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Volumn 41, Issue , 2012, Pages 134-144

A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses

Author keywords

Batch process; Bayesian inference; Fed batch penicillin cultivation process; Process and measurement uncertainty; Soft sensor; Support vector regression

Indexed keywords

BATCH PROCESS; BAYESIAN INFERENCE; CULTIVATION PROCESS; MEASUREMENT UNCERTAINTY; SOFT SENSORS; SUPPORT VECTOR REGRESSION (SVR);

EID: 84859392648     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2012.03.004     Document Type: Article
Times cited : (139)

References (46)
  • 1
    • 33747873973 scopus 로고    scopus 로고
    • Bioprocess control: Advances and challenges
    • Alford J.S. Bioprocess control: Advances and challenges. Computers and Chemical Engineering 2006, 30:1464-1475.
    • (2006) Computers and Chemical Engineering , vol.30 , pp. 1464-1475
    • Alford, J.S.1
  • 2
    • 0034661658 scopus 로고    scopus 로고
    • Soft sensors development for on-line bioreactor state estimation
    • Assis A.J., Filho R.M. Soft sensors development for on-line bioreactor state estimation. Computers and Chemical Engineering 2000, 24:1099-1103.
    • (2000) Computers and Chemical Engineering , vol.24 , pp. 1099-1103
    • Assis, A.J.1    Filho, R.M.2
  • 5
    • 0037110983 scopus 로고    scopus 로고
    • A modular simulation package for fed-batch fermentation: Penicillin production
    • Birol G., Ündey C., Çinar A. A modular simulation package for fed-batch fermentation: Penicillin production. Computers and Chemical Engineering 2002, 26:1553-1565.
    • (2002) Computers and Chemical Engineering , vol.26 , pp. 1553-1565
    • Birol, G.1    Ündey, C.2    Çinar, A.3
  • 7
    • 70350657090 scopus 로고    scopus 로고
    • Integrated estimation of measurement error with empirical process modeling-A hierarchical Bayes approach
    • Chen H., Bakshi B.R., Goel P.K. Integrated estimation of measurement error with empirical process modeling-A hierarchical Bayes approach. AIChE Journal 2009, 55:2883-2895.
    • (2009) AIChE Journal , vol.55 , pp. 2883-2895
    • Chen, H.1    Bakshi, B.R.2    Goel, P.K.3
  • 10
    • 33745482610 scopus 로고    scopus 로고
    • Improvement of bioprocess monitoring: Development of novel concepts
    • Clementschitsch F., Bayer K. Improvement of bioprocess monitoring: Development of novel concepts. Microbial Cell Factories 2006, 5:1-11.
    • (2006) Microbial Cell Factories , vol.5 , pp. 1-11
    • Clementschitsch, F.1    Bayer, K.2
  • 11
    • 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. Biochemical Engineering Journal 2006, 27:225-239.
    • (2006) Biochemical Engineering Journal , vol.27 , pp. 225-239
    • Desai, K.1    Badhe, Y.2    Tambe, S.S.3    Kulkarni, B.D.4
  • 12
    • 0000264253 scopus 로고    scopus 로고
    • Dynamical modeling, analysis, monitoring and control design for nonlinear bioprocesses
    • Dochain D., Perrier M. Dynamical modeling, analysis, monitoring and control design for nonlinear bioprocesses. Advances in Biochemical Engineering 1997, 56:149-197.
    • (1997) Advances in Biochemical Engineering , vol.56 , pp. 149-197
    • Dochain, D.1    Perrier, M.2
  • 13
    • 0032188415 scopus 로고    scopus 로고
    • Nonlinear inferential control for process applications
    • Doyle F.J. Nonlinear inferential control for process applications. Journal of Process Control 1998, 8:339-353.
    • (1998) Journal of Process Control , vol.8 , pp. 339-353
    • Doyle, F.J.1
  • 14
    • 11144267998 scopus 로고    scopus 로고
    • Neural network-based software sensor: Training set design and application to a continuous pulp digester
    • Dufour P., Bhartiya S., Dhurjati P.S., Doyle F.J. Neural network-based software sensor: Training set design and application to a continuous pulp digester. Control Engineering Practice 2005, 13:135-143.
    • (2005) Control Engineering Practice , vol.13 , pp. 135-143
    • Dufour, P.1    Bhartiya, S.2    Dhurjati, P.S.3    Doyle, F.J.4
  • 17
    • 0003425664 scopus 로고    scopus 로고
