Monitoring a complex refining process using multivariate statistics

Over the past decade, multivariate statistical process control (MSPC) methods have been proven, in the process industries, to be an effective tool for process monitoring, modelling and fault detection. This paper describes the development of a real-time monitoring solution for a complex petroleum refining process with an installed multivariable model predictive controller. The developed solution was designed to track the time-varying and non-stationary dynamics of the process and for improved isolation capabilities, a multiblock approach was applied. The paper highlights the systematic and generic approach that was followed to develop the monitoring solution and stresses the importance of exploiting the knowledge of experienced plant personnel when developing any such system.

[1]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[2]  Yale Zhang,et al.  Industrial application of multivariate SPC to continuous caster start-up operations for breakout prevention , 2006 .

[3]  Michael S. Dudzic,et al.  An industrial perspective on implementing on-line applications of multivariate statistics , 2004 .

[4]  B Lennox,et al.  Process monitoring of an industrial fed-batch fermentation. , 2001, Biotechnology and bioengineering.

[5]  L. Eriksson.  Multi- and megavariate data analysis , 2006 .

[6]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[7]  Johan Trygg,et al.  Multi- and Megavariate Data Analysis : Part II: Advanced Applications and Method Extensions , 2006 .

[8]  Barry Lennox,et al.  Real-time monitoring of an industrial batch process , 2006, Comput. Chem. Eng..

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

[10]  Weihua Li,et al.  Recursive PCA for Adaptive Process Monitoring , 1999 .

[11]  Honglu Yu,et al.  Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods , 2003 .

[12]  Ronald K. Pearson,et al.  Outliers in process modeling and identification , 2002, IEEE Trans. Control. Syst. Technol..

[13]  K. Srinivasan,et al.  Computer-aided design and analysis for a class of nonlinear systems , 1990, IEEE Control Systems Magazine.

[14]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

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

[16]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[17]  Uwe Kruger,et al.  Recursive partial least squares algorithms for monitoring complex industrial processes , 2003 .

[18]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[19]  Philip Jonathan,et al.  Two Projection Methods for Use in the Analysis of Multivariate Process Data With an Illustration in Petrochemical Production , 2004, Technometrics.

[20]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[21]  Theodora Kourti,et al.  Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .

[22]  Dragana P. Brzakovic,et al.  A Practical Assessment of Process Data Compression Techniques , 1998 .

[23]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

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

[25]  Nina F. Thornhill,et al.  The impact of compression on data-driven process analyses , 2004 .

[26]  J. Morris,et al.  Monitoring process manufacturing performance , 2002 .

[27]  David J. Sandoz.  The exploitation of adaptive modeling in the model predictive control environment of connoisseur , 2003 .

[28]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[29]  I. Nimmo,et al.  Adequately address abnormal operations , 1995 .

[30]  S. Wold.  Exponentially weighted moving principal components analysis and projections to latent structures , 1994 .

[31]  Manish H. Bharati,et al.  Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques , 2003 .

[32]  Paul A. Taylor,et al.  Application of feedforward: neural networks and partial least squares regression for modelling kappa number in a continuous Kamyr digester: how multivariate data analysis might help pulping , 1994 .

[33]  Qunxiong Zhu,et al.  Multiscale Nonlinear Principal Component Analysis (NLPCA) and Its Application for Chemical Process Monitoring , 2005 .