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Particle processing industries, such as pharmaceutical, food processing and consumer goods sectors, increasingly require strategies to control and engineer particle attributes. In both traditional batch and continuous processes, particle size and shape need to be effectively monitored through in-line measurements from Process Analytical Technologies. However, obtaining quantitative information from these measurements has proven to be challenging and in-line imaging techniques are primarily used for qualitative purposes. Two key challenges are: (1) the presence of out-of-focus objects and (2) images only represent 2D projections of three-dimensional objects. In this work, a novel framework to process frames from in-line imaging probes incorporates a focus evaluation step in order to extract meaningful quantitative shape and size information through rejection of out-of-focus particles. Furthermore, a model is proposed that simulates the 2D projection of three-dimensional particles onto the focal plane and computes the corresponding size and shape distributions. The framework is quantified and evaluated against standard particles of well-defined size and shape such as polystyrene microspheres and needle-like cuboid silicon particles. [ABSTRACT FROM AUTHOR]

Efficient processing of particulate products across various manufacturing steps requires that particles possess desired attributes such as size and shape. Controlling the particle production process to obtain required attributes will be greatly facilitated using robust algorithms providing the size and shape information of the particles from in situ measurements. However, obtaining particle size and shape information in situ during manufacturing has been a big challenge. This is because the problem of estimating particle size and shape (aspect ratio) from signals provided by in-line measuring tools is often ill posed, and therefore it calls for appropriate constraints to be imposed on the problem. One way to constrain uncertainty in estimation of particle size and shape from in-line measurements is to combine data from different measurements such as chord length distribution (CLD) and imaging. This paper presents two different methods for combining imaging and CLD data obtained with in-line tools in order to get reliable estimates of particle size distribution and aspect ratio, where the imaging data is used to constrain the search space for an aspect ratio from the CLD data. [ABSTRACT FROM AUTHOR]

Fouling or encrustation is a significant problem in continuous crystallisation processes where crystal deposits at surfaces impede heat transfer, increase flow resistance and reduce product quality. This paper proposes an automatic algorithm to detect early stages of fouling using images of vessel surfaces from commodity cameras. Statistical analysis of the pixel intensity variation offers the ability to distinguish appearance of crystals in the bulk solution and on the crystalliser walls. This information is used to develop a fouling metric indicator and determine separately induction times for appearance of first crystals at the surfaces and in the bulk. A method to detect process state changes using Bayesian online change point detection is also proposed, where the first change point is used to determine induction time either at the surface or in the bulk, based on real-time online measurements without using any predetermined threshold which usually varies between experiments and depends on data acquisition equipment. This approach can be used for in situ monitoring of early signs of encrustation to allow early warning for corrective actions to be taken when operating continuous crystallisation processes. [ABSTRACT FROM AUTHOR]