Title
Using deepest regression method for optimization of fluidized bed granulation on semi-full scale Using deepest regression method for optimization of fluidized bed granulation on semi-full scale
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Amsterdam ,
Subject
Pharmacology. Therapy
Source (journal)
International journal of pharmaceutics. - Amsterdam
Volume/pages
258(2003) :1-2 , p. 85-94
ISSN
0378-5173
ISI
000183114600009
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
This study applied the deepest regression method to estimate the granule size of unsuccessful fluidized bed granulation runs. This study uses data from a previous study [Int. J. Pharm. 220 (2001) 149] on optimization of fluidized granulation process, wherein 8 of the 30 runs did not succeeded due to overwetting of the powder bed. The "complete data" (the observed and the estimated granule size by the depth regression method) were used to develop two regression models for the granule size: an empirical model based on the process variables (inlet air temperature, inlet airflow rate, spray rate, and inlet air humidity) and a fundamental model based on the powder bed moisture content and the relative droplet size. The regression models based on the incomplete data from the previous study and the regression models of the "complete data" were comparable in the sense that the contour plots based on the respective models and the predicted granule size were comparable. (C) 2003 Elsevier Science B.V. All rights reserved.
E-info
https://repository.uantwerpen.be/docman/iruaauth/d9421c/cd16608.pdf
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