Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed

In the current study, a Neuro-Fuzzy model has been developed to predict the mass of extract in the process of supercritical fluid extraction of Pimpinella anisum L. seed. The adaptive-network-based fuzzy inference system (ANFIS) technique was trained with the recorded data from kinetic experiments o...

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Main Author:	Meysam Davoody
Format:	Thesis
Language:	English
Published:	2012
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/48135/1/MeysamDavoodyMFKM2012.pdf
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spelling	my-utm-ep.481352017-08-16T08:16:50Z Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed 2012 Meysam Davoody, TP Chemical technology In the current study, a Neuro-Fuzzy model has been developed to predict the mass of extract in the process of supercritical fluid extraction of Pimpinella anisum L. seed. The adaptive-network-based fuzzy inference system (ANFIS) technique was trained with the recorded data from kinetic experiments of the mentioned process at pressures of 8, 10, 14 and 18 MPa and constant temperature of 303.15 K which generated the membership function and rules that excellently expounded the input/output correlations in the process. Excellent prediction with Root Mean Square Error (RMSE) of 0.0235 was observed. In the next step of study, mass transfer coefficient in terms of Sherwood number was estimated by a neuro-fuzzy network. Then, the estimated mass transfer coefficient was introduced into the mathematical model. The proposed gray box (hybrid) model was validated with the experimental data. Results confirmed that equipping mathematical model with neuro-fuzzy network improved performance of the model significantly. Shokri et al. (2011) applied Artificial Neural Networks and mathematical modeling on this process, and reported the results of the proposed models. In the last part of this thesis, all four models (including two proposed models of this study) were compared. It was concluded that neuro-fuzzy and gray box models had the best performance. 2012 Thesis http://eprints.utm.my/id/eprint/48135/ http://eprints.utm.my/id/eprint/48135/1/MeysamDavoodyMFKM2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:79400?queryType=vitalDismax&query=Neuro+fuzzy+and+hybrid+modeling+of+supercritical+fluid+extraction+of+Pimpinella+Anisum+L.+seed&public=true masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Meysam Davoody, Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
description	In the current study, a Neuro-Fuzzy model has been developed to predict the mass of extract in the process of supercritical fluid extraction of Pimpinella anisum L. seed. The adaptive-network-based fuzzy inference system (ANFIS) technique was trained with the recorded data from kinetic experiments of the mentioned process at pressures of 8, 10, 14 and 18 MPa and constant temperature of 303.15 K which generated the membership function and rules that excellently expounded the input/output correlations in the process. Excellent prediction with Root Mean Square Error (RMSE) of 0.0235 was observed. In the next step of study, mass transfer coefficient in terms of Sherwood number was estimated by a neuro-fuzzy network. Then, the estimated mass transfer coefficient was introduced into the mathematical model. The proposed gray box (hybrid) model was validated with the experimental data. Results confirmed that equipping mathematical model with neuro-fuzzy network improved performance of the model significantly. Shokri et al. (2011) applied Artificial Neural Networks and mathematical modeling on this process, and reported the results of the proposed models. In the last part of this thesis, all four models (including two proposed models of this study) were compared. It was concluded that neuro-fuzzy and gray box models had the best performance.
format	Thesis
qualification_level	Master's degree
author	Meysam Davoody,
author_facet	Meysam Davoody,
author_sort	Meysam Davoody,
title	Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
title_short	Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
title_full	Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
title_fullStr	Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
title_full_unstemmed	Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed
title_sort	neuro fuzzy and hybrid modeling of supercritical fluid extraction of pimpinella anisum l. seed
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2012
url	http://eprints.utm.my/id/eprint/48135/1/MeysamDavoodyMFKM2012.pdf
_version_	1747817316277551104

Neuro fuzzy and hybrid modeling of supercritical fluid extraction of Pimpinella Anisum L. seed

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