst. , vol.58 , pp. 109-130
    • Wold, S.1    Sjöström, M.2    Eriksson, L.3
  • 19
    • 79954599740 scopus 로고    scopus 로고
    • Local learning-based adaptive soft sensor for catalyst activation prediction
    • Kadlec, P.; Gabrys, B. Local learning-based adaptive soft sensor for catalyst activation prediction AIChE J. 2011, 57, 1288-1301 10.1002/aic.12346
    • (2011) AIChE J. , vol.57 , pp. 1288-1301
    • Kadlec, P.1    Gabrys, B.2
  • 20
    • 84862208873 scopus 로고    scopus 로고
    • Localized, adaptive recursive partial least squares regression for dynamic system modeling
    • Ni, W.; Tan, S. K.; Ng, W. J.; Brown, S. D. Localized, adaptive recursive partial least squares regression for dynamic system modeling Ind. Eng. Chem. Res. 2012, 51, 8025-8039 10.1021/ie203043q
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 8025-8039
    • Ni, W.1    Tan, S.K.2    Ng, W.J.3    Brown, S.D.4
  • 21
    • 84896913551 scopus 로고    scopus 로고
    • A localized adaptive soft sensor for dynamic system modeling
    • Ni, W.; Br own, S. D.; Man, R. A localized adaptive soft sensor for dynamic system modeling Chem. Eng. Sci. 2014, 111, 350-363 10.1016/j.ces.2014.03.002
    • (2014) Chem. Eng. Sci. , vol.111 , pp. 350-363
    • Ni, W.1    Brown, S.D.2    Man, R.3
  • 22
    • 84883736569 scopus 로고    scopus 로고
    • Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges
    • Kim, S.; Kano, M.; Hasebe, S.; Takinami, A.; Seki, T. Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges Ind. Eng. Chem. Res. 2013, 52, 12346-12356 10.1021/ie303488m
    • (2013) Ind. Eng. Chem. Res. , vol.52 , pp. 12346-12356
    • Kim, S.1    Kano, M.2    Hasebe, S.3    Takinami, A.4    Seki, T.5
  • 23
    • 84924229688 scopus 로고    scopus 로고
    • Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process
    • Jin, H.; Chen, X.; Yang, J.; Wang, L.; Wu, L. Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process Chemom. Intell. Lab. Syst. 2015, 143, 58-78 10.1016/j.chemolab.2015.02.018
    • (2015) Chemom. Intell. Lab. Syst. , vol.143 , pp. 58-78
    • Jin, H.1    Chen, X.2    Yang, J.3    Wang, L.4    Wu, L.5
  • 24
    • 84906316402 scopus 로고    scopus 로고
    • Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division
    • Shao, W.; Tian, X.; Wang, P. Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division Chin. J. Chem. Eng. 2014, 22, 828-836 10.1016/j.cjche.2014.05.003
    • (2014) Chin. J. Chem. Eng. , vol.22 , pp. 828-836
    • Shao, W.1    Tian, X.2    Wang, P.3
  • 25
    • 84961290207 scopus 로고    scopus 로고
    • Adaptive Soft Sensor for Quality Prediction of Chemical Processes Based on Selective Ensemble of Local Partial Least Squares Models
    • Shao, W.; Tian, X. Adaptive Soft Sensor for Quality Prediction of Chemical Processes Based on Selective Ensemble of Local Partial Least Squares Models Chem. Eng. Res. Des. 2015, 95, 113-132 10.1016/j.cherd.2015.01.006
    • (2015) Chem. Eng. Res. Des. , vol.95 , pp. 113-132
    • Shao, W.1    Tian, X.2
  • 26
    • 58449118276 scopus 로고    scopus 로고
    • Development of a new soft sensor method using independent component analysis and partial least squares
    • Kaneko, H.; Arakawa, M.; Funatsu, K. Development of a new soft sensor method using independent component analysis and partial least squares AIChE J. 2009, 55, 87-98 10.1002/aic.11648
    • (2009) AIChE J. , vol.55 , pp. 87-98
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 27
    • 84887725182 scopus 로고    scopus 로고
    • Ensemble independent component regression models and soft sensing application
    • Ge, Z.; Song, Z. Ensemble independent component regression models and soft sensing application Chemom. Intell. Lab. Syst. 2014, 130, 115-122 10.1016/j.chemolab.2013.09.009
    • (2014) Chemom. Intell. Lab. Syst. , vol.130 , pp. 115-122
    • Ge, Z.1    Song, Z.2
  • 28
    • 84894317151 scopus 로고    scopus 로고
    • Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes
    • Ge, Z.; Song, Z.; Wang, P. Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes Chem. Eng. Res. Des. 2014, 92, 509-521 10.1016/j.cherd.2013.09.010