    • Support vector machines for classification and regression
    • Gunn S.R. Support vector machines for classification and regression. ISIS Technical Report 1998.
    • (1998) ISIS Technical Report
    • Gunn, S.R.1
  • 18
    • 0040287998 scopus 로고
    • Artificial neural network models of knowledge representation in chemical engineering
    • Hoskins J.C., Himmelblau D.M. Artificial neural network models of knowledge representation in chemical engineering. Computers and Chemical Engineering 1988, 12:881-890.
    • (1988) Computers and Chemical Engineering , vol.12 , pp. 881-890
    • Hoskins, J.C.1    Himmelblau, D.M.2
  • 20
    • 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 Journal 2009, 55:87-98.
    • (2009) AIChE Journal , vol.55 , pp. 87-98
    • Kaneko, H.1    Arakawa, M.2    Funatsu, K.3
  • 21
    • 0033874839 scopus 로고    scopus 로고
    • Inferential control system of distillation compositions using dynamic partial least squares regression
    • Kano M., Miyazaki K., Hasebe S., Hashimoto I. Inferential control system of distillation compositions using dynamic partial least squares regression. Journal of Process Control 2000, 10:157-166.
    • (2000) Journal of Process Control , vol.10 , pp. 157-166
    • Kano, M.1    Miyazaki, K.2    Hasebe, S.3    Hashimoto, I.4
  • 22
    • 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. Computers and Chemical Engineering 2008, 32:12-24.
    • (2008) Computers and Chemical Engineering , vol.32 , pp. 12-24
    • Kano, M.1    Nakagawa, Y.2
  • 23
    • 57249097849 scopus 로고    scopus 로고
    • Dealing with irregular data in soft sensors: Bayesian method and comparative study
    • Khartibisepehr S., Huang B. Dealing with irregular data in soft sensors: Bayesian method and comparative study. Industrial & Engineering Chemistry Research 2008, 47:8713-8723.
    • (2008) Industrial & Engineering Chemistry Research , vol.47 , pp. 8713-8723
    • Khartibisepehr, S.1    Huang, B.2
  • 25
    • 34547487982 scopus 로고    scopus 로고
    • Iterative learning control applied to batch processes: An overview
    • Lee J.H., Lee K.S. Iterative learning control applied to batch processes: An overview. Control Engineering Practice 2007, 15:1306-1318.
    • (2007) Control Engineering Practice , vol.15 , pp. 1306-1318
    • Lee, J.H.1    Lee, K.S.2
  • 27
    • 0026360534 scopus 로고
    • Estimation of distillation compositions from multiple temperature measurements using partial least-squares regression
    • Mejdell T., Skogestad S. Estimation of distillation compositions from multiple temperature measurements using partial least-squares regression. Industrial & Engineering Chemistry Research 1991, 30:2543-2555.
    • (1991) Industrial & Engineering Chemistry Research , vol.30 , pp. 2543-2555
    • Mejdell, T.1    Skogestad, S.2
  • 28
    • 0026646385 scopus 로고
    • Enhancing bioprocess operability with generic software sensors
    • Montague G.A., Morris A.J., Tham M.T. Enhancing bioprocess operability with generic software sensors. Journal of Biotechnology 1992, 25:183-201.
    • (1992) Journal of Biotechnology , vol.25 , pp. 183-201
    • Montague, G.A.1    Morris, A.J.2    Tham, M.T.3
  • 31
    • 0026853320 scopus 로고
    • Nonlinear PLS modeling using neural networks
    • Qin S.J., McAvoy T. Nonlinear PLS modeling using neural networks. Computers and Chemical Engineering 1992, 16(4):379-391.
    • (1992) Computers and Chemical Engineering , vol.16 , Issue.4 , pp. 379-391
    • Qin, S.J.1    McAvoy, T.2
  • 32
    • 0031139929 scopus 로고    scopus 로고
    • Self-validating inferential sensors with application to air emission monitoring
    • Qin S.J., Yue H., Dunia R. Self-validating inferential sensors with application to air emission monitoring. Industrial & Engineering Chemistry Research 1997, 36:1675-1685.
    • (1997) Industrial & Engineering Chemistry Research , vol.36 , pp. 1675-1685
    • Qin, S.J.1    Yue, H.2    Dunia, R.3
  • 36
    • 0024905756 scopus 로고
    • A neural network methodology for process fault diagnosis
