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The application of Neural Network has been considered as promising tool for the analysis of ion-exchange process because of their simplicity with respect to simulation, prediction and modelling.
This process is complex, non-linear and involves many factors influencing the ions’ uptake mechanisms from the pregnant solution, the subsequent step being the elution.
In order to improve the performance of the ion-exchange process, optimization and analysis of the process should be accomplished.
The majority of models based on the ion-exchange process are theoretical.
Modelling and simulation are tools to achieve the objectives.
Most of these models are derived from physical descriptions and an understanding of the ion-exchange process under certain assumptions.
However, as mentioned above, they are mathematically complex, computationally expensive and they ideally require a very detailed knowledge of the ion-exchange process itself.
Therefore, there is a need to find an alternative means for predicting process performance which has led to the interest by researchers in applying Neural Network techniques.
John Kabuba Tshilenge is a Lecturer in Department of Chemical Engineering, Vaal University of Technology.He has more than 13 years’ experience in academia.
He has published over 30 peer reviewed articles in journals, conferences and book chapter.
His research interests are mainly in the broad areas of wastewater treatment and Neural Network.
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