    • (2014) Chem. Eng. Res. Des. , vol.92 , pp. 509-521
    • Ge, Z.1    Song, Z.2    Wang, P.3
  • 29
    • 84906872234 scopus 로고    scopus 로고
    • Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes
    • Yuan, X.; Ge, Z.; Song, Z. Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes Ind. Eng. Chem. Res. 2014, 53, 13736-13749 10.1021/ie4041252
    • (2014) Ind. Eng. Chem. Res. , vol.53 , pp. 13736-13749
    • Yuan, X.1    Ge, Z.2    Song, Z.3
  • 30
    • 0038259120 scopus 로고    scopus 로고
    • Kernel partial least squares regression in reproducing kernel hilbert space
    • Rosipal, R.; Trejo, L. J. Kernel partial least squares regression in reproducing kernel hilbert space Journal of Machine Learning Research 2001, 2, 97-123
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 97-123
    • Rosipal, R.1    Trejo, L.J.2
  • 31
    • 84868224530 scopus 로고    scopus 로고
    • Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes
    • Yu, J. Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes Ind. Eng. Chem. Res. 2012, 51, 13227-13237 10.1021/ie3020186
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 13227-13237
    • Yu, J.1
  • 32
    • 84905686213 scopus 로고    scopus 로고
    • Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes
    • Jin, H.; Chen, X.; Yang, J.; Wu, L. Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes Comput. Chem. Eng. 2014, 71, 77-93 10.1016/j.compchemeng.2014.07.014
    • (2014) Comput. Chem. Eng. , vol.71 , pp. 77-93
    • Jin, H.1    Chen, X.2    Yang, J.3    Wu, L.4
  • 33
    • 57049112694 scopus 로고    scopus 로고
    • ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
    • Gonzaga, J.; Meleiro, L.; Kiang, C.; Maciel Filho, R. ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process Comput. Chem. Eng. 2009, 33, 43-49 10.1016/j.compchemeng.2008.05.019
    • (2009) Comput. Chem. Eng. , vol.33 , pp. 43-49
    • Gonzaga, J.1    Meleiro, L.2    Kiang, C.3    Maciel Filho, R.4
  • 34
    • 78650945964 scopus 로고    scopus 로고
    • Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression
    • Niu, D.-p.; Wang, F.-l.; Zhang, L.-l.; He, D.-k.; Jia, M.-x. Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression Chemom. Intell. Lab. Syst. 2011, 105, 125-130 10.1016/j.chemolab.2010.11.007
    • (2011) Chemom. Intell. Lab. Syst. , vol.105 , pp. 125-130
    • Niu, D.-P.1    Wang, F.-L.2    Zhang, L.-L.3    He D.-k.4    Jia, M.-X.5
  • 35
    • 84855946000 scopus 로고    scopus 로고
    • Data-driven prediction of the product formation in industrial 2-keto-L-gulonic acid fermentation
    • Cui, L.; Xie, P.; Sun, J.; Yu, T.; Yuan, J. Data-driven prediction of the product formation in industrial 2-keto-L-gulonic acid fermentation Comput. Chem. Eng. 2012, 36, 386-391 10.1016/j.compchemeng.2011.06.012
    • (2012) Comput. Chem. Eng. , vol.36 , pp. 386-391
    • Cui, L.1    Xie, P.2    Sun, J.3    Yu, T.4    Yuan, J.5
  • 36
    • 27444433806 scopus 로고    scopus 로고
    • Soft-sensor development for fed-batch bioreactors using support vector regression
    • Desai, K.; Badhe, Y.; Tambe, S. S.; Kulkarni, B. D. Soft-sensor development for fed-batch bioreactors using support vector regression Biochem. Eng. J. 2006, 27, 225-239 10.1016/j.bej.2005.08.002
    • (2006) Biochem. Eng. J. , vol.27 , pp. 225-239
    • Desai, K.1    Badhe, Y.2    Tambe, S.S.3    Kulkarni, B.D.4
  • 37
    • 33947266512 scopus 로고    scopus 로고
    • Development of a soft sensor for a batch distillation column using support vector regression techniques
    • Jain, P.; Rahman, I.; Kulkarni, B. Development of a soft sensor for a batch distillation column using support vector regression techniques Chem. Eng. Res. Des. 2007, 85, 283-287 10.1205/cherd05026
    • (2007) Chem. Eng. Res. Des. , vol.85 , pp. 283-287
    • Jain, P.1    Rahman, I.2    Kulkarni, B.3
  • 38
    • 84859392648 scopus 로고    scopus 로고
    • A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses
    • Yu, J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses Comput. Chem. Eng. 2012, 41, 134-144 10.1016/j.compchemeng.2012.03.004
