Designing and Analyzing Fixed-Bed Adsorption Systems with Artificial Neural Networks
Imad A. Basheer
Yacoub M. Najjar
AbstractAdsorption in fixed beds is a common unit process in water treatment and in a large number of separation technologies. The dynamics of fixed-bed adsorption are commonly described by mathematical models which require a thorough understanding of the complex phenomena. The mathematical models of adsorption, which are a set of partial differential equations describing mass balances and transfer, usually require numerical solution. This article describes the application of artificial neural networks to the problem of predicting the breakthrough time, a critical parameter in adsorption in fixed beds. The approach employed also can be used to develop more general predictive neural networks that deal with a large number of situations encountered in fixed-bed adsorption. The ability of the developed neural network to predict the breakthrough time is found to be comparable to that for a mathematically-based model. The advantages of the neural networks over the conventional mathematical models as well as their limitations are stated.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.