Kinetic models for granule nucleation by the immersion mechanism

Hounslow, Michael J.; Oullion, M.; Reynolds, Gavin K.

doi:10.1016/j.powtec.2008.04.008

Cited by 31 publications

(11 citation statements)

References 18 publications

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“…For a spherical geometry granule, the growth rate in Eq. is insensitive to the critical packing fraction

Φ_{c p}

, as discussed in Hounslow et al…”

Section: Resultsmentioning

confidence: 93%

“…This mechanism is considered as a continuous process to increase solid mass within granule. A model has been developed on the basis of a diffusion phenomenon by Hounslow et al It assumes that the mass of primary particles incorporated into the granule is related to an effective diffusion rate at its surface. For a spherical granule, an approximate analytical solution of this problem is given as

\frac{d m_{s, i}}{d t} = \frac{12 D_{e f f} Φ_{c p}^{2 / 3}}{{(\frac{3 m_{L, i}}{4 π ρ_{L}})}^{2 / 3}} (|, \frac{1 - Φ_{c p}}{Φ_{c p}} \frac{ρ_{L}}{ρ_{S}} m_{L, i} - m_{S, i})

…”

Section: Introductionmentioning

confidence: 99%

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A compartmental CFD‐PBM model of high shear wet granulation

Hounslow

Reynolds

et al. 2016

AIChE Journal

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The conventional, geometrically lumped description of the physical processes inside a high shear granulator is not reliable for process design and scale-up. In this study, a compartmental Population Balance Model (PBM) with spatial dependence is developed and validated in two lab-scale high shear granulation processes using a 1.9L MiPro granulator and 4L DIOSNA granulator. The compartmental structure is built using a heuristic approach based on computational fluid dynamics (CFD) analysis, which includes the overall flow pattern, velocity and solids concentration. The constant volume Monte Carlo approach is implemented to solve the multi-compartment population balance equations. Different spatial dependent mechanisms are included in the compartmental PBM to describe granule growth.It is concluded that for both cases (low and high liquid content), the adjustment of parameters (e.g. layering, coalescence and breakage rate) can provide a quantitative prediction of the granulation process.

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“…For a spherical geometry granule, the growth rate in Eq. is insensitive to the critical packing fraction

Φ_{c p}

, as discussed in Hounslow et al…”

Section: Resultsmentioning

confidence: 93%

\frac{d m_{s, i}}{d t} = \frac{12 D_{e f f} Φ_{c p}^{2 / 3}}{{(\frac{3 m_{L, i}}{4 π ρ_{L}})}^{2 / 3}} (|, \frac{1 - Φ_{c p}}{Φ_{c p}} \frac{ρ_{L}}{ρ_{S}} m_{L, i} - m_{S, i})

…”

Section: Introductionmentioning

confidence: 99%

A compartmental CFD‐PBM model of high shear wet granulation

Hounslow

Reynolds

et al. 2016

AIChE Journal

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show abstract

“…The layering mechanism used is that developed in [30]. The nucleation and rewetting rates are dependent on the liquid binder addition.…”

Section: Population Balance Modelingmentioning

confidence: 99%

Analysis of mesoscale effects in high-shear granulation through a computational fluid dynamics–population balance coupled compartment model

et al. 2018

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In high shear granulation it has been pointed out that there is a need for meso-scale resolution and coupling between flow field information and the evolution of particle properties. In this article we develop a modelling framework that compartmentalizes the high shear granulation process based on process relevant parameters both in time and space. It is built up by a coupled flow field and population balance solver and is used to resolve and analyze the effects of meso-scales on the evolution of particle properties. A Diosna high shear mixer is modelled with micro crystalline cellulose powder as the granulation material. The analysis of the flow field solution and compartmentalization allows for a resolution of the stress and collision peak at the impeller blades. Different compartmentalizations were done showing the importance of resolving the impeller region, both for only aggregating systems and systems with breakage. An investigation on the time evolution of the flow field depending on the changing particle properties was also done, indicating the importance of resolving meso scale phenomena in time as well as space.

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“…The demarcation between these particles is commonly based on some predetermined cut-off value of the agglomerate size, which is then used to distinguish nucleation from granule growth. Therefore, a better definition for nucleation was proposed by Hounslow et al [19]. They defined nucleation as the time period during which ''the frequency of particle-granule collisions is very much greater than the frequency of granule-granule collisions.''…”

Section: Granulation Rate Processesmentioning

confidence: 99%

Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

Matero

Poutiainen

Leskinen

et al. 2010

Journal of Chemometrics

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Fluidized bed wet granulation of a pharmaceutical mixture is an unpredictable and complex process. Batch-to-batch variation, i.e. output material inconsistency, is still an unmanaged problem in fluidized bed granulation. The variation in outcome with different size distributions and yield arises from process conditions, which until now have proved difficult to control. The good quality batch is usually one with a high yield of the desired narrow granule size distribution. Thus, for successful granulation the size distribution needs to be controlled throughout the process in order to prevent the heterogeneity of different batch runs and inconsistent batch products. This paper introduces a method for determining end-product granule size distribution in the early phase of granulation, based on the observation that the quality of the early phase nucleation strongly affects the quality of the end product. In the quantitative model, the information gained from the process stream acquired with an acoustic emission (AE) transducer was combined and modeled using an N-way PLS method. Reference sieve analysis for the granule size distribution was performed off-line and was used as the response variable. AE monitoring is a non-invasive technique for evaluating granulation process performance by detecting the sound of particle impacts. AE caused by the particle-chamber wall interaction is influenced by the size of the granules; thus AE contains information about the granule size distribution. This study shows for the first time that the nucleation phase of granulation can be detected using AE techniques, and thus the possibility of predicting end product granule size distribution by means of AE measurement during the nucleation phase.

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Kinetic models for granule nucleation by the immersion mechanism

Cited by 31 publications

References 18 publications

A compartmental CFD‐PBM model of high shear wet granulation

A compartmental CFD‐PBM model of high shear wet granulation

Analysis of mesoscale effects in high-shear granulation through a computational fluid dynamics–population balance coupled compartment model

Estimation of granule size distribution for batch fluidized bed granulation process using acoustic emission and N‐way PLS

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