    • (2012) Comput. Chem. Eng. , vol.41 , pp. 134-144
    • Yu, J.1
  • 39
    • 84892441284 scopus 로고    scopus 로고
    • Application of online support vector regression for soft sensors
    • Kaneko, H.; Funatsu, K. Application of online support vector regression for soft sensors AIChE J. 2014, 60, 600-612 10.1002/aic.14299
    • (2014) AIChE J. , vol.60 , pp. 600-612
    • Kaneko, H.1    Funatsu, K.2
  • 40
    • 84903588321 scopus 로고    scopus 로고
    • Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants
    • Kaneko, H.; Funatsu, K. Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants Chemom. Intell. Lab. Syst. 2014, 137, 57-66 10.1016/j.chemolab.2014.06.008
    • (2014) Chemom. Intell. Lab. Syst. , vol.137 , pp. 57-66
    • Kaneko, H.1    Funatsu, K.2
  • 41
    • 84928348714 scopus 로고    scopus 로고
    • Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes
    • Jin, H.; Chen, X.; Yang, J.; Zhang, H.; Wang, L.; Wu, L. Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes Chem. Eng. Sci. 2015, 131, 282-303 10.1016/j.ces.2015.03.038
    • (2015) Chem. Eng. Sci. , vol.131 , pp. 282-303
    • Jin, H.1    Chen, X.2    Yang, J.3    Zhang, H.4    Wang, L.5    Wu, L.6
  • 42
    • 84863357539 scopus 로고    scopus 로고
    • Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes
    • Liu, Y.; Gao, Z.; Li, P.; Wang, H. Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes Ind. Eng. Chem. Res. 2012, 51, 4313-4327 10.1021/ie201650u
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 4313-4327
    • Liu, Y.1    Gao, Z.2    Li, P.3    Wang, H.4
  • 43
    • 84879060636 scopus 로고    scopus 로고
    • Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes
    • Liu, Y.; Chen, J. Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes J. Process Control 2013, 23, 793-804 10.1016/j.jprocont.2013.03.008
    • (2013) J. Process Control , vol.23 , pp. 793-804
    • Liu, Y.1    Chen, J.2
  • 45
    • 61349165676 scopus 로고    scopus 로고
    • Multiple model soft sensor based on affinity propagation, Gaussian process and Bayesian committee machine
    • Li, X.; Su, H.; Chu, J. Multiple model soft sensor based on affinity propagation, gaussian process and bayesian committee machine Chin. J. Chem. Eng. 2009, 17, 95-99 10.1016/S1004-9541(09)60039-2
    • (2009) Chin. J. Chem. Eng. , vol.17 , pp. 95-99
    • Li, X.1    Su, H.2    Chu, J.3
  • 46
    • 61849183105 scopus 로고    scopus 로고
    • Bagging for Gaussian process regression
    • Chen, T.; Ren, J. Bagging for Gaussian process regression Neurocomputing 2009, 72, 1605-1610 10.1016/j.neucom.2008.09.002
    • (2009) Neurocomputing , vol.72 , pp. 1605-1610
    • Chen, T.1    Ren, J.2
  • 47
    • 84864805251 scopus 로고    scopus 로고
    • Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach
    • Yu, J. Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach Chem. Eng. Sci. 2012, 82, 22-30 10.1016/j.ces.2012.07.018
    • (2012) Chem. Eng. Sci. , vol.82 , pp. 22-30
    • Yu, J.1
  • 48
    • 84874515333 scopus 로고    scopus 로고
    • A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty
    • Yu, J.; Chen, K.; Rashid, M. M. A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty Chem. Eng. Sci. 2013, 93, 96-109 10.1016/j.ces.2013.01.058
    • (2013) Chem. Eng. Sci. , vol.93 , pp. 96-109
    • Yu, J.1    Chen, K.2    Rashid, M.M.3
  • 49
    • 84880339799 scopus 로고    scopus 로고
    • Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models
    • Grbić, R.; Slišković, D.; Kadlec, P. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models Comput. Chem. Eng. 2013, 58, 84-97 10.1016/j.compchemeng.2013.06.014
    • (2013) Comput. Chem. Eng. , vol.58 , pp. 84-97
    • Grbić, R.1    Slišković, D.2    Kadlec, P.3
  • 50
    • 84912562670 scopus 로고    scopus 로고
    • Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models
    • Liu, Y.; Gao, Z. Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models. J. Appl. Polym. Sci. 2015, 132, http://dx.doi.org/10.1002/app.41432, 10.1002/app.41432.
