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Frontiers of Chemical Science and Engineering

ISSN 2095-0179

ISSN 2095-0187(Online)

CN 11-5981/TQ

Postal Subscription Code 80-969

2018 Impact Factor: 2.809

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Front Chem Sci Eng    2013, Vol. 7 Issue (3) : 357-365    https://doi.org/10.1007/s11705-013-1336-3
RESEARCH ARTICL
Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system
J. Sargolzaei, A. Hedayati Moghaddam()
Department of chemical engineering, Ferdowsi university of Mashhad, Mashhad 9177948944, Iran
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Abstract

Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO2 (SC-CO2) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient 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     
Corresponding Author(s): Moghaddam A. Hedayati,Email:a.hedayati1985@gmail.com   
Issue Date: 05 September 2013
 Cite this article:   
J. Sargolzaei,A. Hedayati Moghaddam. Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system[J]. Front Chem Sci Eng, 2013, 7(3): 357-365.
 URL:  
https://academic.hep.com.cn/fcse/EN/10.1007/s11705-013-1336-3
https://academic.hep.com.cn/fcse/EN/Y2013/V7/I3/357
Fig.1  The process diagram of the super critical extraction unit
Fig.2  The architecture of the feed forward neural network with n input and m output variables
Fig.3  Radial basis function (RBF) structure
Fig.4  (a) The complexity of the experimental data, (b) Input vector run number
Run No.Training algorithmNo. of neuronsR2MSE
TrainingValidationTestAll
1TRAINLM10-200.998580.973440.986850.991586.6547 e-6
2TRAINLM10-20-100.999390.996840.98910.996362.5086 e-6
3TRAINGDA20-40-200.975660.960020.989690.97799.8986 E-6
4TRAINLM20-40-200.998840.996340.994750.996975.1327 e-7
5TRAINLM30-50-7010.963850.868090.970871.9408 E-5
6TRAINLM30-50-300.999990.91710.985230.97943.8461 E-6
Tab.1  BPNN architecture
Fig.5  (a) predicted data by BPNN experimental data; (b) error run No. for best BPNN model
Fig.6  Comparison between experimental results and BPBB predicted results
Run No.Spread constantR2MSE
10.050.98184.7587 e-6
20.10.96209.9339 e-6
30.30.91672.1746 e-5
40.50.85773.7158 e-5
510.82074.6812 e-5
620.82064.6834 e-5
730.82064.6838 e-5
840.81534.8240 e-5
Tab.2  RBF architecture
Fig.7  (a) predicted RBFNN data experimental data; (b) error run No. for the best RBFNN model
Fig.8  Comparison between the experimental data and the data predicted by RBFNN and BPNN
Fig.9  (a) a plot of the range of influence for the testing data set; (b) a plot of MSE the range of influence for the testing data set for the ANFIS simulation
Fig.10  Comparison of experimental and simulated data normalized temperature at (a) 20 MPa, (b) 30 MPa, and (c) 40 MPa
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