Simulation of ion exchange processes using neuro-fuzzy reasoning

Abstract

Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.