Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system

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

References

[1] Fadavi A, Barzegar M, Hossein Azizi M. Determination of fatty acids and total lipid content in oilseed of 25 pomegranates varieties grown in Iran. Journal of Food Composition and Analysis, 2006, 19(6): 676-680

[2] Engin H, Erogul D, Aksehirli M, Hepaksoy S, Kukul Y. In leaf water potential of pomegranate (Punica Granatum l.) under different irrigation levels. Acta Horticulturae, 2006, 818: 193-198 (ISHS)

[3] Kulkarni A P, Aradhya S M. Chemical changes and antioxidant activity in pomegranate arils during fruit development. Food Chemistry, 2005, 93(2): 319-324

[4] Abbasi H, Rezaei K, Emamdjomeh Z, Mousavi S M E. Effect of various extraction conditions on the phenolic contents of pomegranate seed oil. European Journal of Lipid Science and Technology, 2008, 110(5): 435-440

[5] Abbasi H, Rezaei K, Rashidi L. Extraction of essential oils from the seeds of pomegranate using organic solvents and supercritical CO₂. Journal of the American Oil Chemists'. Society, 2008, 85(1): 83-89

[6] Ozgen M, Durgaç C, Serçe S, Kaya C. Chemical and antioxidant properties of pomegranate cultivars grown in the Mediterranean region of Turkey. Food Chemistry, 2008, 111(3): 703-706

[7] Gil M I, Tomás-Barberán F A, Hess-Pierce B, Holcroft D M, Kader A A. Antioxidant activity of pomegranate juice and its relationship with phenolic composition and processing. Journal of Agricultural and Food Chemistry, 2000, 48(10): 4581-4589

[8] Hora J J, Maydew E R, Lansky E P, Dwivedi C. Chemopreventive effects of pomegranate seed oil on skin tumor development in CD1 mice. Journal of Medicinal Food, 2003, 6(3): 157-161

[9] Kassama L S, Shi J, Mittal G S. Optimization of supercritical fluid extraction of lycopene from tomato skin with central composite rotatable design model. Separation and Purification Technology, 2008, 60(3): 278-284

[10] Sahena F, Zaidul I S M, Jinap S, Karim A A, Abbas K A, Norulaini N A N, Omar A K M. Application of supercritical CO₂ in lipid extraction—A review. Journal of Food Engineering, 2009, 95(2): 240-253

[11] Macías-Sánchez M D, Serrano C M, Rodríguez M R, de la Ossa E M. Kinetics of the supercritical fluid extraction of carotenoids from microalgae with CO₂ and ethanol as cosolvent. Chemical Engineering Journal, 2009, 150(1): 104-113

[12] Rodríguez N R, de Diego S M, Beltrán S, Jaime I, Sanz M T, Rovira J. Supercritical fluid extraction of the omega-3 rich oil contained in hake (Merluccius capensis-Merluccius paradoxus) by-products: Study of the influence of process parameters on the extraction yield and oil quality. Journal of Supercritical Fluids, 2008, 47(2): 215-226

[13] Raasimman M, Govindarajan I, Karthikeyan C. Artificial neural network modeling of an inverse fluidized bed bioreactor. Journal of Applied Sciences and Environmental Management, 2010, 11(2): 65-69

[14] Jang J S R. ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics. IEEE Transactions on, 1993, 23(3): 665-685

[15] Moghaddam A H, Sargolzaei J, Asl M H, Derakhshanfard F. Effect of different parameters on WEPS production and thermal behavior prediction using artificial neural network (ANN). Polymer-Plastics Technology and Engineering, 2012, 51(5): 480-486

[16] Yang S, Shi W, Zeng J. Modelling the supercritical fluid extraction of lycopene from tomato paste waste using neuro-fuzzy approaches. Advances in Neural Networks-ISNN, 2004, 3174: 129-140

[17] Zahedi G, Azizia S, Hatamia T, Sheikhattar L. Gray box modeling of supercritical nimbin extraction from neem seeds using methanol as co-solvent. Open Chemical Engineering Journal, 2010, 4(1): 21-30

[18] Izadifar M, Abdolahi F. Comparison between neural network and mathematical modeling of supercritical CO₂ extraction of black pepper essential oil. Journal of Supercritical Fluids, 2006, 38(1): 37-43

[19] Mitra P, Barman P C, Chang K S. Coumarin extraction from cuscuta reflexa using supercritical fluid carbon dioxide and development of an artificial neural network model to predict the coumarin yield. Food and Bioprocess Technology, 2011, 4(5): 737-744

[20] Menhaj M. Fundamentals of neural networks. Computational Intelligence, 1998, 2: 222-229

