The integration of spectroscopic and process data for enhanced process performance monitoring

Batch process performance monitoring has been achieved primarily using process measurements with the extracted information being associated with the physical parameters of the process. With increasing attention now being paid to the application of on-line real-time process analytics through spectrometry, together with the FDA Process Analytical Technologies (PAT) initiative, the use of spectroscopic information for enhanced monitoring of reactions is gaining impetus. The harmonious integration of process data and spectroscopic data then becomes a major challenge. By integrating the process and spectroscopic measurements for multivariate statistical data modelling and analysis, it is conjectured that improved process understanding and fault diagnosis can be achieved. An investigation into combining process and spectral data using multiblock and multiresolution analysis is proposed and the results from the analysis of experimental data from two industrial application studies are presented to demonstrate the improvements achievable in terms of process performance monitoring and fault diagnosis. Le suivi de la performance des procedes discontinus s'effectue principalement a l'aide de mesures de procedes et les informations obtenues sont associees aux parametres physiques du procede. Compte tenu de l'attention de plus en plus grande portee maintenant a l'application des techniques analytiques de procedes en temps reel en continu par la spectrometrie, associee a l'initiative des technologies analytiques de procedes (PAT) de la FDA, l'utilisation des informations spectroscopiques pour mieux suivre les reactions gagnent du terrain. L'integration harmonieuse des donnees de procedes et des donnees spectroscopiques devient alors un defi majeur. En integrant les mesures de procedes et de spectroscopie pour la modelisation et l'analyse des donnees statistiques multivariees, on estime pouvoir mieux comprendre les procedes et mieux diagnostiquer les erreurs. Une recherche pour combiner les donnees spectrales et de procedes utilisant l'analyse multibloc et multiresolution est proposee et les resultats de l'analyse de donnees experimentales issues de deux etudes d'application industrielle sont presentes afin de demontrer les ameliorations realisables en matiere de suivi des performances des procedes et de diagnostic des erreurs.

[1]  Pekka Teppola,et al.  Wavelet–PLS regression models for both exploratory data analysis and process monitoring , 2000 .

[2]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[3]  Wojtek J. Krzanowski,et al.  Principal Component Analysis in the Presence of Group Structure , 1984 .

[4]  Murray Rudman,et al.  Composing chaos: An experimental and numerical study of an open duct mixing flow , 2006 .

[5]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[6]  A. J. Morris,et al.  Batch Monitoring Through Common Subspace Models , 2004 .

[7]  Michael J. Piovoso,et al.  On unifying multiblock analysis with application to decentralized process monitoring , 2001 .

[8]  S. Wold,et al.  PLS regression on wavelet compressed NIR spectra , 1998 .

[9]  Gang Chen,et al.  Multi-Block Predictive Monitoring of Continuous Processes , 1997 .

[10]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[11]  S. de Jong,et al.  A framework for sequential multiblock component methods , 2003 .

[12]  Bernd Schmidt,et al.  Combining process and spectroscopic data to improve batch modeling , 2006 .

[13]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[14]  Michael J. Piovoso,et al.  PCA of Wavelet Transformed Process Data for Monitoring , 1997, Intell. Data Anal..

[15]  Age K. Smilde,et al.  Monitoring of Batch Processes using Spectroscopy , 2002 .

[16]  W. Krzanowski.  Between-Groups Comparison of Principal Components , 1979 .

[17]  Svante Wold,et al.  Modelling and diagnostics of batch processes and analogous kinetic experiments , 1998 .

[18]  A. J. Morris,et al.  Multi-Site Performance Monitoring in Batch Pharmaceutical Production , 2004 .

[19]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[20]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[21]  B. Flury.  Common Principal Components in k Groups , 1984 .

[22]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[23]  S. Joe Qin,et al.  Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .

[24]  A. J. Morris,et al.  Performance monitoring of a multi-product semi-batch process , 2001 .

[25]  E. Martin,et al.  Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction. , 2006, Analytical chemistry.

[26]  L. E. Wangen,et al.  A multiblock partial least squares algorithm for investigating complex chemical systems , 1989 .

[27]  S. Mallat.  Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .

[28]  B. Bakshi.  Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[29]  P. Miller,et al.  Contribution plots: a missing link in multivariate quality control , 1998 .

[30]  B. Flury,et al.  Two generalizations of the common principal component model , 1987 .

[31]  A. J. Morris,et al.  Monitoring Performance in Flexible Process Manufacturing , 2004 .