Model-based design and control of a continuous drum granulation process

Abstract This paper is concerned with enhanced process design and control of a multiple-input multiple-output (MIMO) granulation process. The work is based on a first-principles mechanistic three-dimensional population balance model (3D-PBM), which has been previously validated against experiments at the laboratory-scale for various operating conditions and formulations. The main objective of this study is via a novel process design, to control and operate the granulation process under more optimal conditions. Novelty of the work lies in the usage of the validated 3D-PBM to extract suitable multiple control-loop pairings from which an overall control loop performance is qualitatively and quantitatively assessed. Results show that for most existing granulation process configurations, enhanced control-loop performance is not achieved and as a result an alternative process design strategy is necessary. The proposed design demonstrates increased efficiency in the control and operation of the granulation process, which is required for further efficient control and operation of subsequent downstream processes.

[1]  Francis J. Doyle,et al.  Experimental validation studies on a multi-dimensional and multi-scale population balance model of batch granulation , 2009 .

[2]  T. Harris,et al.  Performance assessment measures for univariate feedback control , 1992 .

[3]  Vinay Kariwala,et al.  Fundamental limitation on achievable decentralized performance , 2007, Autom..

[4]  Nina F. Thornhill,et al.  Alternative solutions to multi-variate control performance assessment problems q , 2006 .

[5]  V. Manousiouthakis,et al.  Best achievable decentralized performance , 1995, IEEE Trans. Autom. Control..

[6]  Francis J. Doyle,et al.  A feasible solution technique for higher-dimensional population balance models , 2007, Comput. Chem. Eng..

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

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

[9]  Francis J. Doyle,et al.  A mechanistic model for breakage in population balances of granulation: Theoretical kernel development and experimental validation , 2009 .

[10]  Ian T. Cameron,et al.  Process systems modelling and applications in granulation: A review , 2005 .

[11]  Elling W. Jacobsen,et al.  Performance limitations in decentralized control , 2000 .

[12]  Panagiotis D. Christofides,et al.  Predictive control of particle size distribution in particulate processes , 2006 .

[13]  Timothy I. Salsbury.  Continuous-time model identification for closed loop control performance assessment , 2007 .

[14]  Edward S. Meadows,et al.  Integrity of systems under decentralized integral control , 2005, Autom..

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

[16]  F.J. Doyle,et al.  Sensitivity analysis of multi-regime population balance model for control of multiple particulate properties in granulation , 2005, Proceedings of the 2005, American Control Conference, 2005..

[17]  Okko H. Bosgra,et al.  Controllability of particulate processes in relation to the sensor characteristics , 2000 .

[18]  Jeffrey D. Ward,et al.  Plantwide dynamics and control of processes with crystallization , 2010, Comput. Chem. Eng..

[19]  Ian T. Cameron,et al.  Performance assessment and model validation for an industrial granulation circuit , 2007 .

[20]  N. Bianco,et al.  Modelling and Control Considerations for Particle Populations in Particulate Processes Within a Multi-Scale Framework , 2006 .

[21]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[22]  Prashant Mhaskar,et al.  Predictive control of crystal size distribution in protein crystallization , 2005, Nanotechnology.

[23]  Francis J. Doyle,et al.  Solution technique for a multi-dimensional population balance model describing granulation processes , 2005 .

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

[25]  Francis J. Doyle,et al.  Model predictive control of continuous drum granulation , 2009 .

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

[27]  Panagiotis D. Christofides,et al.  Control of Particulate Processes , 2010 .

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

[29]  Paul R. Mort,et al.  Scale-up of binder agglomeration processes , 2005 .

[30]  P. I. Barton,et al.  Efficient sensitivity analysis of large-scale differential-algebraic systems , 1997 .

[31]  P. I. Barton,et al.  Effective parameter estimation within a multi-dimensional population balance model framework , 2010 .

[32]  W. Harmon Ray,et al.  Control of systems described by population balance equations—I. Controllability analysis , 1995 .

[33]  Marianthi G. Ierapetritou,et al.  Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data‐Based Methods , 2012 .

[34]  Rohit Ramachandran.  Multi-scale population balance modelling and controllability of granulation processes , 2008 .

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

[36]  M. Hounslow,et al.  An investigation into the kinetics of liquid distribution and growth in high shear mixer agglomeration , 1998 .

[37]  E. Bristol.  On a new measure of interaction for multivariable process control , 1966 .

[38]  Francis J. Doyle,et al.  Experimental studies on distributions of granule size, binder content and porosity in batch drum granulation: Inferences on process modelling requirements and process sensitivities , 2008 .

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

[40]  Francis J. Doyle,et al.  A three-dimensional population balance model of granulation with a mechanistic representation of the nucleation and aggregation phenomena , 2008 .

[41]  Panagiotis D. Christofides,et al.  Model-Based Control of Particulate Processes , 2002 .

[42]  Nikolaos Kazantzis,et al.  Model reduction and coarse-graining approaches for multiscale phenomena , 2006 .

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

[44]  Francis J. Doyle,et al.  A two-level discretisation algorithm for the efficient solution of higher-dimensional population balance models , 2008 .