[21] Kalavathi M S, Reddy B R, Singh B P. Modeling transformer internal short circuit faults using neural network techniques. 2005 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 2005, 601-604

[22] Bors A G, Gabbouj M. Minimal topology for a radial basis functions neural network for pattern classification. Digital Signal Processing, 1994, 4(3): 173-188

[23] Poggio T, Girosi F. Networks for approximation and learning. Proceedings of the IEEE, 1990, 78(9): 1481-1497

[24] Bors A G, Pitas I. Median radial basis function neural network. Neural Networks. IEEE Transactions on, 1996, 7(6): 1351-1364

[25] Ni Y, Xia Z, Kokot S. A kinetic spectrophotometric method for simultaneous determination of phenol and its three derivatives with the aid of artificial neural network. Journal of Hazardous Materials, 2011, 192(2): 722-729

[26] Wedge D C, Ingram D M, Mingham C G, McLean D A, Bandar Z A. Neural network architectures and overtopping predictions. Proceedings of the ICE-Maritime Engineering, 2005, 158(3): 123-133

[27] Liu F, Nie P C, Huang M, Kong W W, He Y. Nondestructive determination of nutritional information in oil seed rape leaves using visible/near infrared spectroscopy and multivariate calibrations. Science China Information Sciences, 2011, 54(3): 598-608

[28] Pan Y, Jiang J, Wang Z. Quantitative structure-property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network. Journal of Hazardous Materials, 2007, 147(1-2): 424-430

[29] Fazilat H, Ghatarband M, Mazinani S, Asadi Z, Shiri M, Kalaee M. Predicting the mechanical properties of glass fiber reinforced polymers via artificial neural network and adaptive neuro-fuzzy inference system. Computational Materials Science, 2012, 58: 31-37

[30] Iyatomi H, Hagiwara M. Adaptive fuzzy inference neural network. Pattern Recognition, 2004, 37(10): 2049-2057

[31] Pomares H, Rojas I, González J, Prieto A. Structure identification in complete rule-based fuzzy systems. Fuzzy Systems. IEEE Transactions on, 2002, 10(3): 349-359

[32] Pomares H, Rojas I, Gonzalez J, Prieto A. A method for structure identification in complete rule-based fuzzy systems. IEEE, 2001, pp: 376-379

[33] Sargolzaei J, Saghatoleslami N, Mosavi S M, Khoshnoodi M. Comparative study of artificial neural networks (ANN) and statistical methods for predicting the performance of ultrafiltration process in the milk industry. Iranian Journal of Chemistry & Chemical Engineering, 2006, 25(2): 67-76

[34] Sargolzaei J, Kianifar A. Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque. Simulation Modelling Practice and Theory, 2009, 17(7): 1290-1298

[35] Richard Bowen W, Jones M G, Yousef H N S. Prediction of the rate of crossflow membrane ultrafiltration of colloids: A neural network approach. Chemical Engineering Science, 1998, 53(22): 3793-3802

[36] Hagan M T, Menhaj M B. Training feedforward networks with the Marquardt algorithm. Neural Networks. IEEE Transactions on, 1994, 5(6): 989-993

[37] Sharma A, Grover P, Kumar R. Reusability assessment for software components. ACM SIGSOFT Software Engineering Notes, 2009, 34(2): 1-6

[38] Tang L, Zeng G M, Shen G L, Zhang Y, Huang G H, Li J B. Simultaneous amperometric determination of lignin peroxidase and manganese peroxidase activities in compost bioremediation using artificial neural networks. Analytica Chimica Acta, 2006, 579(1): 109-116

[39] Zahra F, Jeyasurya B, Quaicoe J. High-speed transmission line relaying using artificial neural networks. Electric Power Systems Research, 2000, 53(3): 173-179

[40] Hosseini H G, Luo D, Xu G, Liu H, Benjamin D.Intelligent fish freshness assessment. Journal of Sensors, 2008, 2008: 1-8

[41] Anwar N, Khan M S, Ahmed K, Ahmad A, Athar A. Speed scheduling of autonomous railway vehicle control system using ANN. International Journal of Scientific & Engineering Research, 2011, 2(6): 1-6

[42] Tolba A, Abu-Rezq A. Combined classifiers for invariant face recognition. Pattern Analysis & Applications, 2000, 3(4): 289-302

[43] Lahsasna A, Ainon R, Wah T Y. Intelligent credit scoring model using soft computing approach. IEEE, 2008, 396-402

[44] Venkatraghavan V, Acharya U R, Pal M, Paul R R, Min L C, Ray A K, Chatterjee J, Chakraborty C. Automated oral cancer identification using histopathological images: A hybrid feature extraction paradigm. Micron (Oxford, England), 2011, 43(2-3): 352-364

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