    • Venkatasubramanian V., Chan K. A neural network methodology for process fault diagnosis. AIChE Journal 1989, 35:1993-2002.
    • (1989) AIChE Journal , vol.35 , pp. 1993-2002
    • Venkatasubramanian, V.1    Chan, K.2
  • 37
    • 2342567014 scopus 로고    scopus 로고
    • Soft sensing modeling based on support vector machine and Bayesian model selection
    • Yan W., Shao H., Wang X. Soft sensing modeling based on support vector machine and Bayesian model selection. Computers and Chemical Engineering 2004, 28:1489-1498.
    • (2004) Computers and Chemical Engineering , vol.28 , pp. 1489-1498
    • Yan, W.1    Shao, H.2    Wang, X.3
  • 38
    • 79958703777 scopus 로고    scopus 로고
    • Localized Fisher discriminant analysis based complex process monitoring
    • Yu J. Localized Fisher discriminant analysis based complex process monitoring. AIChE Journal 2011, 57:1817-1828.
    • (2011) AIChE Journal , vol.57 , pp. 1817-1828
    • Yu, J.1
  • 39
    • 79952591121 scopus 로고    scopus 로고
    • Nonlinear bioprocess monitoring based multiway kernel localized Fisher discriminant analysis
    • Yu J. Nonlinear bioprocess monitoring based multiway kernel localized Fisher discriminant analysis. Industrial & Engineering Chemistry Research 2011, 50:3390-3402.
    • (2011) Industrial & Engineering Chemistry Research , vol.50 , pp. 3390-3402
    • Yu, J.1
  • 40
    • 81055156706 scopus 로고    scopus 로고
    • A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes
    • Yu J. A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes. Chemical Engineering Science 2012, 68:506-519.
    • (2012) Chemical Engineering Science , vol.68 , pp. 506-519
    • Yu, J.1
  • 41
    • 47549099484 scopus 로고    scopus 로고
    • Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models
    • Yu J., Qin S.J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal 2008, 54:1811-1829.
    • (2008) AIChE Journal , vol.54 , pp. 1811-1829
    • Yu, J.1    Qin, S.J.2
  • 42
    • 70349329819 scopus 로고    scopus 로고
    • Multiway Gaussian mixture model based multiphase batch process monitoring
    • Yu J., Qin S.J. Multiway Gaussian mixture model based multiphase batch process monitoring. Industrial & Engineering Chemistry Research 2009, 48:8585-8594.
    • (2009) Industrial & Engineering Chemistry Research , vol.48 , pp. 8585-8594
    • Yu, J.1    Qin, S.J.2
  • 43
    • 70749091622 scopus 로고    scopus 로고
    • Variance component analysis based fault diagnosis of multi-layer overlay lithography processes
    • Yu J., Qin S.J. Variance component analysis based fault diagnosis of multi-layer overlay lithography processes. IIE Transactions 2009, 41:764-775.
    • (2009) IIE Transactions , vol.41 , pp. 764-775
    • Yu, J.1    Qin, S.J.2
  • 44
    • 4444289955 scopus 로고    scopus 로고
    • Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis
    • Zamprogna E., Barolo M., Seborg D.E. Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis. Journal of Process Control 2005, 15:39-52.
    • (2005) Journal of Process Control , vol.15 , pp. 39-52
    • Zamprogna, E.1    Barolo, M.2    Seborg, D.E.3
  • 45
    • 0141642162 scopus 로고    scopus 로고
    • Integrated condition monitoring and control of fed-batch fermentation processes
    • Zhang H., Lennox B. Integrated condition monitoring and control of fed-batch fermentation processes. Journal of Process Control 2004, 14:41-50.
    • (2004) Journal of Process Control , vol.14 , pp. 41-50
    • Zhang, H.1    Lennox, B.2
  • 46
    • 40449133038 scopus 로고    scopus 로고
    • Nonlinear multivariate quality estimation and prediction based on kernel partial least squares
    • Zhang X., Yan W., Shao H. Nonlinear multivariate quality estimation and prediction based on kernel partial least squares. Industrial & Engineering Chemistry Research 2008, 47:1120-1131.
    • (2008) Industrial & Engineering Chemistry Research , vol.47 , pp. 1120-1131
    • Zhang, X.1    Yan, W.2    Shao, H.3


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