Multivariate statistical process monitoring

Chen, J., Bandoni, A., and Romagnoli, J. A. (1996). Robust PCA and normal region in multivariable statistical process monitoring. AIChE J. 42, 3563-3566. 

E Tatara and A Cinar. An intelligent system for multivariate statistical process monitoring and diagnosis. ISA Trans., 41 255-270, 2002. 

Westerhuis JA, Gurden SP, Smilde AK, Generalized contribution plots in multivariate statistical process monitoring, Chemometrics and Intelligent Laboratory Systems, 2000a, 51, 95-114. 

B.R. Bakshi, Multiscale PCA with Application to Multivariate Statistical Process Monitoring, AlCliE Journal. 44(7) (1998). 1596-1610,... 

Kano, M., Hasebe, S. Hashimoto, I. and Ohno, H., 2001. A new multivariate statistical process monitoring method using principal component analysis. Computer and Chemical Engineering, 25, 1103-1113. 

In this work, we describe an approach to integrate multivariate statistical process monitoring and online HAZOP analysis for abnormal event management of batch processes. The framework consists of three main parts process monitoring and fault detection, automated online HAZOP analysis module and a coordinator. Multiway PCA is used for batch process monitoring and fault detection. When abnormal event is detected, signal-to-symbol transformation technique based on variable contribution is used to transfer quantitative sensor readings to qualitative states. Online HAZOP analysis is based on PHASuite, an automated HAZOP analysis tool, to identify the potential causes, adverse consequences and potential operator options for the identified abnormal event. 

Monitoring and control are crucial tasks in the operation of a batch process. Multivariate Statistical Process Monitoring (MSPM) methods, such as multiway PCA, are becoming popular in recent years for monitoring batch processes. 

Martin, E. B., Morris, A. J., and Zhang, J. (1996). Process performance monitoring using multivariate statistical process control. IEE Proc. Control Theory 143, 132. 

A bioprocess system has been monitored using a multi-analyzer system with the multivariate data used to model the process.27 The fed-batch E. coli bioprocess was monitored using an electronic nose, NIR, HPLC and quadrupole mass spectrometer in addition to the standard univariate probes such as a pH, temperature and dissolved oxygen electrode. The output of the various analyzers was used to develop a multivariate statistical process control (SPC) model for use on-line. The robustness and suitability of multivariate SPC were demonstrated with a tryptophan fermentation. 

Cimander, C. Mandenius, C.-F., Online monitoring of a bioprocess based on a multi-analyser system and multivariate statistical process modelling /. Chem. Technol. Biotechnol. 2002, 77, 1157-1168. 

A. Raich and A. Cinar, 1994, Statistical Process Monitoring and Disturbance Diagnosis in Multivariable Continuous Processes,H/C/ifi 7., Vol. 42, Issue 1,995... 

The book follows a rational presentation structure, starting with the fundamentals of univariate statistical techniques and a discussion on the implementation issues in Chapter 2. After stating the limitations of univariate techniques, Chapter 3 focuses on a number of multivariate statistical techniques that permit the evaluation of process performance and provide diagnostic insight. To exploit the information content of process measurements even further. Chapter 4 introduces several modeling strategies that are based on the utilization of input-output process data. Chapter 5 provides statistical process monitoring techniques for continuous processes and three case studies that demonstrate the techniques. 

A Raich and A Cinar. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE J., 42(4) 995-1009, 1996. 

Kourti, T., and J. F. MacGregor, 1996. Multivariate Statistical Process Control methods for Monitoring, Diagnosing Process and Product Performance. J. Qual. Tech. 28 409-428. 

Tates AA, Louwerse DJ, Smilde AK, Koot GLM, Berndt H, Monitoring a PVC batch process with multivariate statistical process control charts, Industrial and Engineering Chemistry Research, 1999, 38, 4769 1776. 

The emphasis on uncertainty is perhaps more telling for applying the concept of risk to PAT, and as shall be demonstrated, is very much in line with what is intended in monitoring PCCPs with sensors that are distributed throughout a process via SCADA systems that utilize multivariate statistical process control (MSPC) methodologies for controlling these processes. 

Piovoso, M. J., and Kosanovich, K. A., Applications of multivariate statistical methods to process monitoring and controller design, Int. J. Control 59(3), 743-765 (1994). 

One of the most influential books on the subject of PCA was by I.T. Jolliffe [128] who published recently a new edition [129] of his book. The book by Smilde et al. [276] is the most recent contribution to the literature on multivariate statistics, with special emphasis on chemical systems. Two books coauthored by R. Braatz [38, 260] review a number of fault detection and diagnosis techniques for chemical processes. Cinar [41] coauthored a book on monitoring of batch fermentation and fault diagnosis in batch process operations. 

In the era of single-loop control systems in chemical processing plants, there was little infrastructure for monitoring multivariable processes by using multivariate statistical techniques. A limited number of process and quality variables were measured in most plants, and use of univariate SPM tools for monitoring critical process and quality variables seemed appropriate. The installation of computerized data acquisition and storage systems, the availability of inexpensive sensors for typical process variables such as temperature, flow rate, and pressure, and the development of advanced chemical analysis systems that can provide reliable information on quality variables at high frequencies increased the number of variables measured at... 

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