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. 

Gurden et al. studied the monitoring of batch processes using spectroscopy. As a case study, they followed a pseudo first-order biochemical reaction with an intermediate using UV-vis spectroscopy. Following statistical process monitoring, process disturbances could be detected in a series of batches. 

Individuals Control Charts In some chemical and biopharmaceutical manufacturing processes involving lengthy and expensive procedures, it is not feasible to form a sample of size greater than one because only one product or one batch is available each time. When the sample size used for statistical process monitoring is limited to one, individual control charts, I and MR charts, are needed. 

Lin, D. K. J. (2000). Recent developments in supersaturated designs, Chapter 18. In Statistical Process Monitoring and Optimization. Editors S. H. Park and G. G. Vining, pages 305-319. Marcel Dekker, New York. 

Montgomery, D. C., Yatskievitch, M. and Messina, W. S., 2000, Integrating Statistical Process Monitoring with Feedforward Control, Quality and Reliability Engineering International, 16(6), 515-525. 

M. Kano, S. Hasebe, I. Hashimoto and H. Ohno, 2002, Statistical Process Monitoring Based on Dissimilarity of Process Data, AIChEJ.,Vo. 48, No.6, 1231... 

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. 

J-M Lee, CK Yoo, and I-B Lee. Statistical process monitoring with independent components analysis. J. Process Control, 14 467-485, 2004. 

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

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

Box GEP, Kramer T, Statistical process monitoring and feedback adjustment - a discussion, Technometrics, 1992, 34, 251-267. 

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. 

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