    • (2015) J. Appl. Polym. Sci. , vol.132
    • Liu, Y.1    Gao, Z.2
  • 51
    • 84924482066 scopus 로고    scopus 로고
    • Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method
    • Liu, Y.; Gao, Z. Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method. J. Appl. Polym. Sci. 2015, 132, http://dx.doi.org/10.1002/app.41958, 10.1002/app.41958.
    • (2015) J. Appl. Polym. Sci. , vol.132
    • Liu, Y.1    Gao, Z.2
  • 54
    • 0036639869 scopus 로고    scopus 로고
    • Scalable techniques from nonparametric statistics for real time robot learning
    • Schaal, S.; Atkeson, C. G.; Vijayakumar, S. Scalable techniques from nonparametric statistics for real time robot learning Applied Intelligence 2002, 17, 49-60 10.1023/A:1015727715131
    • (2002) Applied Intelligence , vol.17 , pp. 49-60
    • Schaal, S.1    Atkeson, C.G.2    Vijayakumar, S.3
  • 55
    • 2942558590 scopus 로고    scopus 로고
    • A new data-based methodology for nonlinear process modeling
    • Cheng, C.; Chiu, M.-S. A new data-based methodology for nonlinear process modeling Chem. Eng. Sci. 2004, 59, 2801-2810 10.1016/j.ces.2004.04.020
    • (2004) Chem. Eng. Sci. , vol.59 , pp. 2801-2810
    • Cheng, C.1    Chiu, M.-S.2
  • 56
    • 80052809967 scopus 로고    scopus 로고
    • Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes
    • Chen, K.; Ji, J.; Wang, H.; Liu, Y.; Song, Z. Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes Chem. Eng. Res. Des. 2011, 89, 2117-2124 10.1016/j.cherd.2011.01.032
    • (2011) Chem. Eng. Res. Des. , vol.89 , pp. 2117-2124
    • Chen, K.1    Ji, J.2    Wang, H.3    Liu, Y.4    Song, Z.5
  • 57
    • 77956444702 scopus 로고    scopus 로고
    • The state of the art in chemical process control in Japan: Good practice and questionnaire survey
    • Kano, M.; Ogawa, M. The state of the art in chemical process control in Japan: Good practice and questionnaire survey J. Process Control 2010, 20, 969-982 10.1016/j.jprocont.2010.06.013
    • (2010) J. Process Control , vol.20 , pp. 969-982
    • Kano, M.1    Ogawa, M.2
  • 58
    • 26444562687 scopus 로고    scopus 로고
    • The problem of concept drift: Definitions and related work
    • Department of Computer Science, Trinity College Dublin, The University of Dublin: Ireland.
    • Tsymbal, A. The problem of concept drift: definitions and related work. Technical Report; Department of Computer Science, Trinity College Dublin, The University of Dublin: Ireland, 2004.
