Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column

Liquid-liquid extraction is one of the most important separation processes that widely used in industries. Rotating Disc Contactor (RDC) column is one of the liquidliquid extractor. Therefore, the study of liquid-liquid extraction in RDC column has become a very important subject to be discussed not...

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Main Author:	Azmi, Ezzatul Farhain
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/33082/5/EzzatulFarhainAzmiMFS2013.pdf
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id	my-utm-ep.33082
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spelling	my-utm-ep.330822017-09-11T00:39:21Z Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column 2013-01 Azmi, Ezzatul Farhain Q Science (General) Liquid-liquid extraction is one of the most important separation processes that widely used in industries. Rotating Disc Contactor (RDC) column is one of the liquidliquid extractor. Therefore, the study of liquid-liquid extraction in RDC column has become a very important subject to be discussed not just among the chemical engineers but mathematician as well. This project presents Support Vector Machine (SVM) and Neural Network modeling in the prediction of concentration of dispersed phase outlet in RDC column. SVM is an exciting Machine Learning technique that learns by example to sign labels to object and can be used for regression as well as classification purpose, while Neural Network is widely used as effective approach for handling nonlinear data especially in situations where the physical processes are not fully understood. Both modeling systems offer the potential for a more flexible and less error in forecasting. Thus, it can help to save time and reducing cost in conducting experiments. A Statistica software is utilized to help with the SVM modeling and a Matlab code is produced to run the Neural Network simulation in this project. The mean square error is calculated to compare the result between the two models. The analysis shows that both SVM and Neural Network modeling can predict the concentration of dispersed phase in RDC column but the SVM approach gives better result than the Neural Network approach. 2013-01 Thesis http://eprints.utm.my/id/eprint/33082/ http://eprints.utm.my/id/eprint/33082/5/EzzatulFarhainAzmiMFS2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69775?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Azmi, Ezzatul Farhain Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
description	Liquid-liquid extraction is one of the most important separation processes that widely used in industries. Rotating Disc Contactor (RDC) column is one of the liquidliquid extractor. Therefore, the study of liquid-liquid extraction in RDC column has become a very important subject to be discussed not just among the chemical engineers but mathematician as well. This project presents Support Vector Machine (SVM) and Neural Network modeling in the prediction of concentration of dispersed phase outlet in RDC column. SVM is an exciting Machine Learning technique that learns by example to sign labels to object and can be used for regression as well as classification purpose, while Neural Network is widely used as effective approach for handling nonlinear data especially in situations where the physical processes are not fully understood. Both modeling systems offer the potential for a more flexible and less error in forecasting. Thus, it can help to save time and reducing cost in conducting experiments. A Statistica software is utilized to help with the SVM modeling and a Matlab code is produced to run the Neural Network simulation in this project. The mean square error is calculated to compare the result between the two models. The analysis shows that both SVM and Neural Network modeling can predict the concentration of dispersed phase in RDC column but the SVM approach gives better result than the Neural Network approach.
format	Thesis
qualification_level	Master's degree
author	Azmi, Ezzatul Farhain
author_facet	Azmi, Ezzatul Farhain
author_sort	Azmi, Ezzatul Farhain
title	Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
title_short	Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
title_full	Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
title_fullStr	Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
title_full_unstemmed	Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column
title_sort	application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (rdc) column
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2013
url	http://eprints.utm.my/id/eprint/33082/5/EzzatulFarhainAzmiMFS2013.pdf
_version_	1747816075230183424

Application of support vector machine and neural network modeling in the prediction of concentration of dispersed phase outlet in rotating disc contactor (RDC) column

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