Title : ( Predicting the yield of pomegranate oil from supercritical extraction using ar ti ficial neural networks and an adaptive-network-based fuzzy inference system )
Authors: Javad Sargolzaei , Amin Hedayati Moghaddam ,
دانلود فایل برای اعضای دانشگاهAccess to full-text not allowed by authors
Abstract
Various simulation tools were used to develop an effective intelligent system to predict the effects of t empe ra tur e a n d pr es sur e o n a n oil ex t ract ion yie ld. Pomegranate oil was extracted using a supercritical CO(SC-CO2) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis f u n c t i on ne u r a l n e t w or k ( RB F N N ) a n d a n a d a pt i v e ne two rk -b as ed fu zzy i n f eren ce sys tem (AN FIS) wer e tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coeffi cient of determination ( R2) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an R2 of 0.9948.
Keywords
, oil recovery, artificial intelligence, extraction, neural networks , supercritical extraction
@article{paperid:1036476,
author = {Sargolzaei, Javad and Hedayati Moghaddam, Amin},
title = {Predicting the yield of pomegranate oil from supercritical extraction using ar ti ficial neural networks and an adaptive-network-based fuzzy inference system},
journal = {Frontiers of Chemical Science and Engineering},
year = {2013},
volume = {7},
number = {3},
month = {April},
issn = {2095-0179},
pages = {357--365},
numpages = {8},
keywords = {oil recovery; artificial intelligence; extraction; neural networks ; supercritical extraction},
}
%0 Journal Article
%T Predicting the yield of pomegranate oil from supercritical extraction using ar ti ficial neural networks and an adaptive-network-based fuzzy inference system
%A Sargolzaei, Javad
%A Hedayati Moghaddam, Amin
%J Frontiers of Chemical Science and Engineering
%@ 2095-0179
%D 2013
