» Download Article

Authors
Sinem Perk, Ali Cinar

Identifier
None

Index Terms
batch monitoring,fault detection and diagnosis,multiblock models

Abstract
Batch process monitoring to detect the existence and magnitude of changes that cause a deviation from the normal operation has gained considerable attention in the last decade. There are some batch processes that occur as a single step, whereas many others include multiple phases due to operational or phenomenological regimes or multiple stages where different processing units are employed. Having a single model for all different phases/stages with different covariance structures may not give a sufficient explanation of the system behavior and fault detection and diagnosis can be more challenging with increasing model size. Multiblock methods have been recently proposed to improve the capabilities of the existing statistical monitoring models. In this study, a multiblock algorithm based on concensus principal component analysis is applied to the benchmark fedbatch penicillin fermentation simulator data. The results of a static multiblock model and a sliding window multiblock model are compared. The need for data synchronization, and the effect of block size are discussed. Multiblock multiway principal component analysis methods are found to be effective in fault detection and localization.

References

[1] Birol, G., C. Ündey and A. Çinar (2002). A modular
     simulation package for fed-batch fermentation:
     penicillin production. Computers and
    Chemical Engineering 26, 1553-1565.
[2] Çinar, A., S.J. Parulekar, C. Ündey and Í. Birol
    (2002). Batch Fermentation: Modeling, Monitoring
     and Control. Marcel Dekker.
[3] Kassidas, A., J.F. MacGregor and P.A. Taylor
    (1998). Synchronization of batch trajectories
    using dynamic time warping. AIChE Journal
    44, 864-875.
[4] Kourti, T., P. Nomikos and J.F. MacGregor
    (1995). Analysis, monitoring and fault diagnosis
     of batch processes using multiblock,
    multiway PLS. Journal of Process Control
    5, 277-284.
[5] Lee, D.S. and P.A. Vanrolleghem (2003). Monitoring
     of a sequencing batch reactor using adaptive
     multiblock principal component analysis.
    Biotechnology and Bioengineering 82, 489-
    497.
[6] MacGregor, J.F., C. Jaeckle, C. Kiparissides and
    M. Koutoudi (1994). Process monitoring and
    diagnosis by multiblock PLS methods. AIChE
    Journal 40, 826-838.
[7] Nomikos, P. and J.F. MacGregor (1994). Monitoring
     batch processes using multiway principal
     component analysis. AIChE Journal
    40, 1361-1375.
[8] Nomikos, P. and J.F. MacGregor (1995a). Multivariate
     SPC charts for batch processes. Technometrics
     37, 41-59.
[9] Nomikos, P. and J.F. MacGregor (1995b). Multiway
     partial least squares in monitoring batch
    processes. Chemometrics and Intelligent Laboratory
     Systems 30, 97-108.
[10] Qin, S.J., S. Valle and M.J. Piovoso (2001). On
     unifying multiblock analysis with application
     to decentralized process monitoring. Journal
     of Chemometrics 15, 715-742.
[11] Rännar, S., J.F. MacGregor and S. Wold (1998).
     Adaptive batch monitoring using hierarchical
     PCA. Chemometrics and Intelligent Laboratory
      Systems 41, 73-81.
[12] Smilde, A.K., J.A. Westerhuis and
     S. de Jong (2003). A framework for sequential
      multiblock component methods. Journal
     of Chemometrics 17, 323-337.
[13] Ündey, C. and A. Çinar (2002). Statistical
     monitoring of multistage, multiphase batch
     processes. IEEE Control Systems Magazine
     22(5), 40-52.
[14] Westerhuis, J.A., T. Kourti and J.F. MacGregor
     (1998). Analysis of multiblock and hierarchical
      PCA and PLS models. Journal of Chemometrics
      12, 301-321.
[15] Wold, S., P. Geladi, K. Esbensen and J. Öhman
     (1987). Multi-way principal components and
     PLS analysis. Journal of Chemometrics
     1, 41-56.