Modeling of Semicontinuous Fluid Bed Drying of Pharmaceutical Granules With Respect to Granule Size

Ghijs, Michaël; Schäfer, Elisabeth; Kumar, Ashish; Cappuyns, Philippe; Assche, Ivo Van; Leersnyder, Fien De; Vanhoorne, Valérie; Beer, Thomas De

doi:10.1016/j.xphs.2019.01.013

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…Figure 17b shows that dry granule size increases with LS ratio, screw speed, drying time, and air temperature, and decreases with flow rate. This is in accordance with experiments used for dryer model development [15]. Drying is expected to increase granule strength and lower breakage rate, which leads to a larger size.…”

Section: Scenario Analysis Resultssupporting

confidence: 89%

“…Overall, kernel parameters R 2 , β 0 , and top 1 in the wetting zone and β 0 , S 0 , f prim , µ, and σ in the kneading zone are linearly related to the process setting values in the units given in Table 2. The fluidized bed dryer (fluid bed dryer (FBD)) model consists of prediction of granule batch drying kinetics based on single granule drying kinetics for one dryer cell [15]. The single granule drying kinetics are governed by Stefan diffusion of water vapor through the granule pores, from the source of evaporation to the edge of the granule.…”

Section: Twin-screw Wet Granulatormentioning

confidence: 99%

“…Hereafter, the remainder of the drying time is completed in the drying stage, where the mass flow rate at time t (MFR TSWG (t)) in that cell is zero. In the final emptying stage, the mass transfer rates in Equations (13) and (15) are set to zero, and the change in PSD is calculated by the PBM model based on the residual moisture content of the material. These material property values are thus those perceived at the inlet of the evaluation module.…”

Section: Twin-screw Wet Granulatormentioning

confidence: 99%

“…Specifically, the units involved are feeders, blenders, twin-screw wet granulator (TSWG), fluid bed dryer (FBD), comill, and tablet press. Models for these processes are developed [12][13][14][15][16] and included. Besides these, models for intermediate feeding and blending operations are also included.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

et al. 2019

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In view of growing interest and investment in continuous manufacturing, the development and utilization of mathematical model(s) of the manufacturing line is of prime importance. These models are essential for understanding the complex interplay between process-wide critical process parameters (CPPs) and critical quality attributes (CQAs) beyond the individual process operations. In this work, a flowsheet model that is an approximate representation of the ConsiGma TM -25 line for continuous tablet manufacturing, including wet granulation, is developed. The manufacturing line involves various unit operations, i.e., feeders, blenders, a twin-screw wet granulator, a fluidized bed dryer, a mill, and a tablet press. The unit operations are simulated using various modeling approaches such as data-driven models, semi-empirical models, population balance models, and mechanistic models. Intermediate feeders, blenders, and transfer lines between the units are also simulated. The continuous process is simulated using the flowsheet model thus developed and case studies are provided to demonstrate its application for dynamic simulation. Finally, the flowsheet model is used to systematically identify critical process parameters (CPPs) that affect process responses of interest using global sensitivity analysis methods. Liquid feed rate to the granulator, and air temperature and drying time in the dryer are identified as CPPs affecting the tablet properties.

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Section: Scenario Analysis Resultssupporting

confidence: 89%

Section: Twin-screw Wet Granulatormentioning

confidence: 99%

Section: Twin-screw Wet Granulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

et al. 2019

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“…In models with many interacting factors such as fluid bed drying, sufficiently detailed data is necessary to calibrate and validate the model, to ensure that the final model is the correct representation of the process. As such, current drying models do predict a moisture content distribution in the material (Ghijs et al, 2019;Peglow et al, 2007;Wang et al, 2007). These arise from fluidisation behaviour, the continuous filling of a dryer cell and the heterogeneity in granule size encountered in pharmaceutical granules.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging

Ghijs

Vanbillemont

Nicolaï

et al. 2021

International Journal of Pharmaceutics

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Loss-on-Drying Prediction for a Vibrated Fluidised Bed Dryer by Means of Mass and Energy Balances

Kiricenko,

Hartmann,

Altmeyer

et al. 2023

J Pharm Innov

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Purpose Continuous wet granulation and drying require an adequate process control strategy to ensure the product quality. The most important critical quality attributes of dried granules are the granule size distribution and moisture content. Process analytical technologies (PATs) are available for real-time monitoring of moisture content by, e.g., near-infrared spectroscopy (NIRS), which requires additional installation and complex multivariate validation. Thus, a mass and energy balance (MEB) was derived for a vibrated fluidised bed dryer, which is part of the QbCon® 1 intended for continuous wet granulation and drying. Method Process parameters that are frequently logged were used for the derivation of a MEB. The predicted MEB was compared with the measured loss-on-drying (LOD) for two different formulations. Results The model-derived data were in good agreement with the observed LOD, leading to RMSE values of 0.12–0.45. Conclusion The implemented MEB can predict the LOD over time and thus might be suitable as a soft sensor without the installation of additional sensors. The obtained energy flux gives insight into the heat transfer, and the derived energy balance might be used to determine the required energy under certain drying conditions.

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Modeling of Semicontinuous Fluid Bed Drying of Pharmaceutical Granules With Respect to Granule Size

Cited by 22 publications

References 28 publications

Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging

Loss-on-Drying Prediction for a Vibrated Fluidised Bed Dryer by Means of Mass and Energy Balances

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