doi: 10.7763/IJIMT.2014.V5.500
Neural Network Application in Fixed Bed Column Adsorption
- 1Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti PutraMalaysia, 43300 UPM Serdang, Selangor, Malaysia.
- 2Fuel
Abstract
Adsorption experiments were carried out in fixed bed column. Neural network (NN) was used to describe the fixed bed adsorption of POME pigment by resin. The general breakthrough models such as Thomas and Yoon–Nelson models resulted in poor fitness with experimental data (<em>R</em><sup>2</sup> < 0.8). A wavelet neural network model (WNN) was developed to model the breakthrough curves in fixed bed column for multicomponent system and WNN model successfully described the adsorption process (<em>R</em><sup>2</sup> = 1). At the initial stages, BDST model showed good agreement with the experimental data but diverged at higher <em>C<sub>b</sub>/C</em><sub>0</sub> ratio (> 0.11).
Keywords
- Neural network
- fixed bed column
- adsorption
- modeling
How to Cite
M. M. Nourouzi, S. Keshani, and L. Chuah Abdullah, "Neural Network Application in Fixed Bed Column Adsorption," International Journal of Innovation, Management and Technology, vol. 5, no. 2, pp. 130-133, 2014. https://doi.org/10.7763/IJIMT.2014.V5.500
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).