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
Identifiers and Pagination:Year: 2008
First Page: 73
Last Page: 78
Publisher Id: TOCENGJ-2-73
Article History:Received Date: 04/02/2008
Revision Received Date: 24/03/2008
Acceptance Date: 08/04/2008
Electronic publication date: 6/5/2008
Collection year: 2008
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.
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.