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Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data‐Based Methods
Author(s) -
Boukouvala Fani,
Dubey Atul,
Vanarase Aditya,
Ramachandran Rohit,
Muzzio Fernando J.,
Ierapetritou Marianthi
Publication year - 2012
Publication title -
macromolecular materials and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 96
eISSN - 1439-2054
pISSN - 1438-7492
DOI - 10.1002/mame.201100054
Subject(s) - standard deviation , population , mixing (physics) , design of experiments , discrete element method , process (computing) , kriging , identification (biology) , position (finance) , computer science , mathematical optimization , biological system , algorithm , statistics , mathematics , machine learning , mechanics , botany , finance , economics , physics , demography , quantum mechanics , sociology , biology , operating system
The application of computationally inexpensive modeling methods for a predictive study of powder mixing is discussed. A multidimensional population balance model is formulated to track the evolution of the distribution of a mixture of particle populations with respect to position and time. Integrating knowledge derived from a discrete element model, this method can be used to predict residence time distribution, mean and relative standard deviation of the API concentration in a continuous mixer. Low‐order statistical models, including response surface methods, kriging, and high‐dimensional model representations are also presented. Their efficiency for design optimization and process design space identification with respect to operating and design variables is illustrated.