Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation

Isopropyl myristate (IPM) is an important chemical in the cosmetic and pharmaceutical industries. The IPM can be produced either through esterification or the transesterification process in semibatch reactive distillation (BRD). However, the latter process is not widely explored. The transesterifica...

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Main Author:	Bashah, Nur Alwani Ali
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TP1-1185 Chemical technology
Online Access:	http://eprints.usm.my/43419/1/Nur%20Alwani%20Binti%20Ali%20Bashah24.pdf
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id	my-usm-ep.43419
record_format	uketd_dc
spelling	my-usm-ep.434192019-04-12T05:26:17Z Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation 2013-04 Bashah, Nur Alwani Ali TP1-1185 Chemical technology Isopropyl myristate (IPM) is an important chemical in the cosmetic and pharmaceutical industries. The IPM can be produced either through esterification or the transesterification process in semibatch reactive distillation (BRD). However, the latter process is not widely explored. The transesterification process in BRD can be represented by a mathematical model, however, this model will end with a large number of differential equations and be very expensive to solve and will also be time consuming. Hence, the empirical model such as the artificial neural network (ANN) model provides better solution as it can deal with highly nonlinear and complex structures. In this work, the production of industrial scaled IPM in BRD through the transesterification process is simulated using Aspen Plus and the simulation result achieved shows a comparable result as reported in the literature. The validated model is then used for sensitivity analysis to determine the relationship between the process input-output variables. The nonparametric test is used and the selected inputs are ranked according to their mean overall sensitivity. From the results, the reboiler duty, the initial mole of isopropanol, methyl mysistate, the reflux ratio, the feed flowrate and the temperature at stage 32 are considered as the input variables in the ANN model development to predict the bottom and distillate composition. 2013-04 Thesis http://eprints.usm.my/43419/ http://eprints.usm.my/43419/1/Nur%20Alwani%20Binti%20Ali%20Bashah24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Kimia
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TP1-1185 Chemical technology
spellingShingle	TP1-1185 Chemical technology Bashah, Nur Alwani Ali Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
description	Isopropyl myristate (IPM) is an important chemical in the cosmetic and pharmaceutical industries. The IPM can be produced either through esterification or the transesterification process in semibatch reactive distillation (BRD). However, the latter process is not widely explored. The transesterification process in BRD can be represented by a mathematical model, however, this model will end with a large number of differential equations and be very expensive to solve and will also be time consuming. Hence, the empirical model such as the artificial neural network (ANN) model provides better solution as it can deal with highly nonlinear and complex structures. In this work, the production of industrial scaled IPM in BRD through the transesterification process is simulated using Aspen Plus and the simulation result achieved shows a comparable result as reported in the literature. The validated model is then used for sensitivity analysis to determine the relationship between the process input-output variables. The nonparametric test is used and the selected inputs are ranked according to their mean overall sensitivity. From the results, the reboiler duty, the initial mole of isopropanol, methyl mysistate, the reflux ratio, the feed flowrate and the temperature at stage 32 are considered as the input variables in the ANN model development to predict the bottom and distillate composition.
format	Thesis
qualification_level	Master's degree
author	Bashah, Nur Alwani Ali
author_facet	Bashah, Nur Alwani Ali
author_sort	Bashah, Nur Alwani Ali
title	Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
title_short	Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
title_full	Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
title_fullStr	Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
title_full_unstemmed	Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation
title_sort	neural network models and sensitivity analysis for the production of isopropyl myristate in semibatch reactive distillation
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Kimia
publishDate	2013
url	http://eprints.usm.my/43419/1/Nur%20Alwani%20Binti%20Ali%20Bashah24.pdf
_version_	1747821210273579008

Neural Network Models And Sensitivity Analysis For The Production Of Isopropyl Myristate In Semibatch Reactive Distillation

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