Multivariate Statistical Process Control (MSPC)

Barry Lennox

There has been enormous interest in using MSPC techniques, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), to detect and diagnose abnormal conditions in process systems. Work at Manchester in this field began in the mid-1990s when it was supported by Invensys. In 2002 researchers from the University of Manchester and engineers from Invensys formed the spin-out company, Perceptive Engineering, which now has control and monitoring applications throughout the world.

The research at the University of Manchester has focused on the application of MSPC techniques to large-scale continuous and batch industrial processes. A selection of the case-studies that have been investigated are described in the papers below:

Batch applications:

Marjanovic, O, Lennox, B., Sandoz, D and Smith, K. (2006), ‘Real-time monitoring of an industrial batch process’, Computers and Chemical Engineering, 30 (10-12), 1476-1481

Zhang, H. and Lennox, B., (2003), ‘Integrated condition monitoring and control of fed-batch fermentation processes’, Journal of Process Control, 14 (1), 41-50

Lennox, B., Kipling, K., Glassey, J., and Montague, G., (2002), ‘Automated production support for the bioprocess industry’, Biotechnology Progress, 18 (2), 269-275

Lennox, B., Hiden, H.G., Montague, G.A., Kornfeld, G. and Goulding, P.R., (2001), ‘Process monitoring of an industrial fed-batch fermentation’, Biotechnology and Bioengineering, 74 (2), 125-135

Refining:

Al-Ghazzawi, A and Lennox, B., (2008), ‘Condition monitoring of a complex refining process using multivariate statistical process control methods’, Control Engineering Practice, 16 (3), 294-307

Al-Ghazzawi, A and Lennox, B., (2009), ‘Model predictive control monitoring using multivariate statistics’, Journal of Process Control, 19 (2), 314-327

Monitoring non-linear and time-varying processes:

Al-Ghazzawi, A and Lennox, B., (2008), ‘Condition monitoring of a complex refining process using multivariate statistical process control methods’, Control Engineering Practice, 16 (3), 294-307

Wang, X., Kruger, U. and Lennox, B., (2003), ‘Recursive partial least squares algorithms for monitoring complex industrial processes’, Control Engineering Practice, 11 (6), 613-632

Monitoring the performance of model predictive control system:

Al-Ghazzawi, A and Lennox, B., (2009), ‘Model predictive control monitoring using multivariate statistics’, Journal of Process Control, 19 (2), 314-327

Applications in the steel industry:

Sandberg, E., Lennox, B. and Undvall, P., (2007), ‘Scrap management by statistical evaluation of EAF process data’, Control Engineering Practice, 15(9), 1063-1075

Lennox, B., Sandberg, E. and Marjanovic, O., (2003), ‘Recent experiences in the application of PLS in the steel industry’, PLS’03, Lisbon

Back to Home Page