DOE-Based CFD Optimization of Pharmaceutical Mixing Processes

Hörmann, Thomas; Suzzi, Daniele; Adam, Siegfried; Khinast, Johannes G.

doi:10.1007/s12247-012-9142-x

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…13 With the everincreasing availability of computing power, CFD has been growing in popularity in recent years for simulating complex pharmaceutical synthesis reactions saving time and costs versus experimental based approaches. [14][15][16][17] Both DoE and CFD are widely reported in the literature for process optimization; 11,14,[18][19][20] however, they have often been used tangentially, in that CFD is used to provide a possible theoretical justification for the experimental work. 17,[21][22][23] The aim of this work is to evaluate the capabilities of CFD for screening the effect of various experimental conditions on product yield.…”

Section: Introductionmentioning

confidence: 99%

Continuous flow synthesis of a pharmaceutical intermediate: a computational fluid dynamics approach

et al. 2019

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Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space. A point-to-point comparison of the results showed absolute differences in product yield as low as 2.4% yield at low residence times and up to 19.1% yield at high residence times with strong correlation between predicted and experimental percent yields. CFD was found to underestimate the product yields at low residence times and overestimate at higher residence times. The correlation in predicted product yield and the agreement in identifying significant factors in reaction performance reveals the utility of CFD as a valuable tool in the design of continuous flow tube reactors with significantly reduced experimentation. † Electronic supplementary information (ESI) available: (1) Experimental determination of kinetic parameters, (2) molar ratio calculations, (3) CFD transport equations, (4) fluid and material properties, (5) mesh independence study, (6) numerical diffusion analysis, (7) complete CFD yield results, (8) DoE Pareto plots. See

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Section: Introductionmentioning

confidence: 99%

Continuous flow synthesis of a pharmaceutical intermediate: a computational fluid dynamics approach

et al. 2019

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“…Indeed, several recent works have addressed the importance of a local hydrodynamics characterization of dissolution apparatuses, ranging from mini vessels for dissolution testing systems 2 to conical stirred tanks for pharmaceutical processes. 3 Both experimental techniques (e.g., particle image velocimetry, laser doppler anemometry) and computational methods (mainly computational fluid dynamics) providing local data for a whole field fluid dynamics characterization have started being applied, 4 whereas the wider amount of dissolution dynamics data are still mainly based on pointwise experimental methods providing information on single locations. The limitations of single point analysis are well overcame by ERT, which provides information on several locations simultaneously.…”

Section: ■ Introductionmentioning

confidence: 99%

“…It is well-know that solid liquid mass transfer is dominated by particle size and mixing conditions, but robust mass transfer prediction methods for industrial design and optimization purposes are not available yet. Indeed, several recent works have addressed the importance of a local hydrodynamics characterization of dissolution apparatuses, ranging from mini vessels for dissolution testing systems to conical stirred tanks for pharmaceutical processes . Both experimental techniques (e.g., particle image velocimetry, laser doppler anemometry) and computational methods (mainly computational fluid dynamics) providing local data for a whole field fluid dynamics characterization have started being applied, whereas the wider amount of dissolution dynamics data are still mainly based on pointwise experimental methods providing information on single locations.…”

Section: Introductionmentioning

confidence: 99%

Mass Transfer in Dilute Solid–Liquid Stirred Tanks

Carletti

Bikić

Montante

et al. 2018

Ind. Eng. Chem. Res.

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This work deals with solid–liquid mass transfer in dilute solids suspension. The dissolution dynamics of salt particles in water are determined in two baffled tanks of similar geometry and different dimensions stirred by Rushton turbines, Pitched Blade Turbines and Lightnin A310 impellers. The liquid conductivity variation during the salt dissolution is measured locally by electrical resistance tomography. The mass transfer coefficient, k L, that ranges from 2.7 × 10–5 to 1.5 × 10–4 m/s, is obtained from the conductivity time traces as a function of the operating conditions and the stirred tank geometry. Stirring conditions aimed at energy savings are also considered. A dimensionless correlation for the mass transfer coefficient in dilute solid–liquid systems is proposed. It matches fairly well the experimental data collected in this and previous investigations, being the overall estimated error equal to 19% for tanks stirred by Rushton turbines, and its applicability can be easily extended to other impellers.

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“…In addition, the impact of different process factors, including impeller clearance, size, and liquid characteristics, on ow behavior and mixing performance may be investigated with the use of these simulations. [35][36][37][38][39] Scaling the appropriate volume of a stirred tank can pose challenges, particularly when dealing with high-viscosity liquids. This issue arises due to notable changes in ow characteristics observed on the reactor scale.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the impact of different process factors, including impeller clearance, size, and liquid characteristics, on flow behavior and mixing performance may be investigated with the use of these simulations. 35–39…”

Section: Introductionmentioning

confidence: 99%