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The statistical framework to systematically detect mean stationarity in the context of continuous manufacturing is described in this article. The methods presented in this article use econometric and financial time‐series analysis concepts in the form of unit‐root and stationarity hypothesis tests. The tests under discussion are the augmented Dickey‐Fuller, Philips‐Perron, Leybourne‐McCabe, and Kwiatkowski‐Phillips‐Schmidt‐Shin. These hypothesis tests are evaluated on data generated by a focused‐beam reflectance measurement sensor implemented on‐line in a continuous plug‐flow crystallizer. This contribution has shown that the hypothesis tests can be used to detect steady‐state conditions on‐line in a plug‐flow crystallizer. Furthermore, this econometric framework can be used as a mean stationarity “certificate” of collected samples to document that the process was mean stationary during the sampling. The statistical framework described in this article can be applied to any continuously operated unit operation or sensor measurement. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2426–2437, 2018 [ABSTRACT FROM AUTHOR]

Abstract: This article investigates monitoring technologies, which provide systematic solutions for nucleation detection based on external bulk video imaging (BVI). The methods under investigation rely on multivariate image analysis, image feature descriptors and statistical control charts (SPCs). For the design of SPCs the video information is transformed into time series. The application of SPCs may be hindered by autocorrelated time series, which show oscillatory patterns due to light reflections from the stirrer blades; however, the autocorrelation can be reduced by performing operations with the first principal component (PC1) of the captured color image or by stacking the frames based on the dominating frequency. Another option is to design digital signal filters in the frequency domain to decrease the autocorrelation of the time series. It was found that the fastest methods for nucleation onset detection were the monitoring in the principal score space and control chart based monitoring of the mean gray intensity of the PC1 images sampled at 25Hz. Furthermore, it was observed that performing principal component analysis (PCA) calculations on multidimensional or multispectral information not only provides the combination of variables that explain most of the variance at a certain time instance but also decreases the autocorrelation of the resulting time series. For acoustic signal based monitoring the gray scale images were converted into a 2 channel stereo sound. It was found that this method has less performant nucleation onset detection capabilities compared to the methods which rely directly on the images. [Copyright &y& Elsevier]

Abstract: The purpose of the paper is twofold: it describes the proof of concept of the newly introduced bulk video imaging (BVI) method and it presents the comparison with existing process analytical technologies (PAT) such as focused beam reflectance measurement (FBRM) and ultra-violet visible (UV/vis) spectroscopy. While the latter two sample the system in small volumes closely to the probe, the BVI approach monitors the entire or large parts of the crystallizer volume. The BVI method is proposed as a complementary noninvasive PAT tool and it is shown that it is able to detect the boundaries of the metastable zone with comparable or better performance than the FBRM and UV/vis probes. [Copyright &y& Elsevier]

Crystallization is one of the most important unit operations used for the separation and purification of crystalline solid products. Appropriate design and control of the crystallization process is paramount to produce crystalline products with tailor-made-properties. This paper provides an overview of selected recent developments in the modelling, monitoring and control of crystallization processes. We consider the topics discussed in this review to be enabling technologies for the development of the next generation of crystallization processes with significantly improved predictability, robustness and controllability. [ABSTRACT FROM AUTHOR]