Stochastic simulation of agglomerate formation in fluidized bed spray drying: A micro-scale approach

Abstract A stochastic model that describes agglomerate growth during fluidized bed spray agglomeration is presented and numerically solved by constant volume Monte Carlo method. The methodology overcomes the difficulties of solving multivariate population balance equations and includes continuous binder addition and drying. Agglomerate formation is treated as a complex combination of consecutive and parallel micro-mechanisms. Due to the discrete nature of the approach, the individual role of the micro-mechanisms on the agglomeration behavior can be analyzed. The results suggest that the droplet capture mechanism governs the agglomeration speed while the maximum agglomerate diameter is ruled by the equilibrium reached between coalescence, rebound and breakage. The mechanism of deposited binder drying is found to play a negligible role on agglomerate formation because of an extremely rapid droplet consumption. The main process variables affecting each micro-mechanism have been identified showing that the liquid spraying rate affects directly the droplet capture mechanism whereas binder properties influence mainly the agglomeration and rebound interactions. The model presented in this study is able to predict qualitatively the experimentally observed response of the system as well as the general shape of the agglomerate size distribution under the variation of several process parameters, demonstrating the potential of the discrete micro-level approach.

[1]  Rohit Ramachandran,et al.  Distribution and accessibility of binder in wet granules , 2009 .

[2]  S. Hogekamp,et al.  Porosity measurement of fragile agglomerates , 2003 .

[3]  J. P. Gooch,et al.  Monte Carlo simulation of size-enlargement mechanisms in crystallization , 1996 .

[4]  M. Pasandideh-Fard,et al.  Capillary effects during droplet impact on a solid surface , 1996 .

[5]  Michael J. Hounslow,et al.  Kinetics of fluidised bed melt granulation: IV. Selecting the breakage model , 2004 .

[6]  W. Peukert,et al.  On the relevance of accounting for the evolution of the fractal dimension in aerosol process simulations , 2003 .

[7]  B. J. Ennis,et al.  A microlevel-based characterization of granulation phenomena , 1991 .

[8]  Michael J. Hounslow,et al.  Kinetics of fluidised bed melt granulation I: The effect of process variables , 2006 .

[9]  Sovirith Tan.  Monte-Carlo simulation of polymers in shear flow , 1999 .

[10]  G. Falope,et al.  On modelling continuous agglomerative crystal precipitation via Monte Carlo simulation , 2001 .

[11]  Frantisek Stepanek,et al.  The effect of primary particle surface energy on agglomeration rate in fluidised bed wet granulation , 2008 .

[12]  Frantisek Stepanek,et al.  A combined experimental and computational study of wet granulation in a Wurster fluid bed granulator , 2009 .

[13]  Themis Matsoukas,et al.  Constant-number Monte Carlo simulation of population balances , 1998 .

[14]  Stefan Heinrich,et al.  An efficient numerical technique for solving population balance equation involving aggregation, breakage, growth and nucleation , 2008 .

[15]  Stefan Heinrich,et al.  Improved accuracy and convergence of discretized population balance for aggregation: The cell average technique , 2006 .

[16]  P. Mort,et al.  Critical parameters and limiting conditions in binder granulation of fine powders , 1997 .

[17]  Pierre Buffière,et al.  Collision frequency and collisional particle pressure in three-phase fluidized beds , 2000 .

[18]  Mehrdji Hemati,et al.  Fluidized bed coating and granulation: influence of process-related variables and physicochemical properties on the growth kinetics , 2003 .

[19]  T. Schaefer,et al.  Agglomerate formation and growth mechanisms during melt agglomeration in a rotary processor. , 2005, International journal of pharmaceutics.

[20]  Cameron Tropea,et al.  Investigations on the impact of a drop onto a small spherical target , 2007 .

[21]  Rosner,et al.  Monte Carlo Simulation of Particle Aggregation and Simultaneous Restructuring. , 1999, Journal of colloid and interface science.

[22]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[23]  G. Cody,et al.  Dependence of particle fluctuation velocity on gas flow, and particle diameter in gas fluidized beds for monodispersed spheres in the Geldart B and A fluidization regimes , 2008 .

[24]  M. Lísal,et al.  Monte Carlo adiabatic simulation of equilibrium reacting systems : The ammonia synthesis reaction , 2005 .

[25]  James D. Litster,et al.  The Science and Engineering of Granulation Processes , 2004 .

[26]  A. Clarke,et al.  Spreading and Imbibition of Liquid Droplets on Porous Surfaces , 2002 .

[27]  D. Gidaspow.  Multiphase Flow and Fluidization: Continuum and Kinetic Theory Descriptions , 1994 .

[28]  Haibo Zhao,et al.  Analysis of four Monte Carlo methods for the solution of population balances in dispersed systems , 2007 .

[29]  Stefan Heinrich,et al.  A generic population balance model for simultaneous agglomeration and drying in fluidized beds , 2007 .

[30]  Glen McHale,et al.  Drop evaporation on solid surfaces: constant contact angle mode , 2002 .

[31]  Doraiswami Ramkrishna,et al.  Analysis of population balance—IV: The precise connection between Monte Carlo simulation and population balances , 1981 .

[32]  Jim R. Jones,et al.  Droplet impact and spreading : Droplet formulation effects , 2007 .

[33]  Doraiswami Ramkrishna,et al.  Simulation of particulate systems using the concept of the interval of quiescence , 1977 .