Characterization of a small-scale crystallizer using CFD simulations and X-ray CT measurements

Achermann, Ramona; Adams, R.E.; Prasser, Horst-Michael; Mazzotti, Marco

doi:10.1016/j.ces.2022.117697

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Mean induction times for various energy dissipation rates for paracetamol nucleation ( S = 2.27) from aqueous solution for different crystallizers and solution volumes. The energy dissipation rates for the vials with overhead and magnetic stirrers were extracted from the literature . A first-degree polynomial was fitted to the data, and the corresponding fits are shown in the legend.…”

Section: Resultsmentioning

confidence: 99%

Unraveling the Role of Fluid Shear on Primary Nucleation of Paracetamol through an Energy-Dissipation Rate Analysis

Devos,

Xiouras,

Van Gerven

et al. 2024

Crystal Growth & Design

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The effect of fluid shear on the primary nucleation of paracetamol in water, isopropyl alcohol, and acetonitrile was investigated. First, experiments were performed with a Taylor−Couette crystallizer. For all three solvents, a comparable effect of fluid shear on the nucleation kinetics was found: increasing fluid shear results in an increase in the nucleation rate and a decrease in the variability of the induction times. The experimental analysis shows that the mean induction times are dependent on the energy dissipation rate to a power of approximately −1/3. A comparative investigation for paracetamol crystallization in stirred batch crystallizers revealed the same dependence of mean induction times on the energy dissipation rate. Extending this analysis to experimental data sets from the literature confirms this further. To find a physical meaning for this dependency, the results are interpreted in terms of the shear-enhanced mass transfer and shear-enhanced cluster aggregation theories.

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Section: Resultsmentioning

confidence: 99%

Unraveling the Role of Fluid Shear on Primary Nucleation of Paracetamol through an Energy-Dissipation Rate Analysis

Devos,

Xiouras,

Van Gerven

et al. 2024

Crystal Growth & Design

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“…Higher fidelity crystallizer models that take into account imperfect mixing and other transport effects via the use of computational fluid dynamics are available. [372][373][374][375] However, they are subject to the same limitations mentioned above with respect to secondary nucleation and impurity effects.…”

Section: Open Problems In Nucleationmentioning

confidence: 99%

Pharmaceutical Digital Design: From Chemical Structure through Crystal Polymorph to Conceptual Crystallization Process

Burcham,

Doherty,

Peters

et al. 2024

Preprint

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A workflow for the digital design of crystallization processes starting from the chemical structure of the active pharmaceutical ingredient (API) is a multi-step, multi-disciplinary process. A simple version would be to first predict the API crystal structure and from it the corresponding properties of solubility, morphology, and growth rates, assume that the nucleation would be controlled by seeding, and then use these parameters to design the crystallization process. This is usually an over-simplification as most APIs are polymorphic, and the most stable crystal of the API alone may not have the required properties for development into a drug product. This perspective, from the experience of a Lilly Digital Design project, considers the fundamental theoretical basis of crystal structure prediction (CSP), free energy, solubility, morphology and growth rate prediction, and the current state of nucleation simulation. This is illustrated by applying the modeling techniques to real examples, olanzapine and succinic acid. We demonstrate the promise of using ab initio computer modeling for solid form selection and process design in pharmaceutical development. We also identify open problems in the application of current computational modeling and achieving the accuracy required for immediate implementation that are currently limiting the applicability of the approach.

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“…31,32 Second, process conditions may vary within the volume; thermal gradients and hydrodynamic variabilities may be present and may alter the local nucleation rate. 15,33 Primary nucleation is commonly interpreted in the context of the classical nucleation theory (CNT). 1,28 The functional form used in this work is the one used in the experimental reference work by Cedeno et al, 3 namely…”

Section: Three Aspects Of Nucleationmentioning

confidence: 99%

“…We highlight that such averaging is inaccurate under two conditions. First, in the case of heterogeneous nucleation, nucleation events are enabled by nucleation sites on external surfaces. , These sites, however, may be distributed arbitrarily within the system. , Since external surfaces such as crystallizer walls may also act as nucleation sites, the primary nucleation frequency may not scale linearly with volume in real processes. , Second, process conditions may vary within the volume; thermal gradients and hydrodynamic variabilities may be present and may alter the local nucleation rate. , …”

Section: Three Aspects Of Nucleationmentioning

confidence: 99%

Conceptual Validation of Stochastic and Deterministic Methods To Estimate Crystal Nucleation Rates

Deck

Mazzotti

2022

Crystal Growth & Design

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This work presents a generalized framework to assess the accuracy of methods to estimate primary and secondary nucleation rates from experimental data. The crystallization process of a well-studied model compound was simulated by means of a novel stochastic modeling methodology. Nucleation rates were estimated from the simulated data through multiple methods and were compared with the true values. For primary nucleation, no method considered in this work was able to estimate the rates accurately under general conditions. Two deterministic methods that are widely used in the literature were shown to overpredict rates in the presence of secondary nucleation. This behavior is shared by all methods that extract rates from deterministic process attributes, as they are insensitive to primary nucleation if secondary nucleation is sufficiently fast. Two stochastic methods were found to be accurate independent of whether secondary nucleation is present, but they underestimated rates in the case where a large number of primary nuclei are formed. We hence proposed a criterion to probe the accuracy of stochastic methods for arbitrary data sets, thus providing the theoretical foundations required for their rational use. Finally, we showed how both primary and secondary nucleation rates can be inferred from the same set of detection time data by combining deterministic and stochastic considerations.

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Characterization of a small-scale crystallizer using CFD simulations and X-ray CT measurements

Cited by 13 publications

References 26 publications

Unraveling the Role of Fluid Shear on Primary Nucleation of Paracetamol through an Energy-Dissipation Rate Analysis

Unraveling the Role of Fluid Shear on Primary Nucleation of Paracetamol through an Energy-Dissipation Rate Analysis

Pharmaceutical Digital Design: From Chemical Structure through Crystal Polymorph to Conceptual Crystallization Process

Conceptual Validation of Stochastic and Deterministic Methods To Estimate Crystal Nucleation Rates

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