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Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data-Based Methods

✍ Scribed by Fani Boukouvala; Atul Dubey; Aditya Vanarase; Rohit Ramachandran; Fernando J. Muzzio; Marianthi Ierapetritou

Publisher: John Wiley and Sons
Year: 2011
Tongue: English
Weight: 587 KB
Volume: 297
Category: Article
ISSN: 1438-7492
DOI: 10.1002/mame.201100054

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✦ Synopsis

Abstract

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. magnified image

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## Abstract Computational methods using mechanistic modeling with a specific application in the area of continuous blending are presented. These methods complement experimental designs and aim to reduce the amount of time, effort, and material required to characterize a device or a process. The dis