The effect of the impeller speed on the product crystal size distribution (CSD) a 22 liter draft tube (DT) crystallizer

Neumann, Andreas M.; Bermingham, Sean K.; Kramer, Herman J. M.; Rosmalen, G.M. van

doi:10.1016/s0022-0248(98)01193-2

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…In this work, two types of stirrers are considered, propeller and blade stirrer, each with three blades as common for crystallization applications , . If an ideal ratio of stirrer diameter d R to draft tube diameter is to be achieved, which is d R / d dt > 0.89 , a distance of 2 mm between draft tube and stirrer is required.…”

Section: Scale‐down Methodsmentioning

confidence: 99%

Design and Hydrodynamic Characterization of a Draft Tube Baffle Tank for Lab‐Scale

Schmalenberg

Nocon

Kockmann

2020

Chemie Ingenieur Technik

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For process development on lab‐scale, it is necessary to have equipment that represents the industrial apparatuses as similar as possible to offer short time‐to‐market. Accordingly, a draft tube baffle (DTB) crystallizer was scaled down from typical m3‐scale to 1 L filling volume. The suspension characteristics were determined for fluidized crystals in saturated solution. For further characterization of the DTB tank, the residence time of the liquid and solid phases were experimentally determined for the continuous operation mode. Additionally, the classifying behavior of the particles in the DTB was investigated.

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Section: Scale‐down Methodsmentioning

confidence: 99%

Design and Hydrodynamic Characterization of a Draft Tube Baffle Tank for Lab‐Scale

Schmalenberg

Nocon

Kockmann

2020

Chemie Ingenieur Technik

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“…The model framework of Gahn and Mersmann, 16 shown in Figure 4, can be seen as a synthesis of three submodels: 27 (1) a procedure to determine the total number of crystals colliding per second with the faces (29) and edges of the impeller blades and the corresponding impact energy per collision; 14,28 (2) a relation between the impact energy and the attrition volume produced due to a single collision of a crystal corner with a hard, flat surface and the resulting number and size distribution of resulting attrition fragments; 29 and (3) a relation to describe the growth rate of the fragments formed by the attrition. [30][31][32] The calculation of the growth/dissolution within the PBE is based on the following set of equations where For this study, the crystallization kinetics are described using the three aforementioned competing models (see Table 1 for a summary).…”

Section: Mass Balance For Solventmentioning

confidence: 99%

Section: Model Of a Single Compartmentmentioning

confidence: 99%

On the Design of Optimally Informative Experiments for Dynamic Crystallization Process Modeling

Chen

Bermingham

Neumann

et al. 2004

Ind. Eng. Chem. Res.

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In this paper, we present the challenging application of now well-established general and systematic procedures for model development, statistical discrimination, and validation to a published large-scale dynamic crystallization process model. Because of the model's size, this represents, to our knowledge, the first application of such statistical methods to such a largescale dynamic model. For completeness, a brief review of both the model development procedures and the dynamic model are included in the paper. In reviewing the model development procedures, we cover such methods as parametric identifiability testing (to determine whether the parameters, as they appear in the model, can in fact be identified), as well as optimal design of dynamic experiments for both model discrimination among three crystallization models (differing in their kinetics only) and parameter precision improvement within the single "best" dynamic model. Because of the relatively large scale of the model, an optimization-based approach is used for testing of model parameter identifiability that involves semi-infinite programming (SIP) to ensure that the entire control (or input) space has been explored. The problem of designing dynamic experiments is cast as an optimal control problem that enables the calculation of optimal sampling points, experiment durations, fixed and variable external control profiles, and initial conditions of a dynamic experiment subject to general constraints on inputs and outputs. Within this framework, methods are presented to provide experiment design robustness, accounting for parametric uncertainty and subsequently model prediction uncertainty. The paper details the progression of the three crystallization models through the model development procedures and shows the Gahn and Mersmann model (Chem. Eng. Sci. 1999, 54, 1273 to be superior to its competitors.

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“…The SS inhomogeneity in the crystallizer is influenced by incomplete agitation and this affects the CSD by local nucleation . Also, CSD can be affected by primary and secondary nucleation rates . Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Application of Rosin–Rammler model for analysis of CSD in sugar crystallization

Zinab

Alamolhoda

Moghaddam

et al. 2015

Crystal Research and Technology

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Crystal‐size distribution (CSD) is one of the most important parameters in sugar production. The objective is to grow crystals of uniform sizes or narrow CSD. CSD appears to be determined by the growth‐rate history of the crystals and the relative supersaturation (SS) of the solution from which crystals growth takes place. Three methods for preparation of nucleation seeds were described and used for industrial crystallization of raw and white sugars; these are wet milling filtered sugar (ML), agitating saturated solution (AS) and powdered sugars (PD). Rosin–Rammler (RR) and mathematical models were adopted to investigate CSD and the uniformity of the produced crystals. Higher uniformity coefficients were reported for the AS seeded crystals than the other two seeding methods. Furthermore, higher crystal contents were obtained for the AS seeded white sugar batches in comparison.

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The effect of the impeller speed on the product crystal size distribution (CSD) a 22 liter draft tube (DT) crystallizer

Cited by 14 publications

References 7 publications

Design and Hydrodynamic Characterization of a Draft Tube Baffle Tank for Lab‐Scale

Design and Hydrodynamic Characterization of a Draft Tube Baffle Tank for Lab‐Scale

On the Design of Optimally Informative Experiments for Dynamic Crystallization Process Modeling

Application of Rosin–Rammler model for analysis of CSD in sugar crystallization

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