Crystal Aggregation in a Flow Tube: Image‐Based Observation

Abstract

The aggregation of crystals within a flow tube was observed based on data extracted from images of the bypassing population. The experiments were conducted under different conditions, namely the flow rate and the particle concentration have been varied simultaneously and two different solvents were used in which the aggregation extent was found to be different under otherwise constant conditions. The analysis of images of bypassing crystals allows for the acquisition of rich datasets both in terms of the variety of shape descriptors and number of particles. This amount of data enables the determination of at least bivariate number distributions of high accuracy with simple histograms. The interpretation of the data is further improved with kernel histograms with which also higher-dimensional volume distributions can be obtained in good quality based on relatively few data points. Indeed, the isosurfaces of 3D distributions turned out to be helpful for inspection of the acquired data.

Crystal Population Growth in a Continuous Helically Coiled Flow Tube Crystallizer

“…1, the HCT used in this study is composed of a coiled-up tube (1) in a cooling bath that is fed from the bottom, an upward coiled tube (2) in air and a downward coiled tube (3) in air. The tubes are made from silicone with wall thickness w. Tube sections (1) and (2) are helically coiled upward on a propylene pipe. Tube section (3) is downward coiled on the outside of tube section (2).…”

“…Continuous crystallization has the advantage of reduced breakage caused by stirring and avoids batch-to-batch variability, although tubular crystallizers may be affected by blocking. Typical continuous tubular setups include straight tubes as presented by Borchert and Sundmacher for crystal aggregation [1], helically coiled flow tubes (HCTs) as studied with respect to flow profiles by Vashisth and Nigam [2], and coiled flow inverters (CFIs) as applied for crystallization by Hohmann et al [3]. The mixing properties of the previously mentioned tubular crystallizers increase from straight tubes to CFIs as shown by Klutz et al [4].…”

Population Balance Equation of Cohesive Particle Flow in a Circulating Fluidized Bed

“…Former research indicates that cohesive particles form agglomerates [4,5] and can be normally fluidized in the form of agglomerates [6]. During fluidization, cohesive particles will form cracks or channels, which makes the aggregation process very complex [7,8], and the understanding of this phenomenon remains limited [9]. It is significant to analyze the aggregation process and to predict the flow behavior of agglomerates for better design and operation of circulating fluidized beds.…”

In-situ crystal morphology identification using imaging analysis with application to the L-glutamic acid crystallization

“…Recently, in-situ measurement and real-time imaging analysis have attracted increasing attentions from industrial and academic communities (Calderon DeAndaetal.,2005a;Wangetal.,2007;Jia et al, 2008;Dang et al, 2009;Borchert and Sundmacher, 2011;Zhou et al, 2011;Zhang et al, 2015), for the sake of monitoring the crystallization process and quality control. Generally, the developed in-situ imaging systems for monitoring crystallization processes could be classified into two groups: invasive and non-invasive imaging systems.…”

“…the process vision and measurement (PVM) instrument made by Mettler Toledo company had been successfully applied to monitor the cooling crystallization (Jia et al, 2008;Dang et al, 2009;Zhou et al, 2009). In contrast, a non-invasive imaging system uses one or more cameras placed outside a crystallizer to monitor crystal size, shape and growth rate, such as the Malvern Sysmex FPIA 3000 (Borchert and Sundmacher, 2011) and the online microscopy systems (Larsen et al, 2007;Wang et al, 2008;Zhang et al, 2015). The use of a non-invasive imaging system can avoid the camera lens or probe surface from being blurred by the crystal slurry to guarantee measurement validity (Borissova et al, 2009;Simone et al, 2015).…”