Holdup prediction in inverse fluidization using non-Newtonian pseudoplastic liquids: Empirical correlation and ANN modeling

2015 ◽  
Vol 273 ◽  
pp. 83-90 ◽  
Author(s):  
Bimal Das ◽  
Uma Prasad Ganguly ◽  
Nirjhar Bar ◽  
Sudip Kumar Das
Author(s):  
Mehdi Fadaei ◽  
M.J. Ameri ◽  
Y. Rafiei ◽  
Kayvan Ghorbanpour

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1055
Author(s):  
Gulenay Guner ◽  
Dogacan Yilmaz ◽  
Ecevit Bilgili

This study examined the impact of stirrer speed and bead material loading on fenofibrate particle breakage during wet stirred media milling (WSMM) via three kinetic models and a microhydrodynamic model. Evolution of median particle size was tracked via laser diffraction during WSMM operating at 3000–4000 rpm with 35–50% (v/v) concentration of polystyrene or zirconia beads. Additional experiments were performed at the center points of the above conditions, as well as outside the range of these conditions, in order to test the predictive capability of the models. First-order, nth-order, and warped-time kinetic models were fitted to the data. Main effects plots helped to visualize the influence of the milling variables on the breakage kinetics and microhydrodynamic parameters. A subset selection algorithm was used along with a multiple linear regression model (MLRM) to delineate how the breakage rate constant k was affected by the microhydrodynamic parameters. As a comparison, a purely empirical correlation for k was also developed in terms of the process/bead parameters. The nth-order model was found to be the best model to describe the temporal evolution; nearly second-order kinetics (n ≅ 2) was observed. When the process was operated at a higher stirrer speed and/or higher loading with zirconia beads as opposed to polystyrene beads, the breakage occurred faster. A statistically significant (p-value ≤ 0.01) MLRM of three microhydrodynamic parameters explained the variation in the breakage rate constant best (R2 ≥ 0.99). Not only do the models and the nth-order kinetic–microhydrodynamic correlation enable deeper process understanding toward developing a WSMM process with reduced cycle time, but they also provide good predictive capability, while outperforming the purely empirical correlation.


2019 ◽  
Vol 18 ◽  
pp. 3357-3364
Author(s):  
Murali Vangalapati ◽  
K. Balaji ◽  
A. Gopichand

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