RESEARCH ARTICLE


Solids Residence Time Distribution in a Three-Phase Bubble Column Reactor: An Artificial Neural Network Analysis



V.K. Pareek1, R. Sharma2, C.G. Cooper3, A.A. Adesina*, 3
1 Department of Chemical Engineering, Curtin University of Technology, Perth, WA 6085, Australia
2 EPIN System Pvt. Ltd., 14, Uniara Garden, Moti Doongri Road, Jaipur, 302 004, India
3 Reactor Engineering & Technology Group, School of Chemical Sciences & Engineering, University of New South Wales, Sydney, NSW, 2052, Australia


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© 2008 Pareek et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Reactor Engineering & Technology Group, School of Chemical Sciences & Engineering, University of New South Wales, Sydney, NSW, 2052, Australia; Tel: +61-2-9385 5268; Fax: +61-2-9385 5966; E-mail: a.adesina@unsw.edu.au


Abstract

Residence time distribution (RTD) study of solids in a three-phase pilot-scale bubble column photoreactor has been carried out in order to provide data for the development of an artificial neural network model usable for process optimisation. The experimental data indicated that the RTD of solids was a complex nonlinear function of gas and liquid velocities as well as the contacting pattern (co-current and countercurrent flow of gas and liquid). In this study, the solid particle RTD data were modeled using feed forward artificial neural networks (ANN). The networks were trained with 250- sets of input-output patterns using back-propagation algorithm. The trained networks were tested using 50-sets of RTD data previously unknown to the networks. Out of several configurations, a 3-layered network with 6-neurons in its hidden layer yielded optimal results with respect to the validation data. The optimal model and empirical data exhibited good agreement with a correlation coefficient of 0.995.

Keywords: RTD, ANN, artificial neural nets, back-propagation, three-phase reactors,, solid recirculation dynamics.