Process systems modelling and applications in granulation: A review

Granulation is one of the fundamental operations in particulate processing and has a very ancient history and widespread use. Much fundamental particle science has occurred in the last two decades to help understand the underlying phenomena. Yet, until recently the development of granulation systems was mostly based on popular practice. The use of process systems approaches to the integrated understanding of these operations is providing improved insight into the complex nature of the processes. Improved mathematical representations, new solution techniques and the application of the models to industrial processes are yielding better designs, improved optimisation and tighter control of these systems. The parallel development of advanced instrumentation and the use of inferential approaches provide real-time access to system parameters necessary for improvements in operation. The use of advanced models to help develop real-time plant diagnostic systems provides further evidence of the utility of process system approaches to granulation processes. This paper highlights some of those aspects of granulation. (c) 2005 Elsevier Ltd. All rights reserved.

[1]  H. Akaike Statistical predictor identification , 1970 .

[2]  James B. Rawlings,et al.  Model identification and control strategies for batch cooling crystallizers , 1994 .

[3]  R C Rowe,et al.  Process control and scale-up of pharmaceutical wet granulation processes: a review. , 2001, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.

[4]  Michael J. Hounslow,et al.  Coupling granule properties and granulation rates in high-shear granulation , 2003 .

[5]  Gabriel I. Tardos,et al.  Computer simulation of wet granulation , 2000 .

[6]  W. Wildeboer,et al.  Design and operation of regime separated granulators , 2002 .

[7]  Paul R. Mort,et al.  Control of agglomerate attributes in a continuous binder-agglomeration process , 2001 .

[8]  Ian T. Cameron,et al.  Optimal control and operation of drum granulation processes , 2004 .

[9]  Colin Thornton,et al.  Numerical simulations of agglomerate impact breakage , 1999 .

[10]  B. Hoomans Granular dynamics of gas-solid two-phase flows , 2000 .

[11]  M. Yekeler,et al.  Determination of the Breakage and Wetting Parameters of Calcite and Their Correlations , 2002 .

[12]  Francis J. Doyle,et al.  Model predictive control of a granulation system using soft output constraints and prioritized control objectives , 2001 .

[13]  B. J. Ennis,et al.  Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review , 2001 .

[14]  Colin Grant,et al.  7th World Congress of Chemical Engineering: A Review , 2005 .

[15]  James D. Litster,et al.  Population balance modelling of drum granulation of materials with wide size distribution , 1995 .

[16]  Colin Thornton,et al.  Discrete particle simulation of agglomerate impact coalescence , 1998 .

[17]  Markus Kraft,et al.  A Monte Carlo methods for identification and sensitivity analysis of coagulation processes , 2004 .

[18]  A. Golovin The Solution of the Coagulation Equation for Raindrops. Taking Condensation into Account , 1963 .

[19]  Justin A. Gantt,et al.  High-shear granulation modeling using a discrete element simulation approach , 2005 .

[20]  Francis J. Doyle,et al.  Inverse problems in population balances: Growth and nucleation from dynamic data , 2002 .

[21]  Lennart Ljung,et al.  Version 5 of the System Identification Toolbox for use with MATLAB - with Object Orientation , 2000 .

[22]  František Štěpánek,et al.  Computer-Aided Product Design: Granule Dissolution , 2004 .

[23]  Ian T. Cameron,et al.  Granulation Process Modeling , 2005 .

[24]  Francis J. Doyle,et al.  Computationally efficient solution of population balance models incorporating nucleation, growth and coagulation: application to emulsion polymerization , 2003 .

[25]  Francis J. Doyle,et al.  Hierarchical multiobjective strategy for particle‐size distribution control , 2003 .

[26]  Kinam Park,et al.  Advances in pharmaceutical materials and processing , 1998 .

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

[28]  J. Baxter,et al.  Three-dimensional particle shape descriptors for computer simulation of non-spherical particulate assemblies , 2004 .

[29]  Vincent Girard,et al.  Granule breakage phenomena in a high shear mixer; influence of process and formulation variables and consequences on granule homogeneity , 2003 .

[30]  James D. Litster,et al.  Scaleup of wet granulation processes: science not art , 2003 .

[31]  Ian T. Cameron,et al.  Evaluation of control strategies for fertiliser granulation circuits using dynamic simulation , 2000 .

[32]  P. C. Kapur,et al.  Coalescence Model for Granulation , 1969 .

[33]  P. C. Kapur Kinetics of granulation by non-random coalescence mechanism , 1972 .