    • (2004) Technical Report
    • Tsymbal, A.1
  • 59
    • 78649468188 scopus 로고    scopus 로고
    • Review of adaptation mechanisms for data-driven soft sensors
    • Kadlec, P.; Grbić, R.; Gabrys, B. Review of adaptation mechanisms for data-driven soft sensors Comput. Chem. Eng. 2011, 35, 1-24 10.1016/j.compchemeng.2010.07.034
    • (2011) Comput. Chem. Eng. , vol.35 , pp. 1-24
    • Kadlec, P.1    Grbić, R.2    Gabrys, B.3
  • 60
    • 84879309312 scopus 로고    scopus 로고
    • Classification of the degradation of soft sensor models and discussion on adaptive models
    • Kaneko, H.; Funatsu, K. Classification of the degradation of soft sensor models and discussion on adaptive models AIChE J. 2013, 59, 2339-2347 10.1002/aic.14006
    • (2013) AIChE J. , vol.59 , pp. 2339-2347
    • Kaneko, H.1    Funatsu, K.2
  • 61
    • 22944436794 scopus 로고    scopus 로고
    • Process monitoring approach using fast moving window PCA
    • Wang, X.; Kruger, U.; Irwin, G. W. Process monitoring approach using fast moving window PCA Ind. Eng. Chem. Res. 2005, 44, 5691-5702 10.1021/ie048873f
    • (2005) Ind. Eng. Chem. Res. , vol.44 , pp. 5691-5702
    • Wang, X.1    Kruger, U.2    Irwin, G.W.3
  • 62
    • 84861071787 scopus 로고    scopus 로고
    • Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing
    • Ni, W.; Tan, S. K.; Ng, W. J.; Brown, S. D. Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing Ind. Eng. Chem. Res. 2012, 51, 6416-6428 10.1021/ie201898a
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 6416-6428
    • Ni, W.1    Tan, S.K.2    Ng, W.J.3    Brown, S.D.4
  • 64
    • 84921634883 scopus 로고    scopus 로고
    • Moving window and just-in-time soft sensor model based on time differences considering a small number of measurements
    • Kaneko, H.; Funatsu, K. Moving window and just-in-time soft sensor model based on time differences considering a small number of measurements Ind. Eng. Chem. Res. 2015, 54, 700-704 10.1021/ie503962e
    • (2015) Ind. Eng. Chem. Res. , vol.54 , pp. 700-704
    • Kaneko, H.1    Funatsu, K.2
  • 65
    • 0032044750 scopus 로고    scopus 로고
    • Recursive PLS algorithms for adaptive data modeling
    • Qin, S. J. Recursive PLS algorithms for adaptive data modeling Comput. Chem. Eng. 1998, 22, 503-514 10.1016/S0098-1354(97)00262-7
    • (1998) Comput. Chem. Eng. , vol.22 , pp. 503-514
    • Qin, S.J.1
  • 66
    • 33645417998 scopus 로고    scopus 로고
    • Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process
    • Mu, S.; Zeng, Y.; Liu, R.; Wu, P.; Su, H.; Chu, J. Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process J. Process Control 2006, 16, 557-566 10.1016/j.jprocont.2005.11.004
    • (2006) J. Process Control , vol.16 , pp. 557-566
    • Mu, S.1    Zeng, Y.2    Liu, R.3    Wu, P.4    Su, H.5    Chu, J.6
  • 67
    • 59349107270 scopus 로고    scopus 로고
    • A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant
    • Ahmed, F.; Nazir, S.; Yeo, Y. K. A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant Korean J. Chem. Eng. 2009, 26, 14-20 10.1007/s11814-009-0003-3
    • (2009) Korean J. Chem. Eng. , vol.26 , pp. 14-20
    • Ahmed, F.1    Nazir, S.2    Yeo, Y.K.3
  • 68
    • 79959784751 scopus 로고    scopus 로고
    • Maintenance-free soft sensor models with time difference of process variables
    • Kaneko, H.; Funatsu, K. Maintenance-free soft sensor models with time difference of process variables Chemom. Intell. Lab. Syst. 2011, 107, 312-317 10.1016/j.chemolab.2011.04.016
    • (2011) Chemom. Intell. Lab. Syst. , vol.107 , pp. 312-317
    • Kaneko, H.1    Funatsu, K.2
  • 69
    • 80055094175 scopus 로고    scopus 로고
    • A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy
    • Kaneko, H.; Funatsu, K. A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy Chemom. Intell. Lab. Syst. 2011, 109, 197-206 10.1016/j.chemolab.2011.09.003
