Loading...
Simulation of ion exchange processes using neuro-fuzzy reasoning
Author(s)
Van den Bosch, Magali Marie
Date Issued
2009
Type
Thesis
Publisher
Cape Peninsula University of Technology
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.
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.
Additional information
Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.
File(s)![Thumbnail Image]()
Loading...
Name
200679295_Van Den Bosch_MM_Mtech_Chemical Engineering_Eng_2009_8011785.pdf
Description
Thesis
Size
32.73 MB
Format
Adobe PDF
Checksum
(MD5):e7360f2347bd49faa339c6f7533783e3