in Wiley InterScience (www.interscience.wiley.com).A morphological (or polyhedral) population balance (PB) model is presented for modeling the dynamic size evolution of crystals grown from solution in all crystal growth directions. The morphological PB approach uses the crystal shape information for a single crystal obtained from morphology prediction, or experiment as the initial face locations, as well as face growth rates to predict the shape evolution of the crystal population. For each time instant during the crystallization process, the prediction uses its shape at the previous time moment, and the growth rate for the specific crystal habit plane. The methodology is demonstrated through a study of potash alum (KAl (SO 4 ) 2 Á12H 2 O), for which literature data is available for comparison and validation. The discretization method, method of classes, was used to convert the equations to ordinary differential form with the computational domain being discretized into small meshes. The ordinary differential equations ([1.5 million) were then solved simultaneously using the Runge-Kutta-Fehlbergh 4th/5th-order solver with automatic time-step control. The results obtained clearly demonstrate that the morphological PB model developed can predict crystal growth and surface area of individual habit faces in detail, together with crystal shape and size evolution.

No abstract

A significant step forward in recent years, in regard to multivariate statistical process control (MSPC) for operational condition monitoring and fault diagnosis, has been the introduction of principal component analysis (PCA) for the compression of process data. An alternative technique that has been studied more recently for data compression is independent component analysis (ICA). Published work has shown that, in some applications of statistical process monitoring, ICA-based methods have exhibited advantages over those based on other data compression techniques. However, it is inappropriate to use ICA in the same way as PCA to derive Hotelling's T 2 and SPE (squared prediction error) charts, because the independent components are separated by maximizing their non-Gaussianity, whereas the satisfying Gaussian distribution is the basis of T 2 and SPE monitoring charts, as well as univariate statistical process control (SPC) charts. In this paper, we propose a new method for deriving SPC charts based on ICA, which can overcome the aforementioned limitation of non-Gaussianity of the independent components (ICs). The method generates a smaller number of variables, i.e., ICs to monitor, each with time-varying upper and lower control SPC limits, and, therefore, can be used to monitor the evolution of a batch run from one time point to another. The method is illustrated in detail by reference to a simulated semibatch polymerization reactor. To test its capability for generalization, it is also applied to a data set that has been collected from industry and proved to be able to detect all seven faults in a straightforward way. A third case study that was studied in the literature for batch statistical monitoring is used in this work, to compare the performance of the current approach with that of other methods. It proves that the new approach can detect the faults earlier than a similar PCA-based method, the PCA-based T 2 approach, and the SPE approach. Comparison with a recently proposed multi-way ICA method in the literature was also made.

Regulation for nanomaterial is urgently needed and the drive to adopt an intelligent testing strategy is evident. The intelligent testing strategy will not only be beneficial from a cost reduction point of view but will also mean the reduction of the moral and ethical concerns related to animal research. In the chemical and legislative world, such an approach is promoted by REACH and in particular the use of (Q)SAR as a tool for the purpose of categorisation. In addition to traditional compounds, (Q)SAR has also been applied to nanomaterials i.e. nano(Q)SAR, useful to correlate toxicological endpoints with physicochemical properties.Although (Q)SAR in chemicals is well established, nano(Q)SAR is still at an early stage of its development and its successful uptake is far from reality. The purpose of this paper is to identify some of the pitfalls and challenges associated with nano-(Q)SARs, in relation for its use to categorise nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predications from the use of such models. Three major types of barriers were identified: a) the need to improve quality of experimental data in which the models are being developed from in the first place, b) the need to have practical guidelines for the development of the nano(Q)SAR models, c) the need to standardise and harmonise activities for the purpose of regulation. Out of the three barriers, immediate attention is needed for a) as this underpins activities associated in b) and c). It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

Abstract:A synthetic image analysis strategy is proposed for in-situ crystal size measurement and shape identification for monitoring crystallization processes, based on using a real-time imaging system. The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification. Fundamental image features of crystals are selected for efficient classification. In particular, a novel shape feature, referred to as inner distance descriptor, is introduced to quantitatively describe different crystal shapes, which is relatively independent of the crystal size and its geometric direction in an image captured for analysis. Moreover, a pixel equivalent calibration method based on subpixel edge detection and circle fitting is proposed to measure crystal sizes from the captured images. In addition, a kernel function based method is given to deal with nonlinear correlations between multiple features of crystals, facilitating computation efficiency for real-time shape identification.Case study and experimental results from the cooling crystallization of L-glutamic acid demonstrate that the proposed image analysis method can be effectively used for in-situ crystal size measurement and shape identification with good accuracy.