[34]  Torben Schæfer,et al.  Granulation: A Review on Pharmaceutical Wet-Granulation , 1987 .

[35]  Francis J. Doyle,et al.  Population balance PSD model for emulsion polymerization with steric stabilizers , 2003 .

[36]  Lennart Ljung,et al.  System identification toolbox for use with MATLAB , 1988 .

[37]  Caroline Hogue,et al.  Shape representation and contact detection for discrete element simulations of arbitrary geometries , 1998 .

[38]  Stefan Heinrich,et al.  Particle population modeling in fluidized bed-spray granulation—analysis of the steady state and unsteady behavior , 2003 .

[39]  James D. Litster,et al.  Population balance modelling of granulation with a physically based coalescence kernel , 2002 .

[40]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[41]  Michael J. Hounslow,et al.  Development of a predictive high-shear granulation model , 2003 .

[42]  Brian Scarlett,et al.  Population balances for particulate processes - a volume approach. , 2002 .

[43]  M. Hounslow,et al.  A discretized population balance for nucleation, growth, and aggregation , 1988 .

[44]  Edward L Cussler,et al.  Chemical product engineering , 2003 .

[45]  M. Goldschmidt,et al.  Hydrodynamic Modelling of Fluidised Bed Spray Granulation , 2001 .

[46]  Ian T. Cameron,et al.  Solution of Differential—Algebraic Systems Using Diagonally Implicit Runge—Kutta Methods , 1983 .

[47]  Ian T. Cameron,et al.  A new wavelet-based method for the solution of the population balance equation , 2001 .

[48]  Michael J. Hounslow,et al.  Tracer studies of high‐shear granulation: II. Population balance modeling , 2001 .

[49]  Gabriel I. Tardos,et al.  Stability of wet agglomerates in granular shear flows , 1997, Journal of Fluid Mechanics.

[50]  Doraiswami Ramkrishna,et al.  Population Balances: Theory and Applications to Particulate Systems in Engineering , 2000 .

[51]  A. A. Adetayo,et al.  Unifying approach to modeling granule coalescence mechanisms , 1997 .

[52]  A. Randolph,et al.  Theory of Particulate Processes: Analysis and Techniques of Continuous Crystallization , 1971 .

[53]  Octave Levenspiel,et al.  A Monte Carlo treatment for reacting and coalescing dispersed phase systems , 1965 .

[54]  Ian T. Cameron,et al.  Review and future directions in the modelling and control of continuous drum granulation , 2002 .

[55]  Babatunde A. Ogunnaike,et al.  Model-based control of a granulation system , 2000 .

[56]  Kalanadh V.S. Sastry,et al.  Similarity size distribution of agglomerates during their growth by coalescence in granulation or green pelletization , 1975 .

[57]  Doraiswami Ramkrishna,et al.  Solutions of inverse problems in population balances—I. Aggregation kinetics , 1992 .

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

[59]  Michael J. Hounslow,et al.  The Population Balance as a Tool for Understanding Particle Rate Processes , 1998 .

[60]  J. Kuipers,et al.  Discrete element modelling of fluidised bed spray granulation , 2003 .

[61]  Jpk Seville,et al.  On control of particle size distribution in granulation using high-shear mixers , 2004 .

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

[63]  James D. Litster,et al.  Coalescence of deformable granules in wet granulation processes , 2000 .

[64]  Colin Thornton,et al.  Effect of interface energy on the impact strength of agglomerates , 1999 .

[65]  S. Iveson,et al.  Limitations of one-dimensional population balance models of wet granulation processes☆ , 2002 .

[66]  K. Miyanami,et al.  A fuzzy control system of high shear granulation using image processing , 2001 .

[67]  P.A.L. Wauters,et al.  Modelling and mechanisms of granulation , 2001 .

[68]  Ian T. Cameron,et al.  Diagnostic goal driven modelling and simulation of multiscale process systems , 2005, Comput. Chem. Eng..

[69]  Jean-Claude Charpentier,et al.  The triplet "molecular processes-product-process" engineering: the future of chemical engineering ? , 2002 .

[70]  James N. Michaels,et al.  Toward rational design of powder processes , 2003 .

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

[72]  Francis J. Doyle,et al.  Control of particle size distribution described by a population balance model of semibatch emulsion polymerization , 2000 .

[73]  Y Osako,et al.  Feedback control in high shear granulation of pharmaceutical powders. , 2001, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.