Kinetics of fluidised bed melt granulation I: The effect of process variables

Tan, H.S.; Salman, Agba D.; Hounslow, Michael J.

doi:10.1016/j.ces.2005.09.012

Cited by 99 publications

(77 citation statements)

References 19 publications

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“…From the marginal densities for all four settings, it is apparent that parameters such as the reaction rate constantx 5 are neither multimodal nor correlated with other parameters. Hence, it is likely that unique estimates for its values can be found, but it has to be kept in mind that any marginalization is an averaging operation that may remove higher-order correlations, etc.…”

Section: Parameter Estimation Resultsmentioning

confidence: 96%

The inverse problem in granulation modeling—Two different statistical approaches

Braumann¹,

Man²,

Kraft³

2011

AIChE Journal

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This article is concerned with parameter estimation for a multidimensional population balance model for granulation. Experimental results were obtained by running a laboratory mixer with sodium carbonate and aqueous polyethylene glycol solutions. Subsequently, a prescan of suitable parameter combinations utilising the experimental results is performed, and a local surrogate model constructed around the best combination. For the actual estimation of the parameters and their uncertainties two different approaches are applied-a projection method and a Bayesian approach. It is found that the model predictions with the parameters obtained through both methods are similar. Furthermore, the uncertainties in the model predictions increase as the experimental uncertainties are increased. Studies of the marginal densities of two-parameter combinations obtained through the Bayesian approach show a correlation between the collision and breakage rate constant, giving potential hints for further model development. Furthermore, a bimodal distribution of the compaction rate constant is observed.

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Section: Parameter Estimation Resultsmentioning

confidence: 96%

The inverse problem in granulation modeling—Two different statistical approaches

Braumann¹,

Man²,

Kraft³

2011

AIChE Journal

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“…In this paper, the binder spreading time in the context of fluidized bed granulation process is analyzed using an experimentally verified computational fluid dynamic (Tan et al, 2006) (Tan et al, 2006) c Obtained from Pont et al (2001) d Obtained from Rajnaik et al (2007) …”

Section: Discussionmentioning

confidence: 99%

Time scale analysis for fluidized bed melt granulation-II: Binder spreading rate

Chua

Makkawi

Hewakandamby

et al. 2011

Chemical Engineering Science

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The spreading time of liquid binder droplet on the surface a primary particle is analysed for Fluidized Bed Melt Granulation (FBMG). As discussed in the first paper of this series (Chua et al., 2010a) the droplet spreading rate has been identified as one of the important parameters affecting the probability of particles aggregation in FBMG. In this paper, the binder droplet spreading time has been estimated using Computational Fluid Dynamic modeling (CFD) based on Volume of Fluid approach (VOF). A simplified analytical solution has been developed and tested to explore its validity for predicting the spreading time. For the purpose of models validation, the droplet spreading evolution was recorded using a high speed video camera. Based on the validated model, a generalized correlative equation for binder spreading time is proposed. For the operating conditions considered here, the spreading time for Polyethylene Glycol (PEG1500) binder was found to fall within the range of 10 -2 to 10 -5 s. The study also included a number of other common binders used in FBMG. The results obtained here will be further used in paper III, where the binder solidification rate is discussed.

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“…12 The initiating mechanism in terms of agglomeration is droplet deposition or wetting of the particle surface. Then, the fluidized bed movement forces the particles to collide.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the kinetics of fluidized bed spray agglomeration based on stochastic methods

2011

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in Wiley Online Library (wileyonlinelibrary.com).Particle formation during fluidized bed spray agglomeration is modeled by a Monte Carlo method. The methodology is based on the micromechanisms occurring within the bed. The most important model parameters are identified as interparticle collision time, deposited droplet drying time and droplet addition time. It is found that a high number of collisions leads to a negligible role of the drying mechanism. In the real bed, however, the process is dependent on the gas inlet temperature. This indicates that the number of collisions relevant to agglomeration is relatively low. The accordance of the model with experimental results for variations of several process parameters demonstrates that the approach is a promising way to simulate the formation of agglomerates. In addition, the model is able to reproduce slower agglomeration at increased temperatures. This result is, for the first time, based on physical mechanisms rather than on the use of fitted agglomeration kernels.

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Kinetics of fluidised bed melt granulation I: The effect of process variables

Cited by 99 publications

References 19 publications

The inverse problem in granulation modeling—Two different statistical approaches

The inverse problem in granulation modeling—Two different statistical approaches

Time scale analysis for fluidized bed melt granulation-II: Binder spreading rate

Investigation of the kinetics of fluidized bed spray agglomeration based on stochastic methods

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