    • (2011) Chemom. Intell. Lab. Syst. , vol.109 , pp. 197-206
    • Kaneko, H.1    Funatsu, K.2
  • 70
    • 84889677253 scopus 로고    scopus 로고
    • Database monitoring index for adaptive soft sensors and the application to industrial process
    • Kaneko, H.; Funatsu, K. Database monitoring index for adaptive soft sensors and the application to industrial process AIChE J. 2014, 60, 160-169 10.1002/aic.14260
    • (2014) AIChE J. , vol.60 , pp. 160-169
    • Kaneko, H.1    Funatsu, K.2
  • 71
    • 84899843065 scopus 로고    scopus 로고
    • A unified recursive just-in-time approach with industrial near infrared spectroscopy application
    • Chen, M.; Khare, S.; Huang, B. A unified recursive just-in-time approach with industrial near infrared spectroscopy application Chemom. Intell. Lab. Syst. 2014, 135, 133-140 10.1016/j.chemolab.2014.04.007
    • (2014) Chemom. Intell. Lab. Syst. , vol.135 , pp. 133-140
    • Chen, M.1    Khare, S.2    Huang, B.3
  • 72
    • 34147222905 scopus 로고    scopus 로고
    • On-line soft sensor for polyethylene process with multiple production grades
    • Liu, J. On-line soft sensor for polyethylene process with multiple production grades Control Engineering Practice 2007, 15, 769-778 10.1016/j.conengprac.2005.12.005
    • (2007) Control Engineering Practice , vol.15 , pp. 769-778
    • Liu, J.1
  • 73
    • 84891520527 scopus 로고    scopus 로고
    • Novel just-in-time learning-based soft sensor utilizing non-Gaussian information
    • Xie, L.; Zeng, J.; Gao, C. Novel just-in-time learning-based soft sensor utilizing non-Gaussian information IEEE Transactions on Control Systems Technology 2014, 22, 360-368 10.1109/TCST.2013.2248155
    • (2014) IEEE Transactions on Control Systems Technology , vol.22 , pp. 360-368
    • Xie, L.1    Zeng, J.2    Gao, C.3
  • 75
    • 73849104985 scopus 로고    scopus 로고
    • An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace
    • Tian, H.-X.; Mao, Z.-Z. An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace IEEE Transactions on Automation Science and Engineering 2010, 7, 73-80 10.1109/TASE.2008.2005640
    • (2010) IEEE Transactions on Automation Science and Engineering , vol.7 , pp. 73-80
    • Tian, H.-X.1    Mao, Z.-Z.2
  • 76
    • 84888306466 scopus 로고    scopus 로고
    • Design of inferential sensors in the process industry: A review of Bayesian methods
    • Khatibisepehr, S.; Huang, B.; Khare, S. Design of inferential sensors in the process industry: A review of Bayesian methods J. Process Control 2013, 23, 1575-1596 10.1016/j.jprocont.2013.05.007
    • (2013) J. Process Control , vol.23 , pp. 1575-1596
    • Khatibisepehr, S.1    Huang, B.2    Khare, S.3
  • 77
    • 84949117585 scopus 로고    scopus 로고
    • A dynamic and on-line ensemble regression for changing environments
    • Soares, S. G.; Araújo, R. A dynamic and on-line ensemble regression for changing environments Expert Systems with Applications 2015, 42, 2935-2948 10.1016/j.eswa.2014.11.053
    • (2015) Expert Systems with Applications , vol.42 , pp. 2935-2948
    • Soares, S.G.1    Araújo, R.2
  • 78
    • 84910607774 scopus 로고    scopus 로고
    • An on-line weighted ensemble of regressor models to handle concept drifts
    • Gomes Soares, S.; Araújo, R. An on-line weighted ensemble of regressor models to handle concept drifts Engineering Applications of Artificial Intelligence 2015, 37, 392-406 10.1016/j.engappai.2014.10.003
    • (2015) Engineering Applications of Artificial Intelligence , vol.37 , pp. 392-406
    • Gomes Soares, S.1    Araújo, R.2
  • 79
    • 79955611348 scopus 로고    scopus 로고
    • Applicability domains and accuracy of prediction of soft sensor models
    • Kaneko, H.; Arakawa, M.; Funatsu, K. Applicability domains and accuracy of prediction of soft sensor models AIChE J. 2011, 57, 1506-1513 10.1002/aic.12351
