Determination of volumetric mass transfer coefficient in gas-solid-liquid stirred vessels handling high solids concentrations: Experiment and modeling

Davoody, M, Bin Abdul Raman, A, Asgharzadehahmadi, S, Binti Ibrahim, S and Parthasarathy, R 2018, 'Determination of volumetric mass transfer coefficient in gas-solid-liquid stirred vessels handling high solids concentrations: Experiment and modeling', Iranian Journal of Chemistry and Chemical Engineering, vol. 37, no. 3, pp. 195-212.


Document type: Journal Article
Collection: Journal Articles

Title Determination of volumetric mass transfer coefficient in gas-solid-liquid stirred vessels handling high solids concentrations: Experiment and modeling
Author(s) Davoody, M
Bin Abdul Raman, A
Asgharzadehahmadi, S
Binti Ibrahim, S
Parthasarathy, R
Year 2018
Journal name Iranian Journal of Chemistry and Chemical Engineering
Volume number 37
Issue number 3
Start page 195
End page 212
Total pages 18
Publisher Iranian Institute of Research and Development in Chemical Industries
Abstract Rigorous analysis of the determinants of volumetric mass transfer coefficient (kLa) and its accurate forecasting are of vital importance for effectively designing and operating stirred reactors. Majority of the available literature is limited to systems with low solids concentration, while there has always been a need to investigate the gas-liquid hydrodynamics in tanks handling high solid loadings. Several models have been proposed for predicting kLa values, but the application of neuro-fuzzy logic for modeling kLa based on combined operational and geometrical conditions is still unexplored. In this paper, an ANFIS (adaptive neuro-fuzzy inference system) model was designed to map three operational parameters (agitation speed (RPS), solid concentration, superficial gas velocity (cm/s)) and one geometrical parameter (number of curved blades) as input data, to kLa as output data. Excellent performance of ANFIS's model in predicting kLa values was demonstrated by various performance indicators with a correlation coefficient of 0.9941.
Subject Chemical Engineering Design
Keyword(s) Adaptive neuro-fuzzy inference system
Artificial intelligence-based modeling
Artificial neural networks
Stirred vessels
Volumetric mass transfer coefficient
Copyright notice © 2018, Iranian Institute of Research and Development in Chemical Industries.
ISSN 1021-9986
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