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Using a series of four case studies, this article illustrates the integration of statistical process control and designed experiments. For such an integration to be effective, this article points out the need to use statistical process control (SPC) as a tool for active process study, rather than simply as a method for maintaining and controlling processes. The use of SPC in this fashion is also illustrated throughout the case studies.

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An statistical process control framework for the characterization of the systematic variability in a historical database of batch pro les is proposed. The framework is geared toward facilitating an understanding of the sources of variability affecting the process. The overall variability in the pro les is categorized into two parts, systematic and unsystematic. The former is further divided, as along the time axis and the measurement axis. Scaling methods are applied to the pro les to obtain scale parameters that characterize the systematic time and measurement axis variability. The pro le scaling is proposed so that each parameter has a very speci c meaning in terms of the type of variability explained. Multivariate SPC charts on the scale parameters and also on the residuals remaining after scaling are developed for process monitoring. Pro les from two simulation examples, a simulated methyl methacralate polymerization reactor and a nylon-6,6 reactor, are used to demonstrate the application of the SPC framework. The examples demonstrate that a systematic study of the correlation structure of the scale parameters can reveal the signature of the primary disturbances affecting the process. Besides providing meaningful scale parameters, the framework also retains the power of projection methods for subtle special cause detection. The demonstration also highlights the importance of using the time variability information for nal product quality predictions in batch data mining.

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