    • (2011) AIChE J. , vol.57 , pp. 1506-1513
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 80
    • 84883613892 scopus 로고    scopus 로고
    • Estimation of predictive accuracy of soft sensor models based on data density
    • Kaneko, H.; Funatsu, K. Estimation of predictive accuracy of soft sensor models based on data density Chemom. Intell. Lab. Syst. 2013, 128, 111-117 10.1016/j.chemolab.2013.08.005
    • (2013) Chemom. Intell. Lab. Syst. , vol.128 , pp. 111-117
    • Kaneko, H.1    Funatsu, K.2
  • 82
    • 84919797914 scopus 로고    scopus 로고
    • Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development
    • Fan, M.; Ge, Z.; Song, Z. Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development Ind. Eng. Chem. Res. 2014, 53, 19979-19986 10.1021/ie5029864
    • (2014) Ind. Eng. Chem. Res. , vol.53 , pp. 19979-19986
    • Fan, M.1    Ge, Z.2    Song, Z.3
  • 83
    • 84859911625 scopus 로고    scopus 로고
    • Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models
    • Xie, X.; Shi, H. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models Ind. Eng. Chem. Res. 2012, 51, 5497-5505 10.1021/ie202720y
    • (2012) Ind. Eng. Chem. Res. , vol.51 , pp. 5497-5505
    • Xie, X.1    Shi, H.2
  • 87
    • 0037110983 scopus 로고    scopus 로고
    • A modular simulation package for fed-batch fermentation: Penicillin production
    • Birol, G.; Ündey, C.; Cinar, A. A modular simulation package for fed-batch fermentation: penicillin production Comput. Chem. Eng. 2002, 26, 1553-1565 10.1016/S0098-1354(02)00127-8
    • (2002) Comput. Chem. Eng. , vol.26 , pp. 1553-1565
    • Birol, G.1    Ündey, C.2    Cinar, A.3
  • 88
    • 34547147425 scopus 로고    scopus 로고
    • Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data
    • Zhao, C.; Wang, F.; Gao, F.; Lu, N.; Jia, M. Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data Ind. Eng. Chem. Res. 2007, 46, 4943-4953 10.1021/ie061320f
    • (2007) Ind. Eng. Chem. Res. , vol.46 , pp. 4943-4953
    • Zhao, C.1    Wang, F.2    Gao, F.3    Lu, N.4    Jia, M.5
  • 89
    • 44349144443 scopus 로고    scopus 로고
    • Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data
    • Zhao, C.; Wang, F.; Mao, Z.; Lu, N.; Jia, M. Adaptive monitoring based on independent component analysis for multiphase batch processes with limited modeling data Ind. Eng. Chem. Res. 2008, 47, 3104-3113 10.1021/ie701680y
    • (2008) Ind. Eng. Chem. Res. , vol.47 , pp. 3104-3113
    • Zhao, C.1    Wang, F.2    Mao, Z.3    Lu, N.4    Jia, M.5
  • 90
    • 58149308461 scopus 로고    scopus 로고
    • Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction
    • Zhao, C.; Wang, F.; Gao, F. Improved calibration investigation using phase-wise local and cumulative quality interpretation and prediction Chemom. Intell. Lab. Syst. 2009, 95, 107-121 10.1016/j.chemolab.2008.09.003
    • (2009) Chemom. Intell. Lab. Syst. , vol.95 , pp. 107-121
    • Zhao, C.1    Wang, F.2    Gao, F.3
  • 91
    • 84875048235 scopus 로고    scopus 로고
    • Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares
    • Hu, Y.; Ma, H.; Shi, H. Enhanced batch process monitoring using just-in-time-learning based kernel partial least squares Chemom. Intell. Lab. Syst. 2013, 123, 15-27 10.1016/j.chemolab.2013.02.004
    • (2013) Chemom. Intell. Lab. Syst. , vol.123 , pp. 15-27
    • Hu, Y.1    Ma, H.2    Shi, H.3
  • 92
    • 84919445476 scopus 로고    scopus 로고
    • Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process
    • Jin, H.; Chen, X.; Yang, J.; Wu, L.; Wang, L. Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process ISA Trans. 2014, 53, 1822-1837 10.1016/j.isatra.2014.08.015
    • (2014) ISA Trans. , vol.53 , pp. 1822-1837
    • Jin, H.1    Chen, X.2    Yang, J.3    Wu, L.4    Wang, L.5


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