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Received August 23, 2016
Accepted October 24, 2016
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Optimization of supercritical extraction of galegine from Galega officinalis L.:Neural network modeling and experimental optimization via response surface methodology
Department of Chemical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
ghoreshi@cc.iut.ac.ir
Korean Journal of Chemical Engineering, March 2017, 34(3), 854-865(12), 10.1007/s11814-016-0304-2
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Abstract
Supercritical CO2 extraction of galegine from Galega officinalis L. was carried out under different operating conditions of temperature (35-55 °C), pressure (10-30MPa), dynamic extraction time (30-150min), CO2 flow rate (0.5-2.5 mL/min) and constant static extraction time of 20 min. Design of experiment was by response surface methodology (RSM) using Minitab software 17. The response surface analysis accuracy was verified by the coefficient of determination (R2=93.4%) along with modified coefficient of determination (mod-R2=87.7%). The optimum operating conditions were found by using RSM modeling to be 42.8 °C, 22.7MPa, 141.5min and 2.15 mL/min, in which the maximum galegine extraction yield of 3.3932mg/g was obtained. Artificial neural network (ANN) using Levenberg-Marquardt backpropagation training function with six neurons in the hidden layer was implemented for the modeling of galegine extraction such that the coefficient of determination (R2) was 96.6%.
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References
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Ghoreishi SM, Hedayati A, Mohammadi S, J. Supercrit. Fluids, 113, 53 (2016)
Kang KY, Ahn DH, Wilkinson GT, Chun BS, Korean J. Chem. Eng., 22(3), 399 (2005)
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Khuri AI, Mukhopadhyay S, Wiley Interdiscip. Rev. Comput. Stat., 2, 128 (2010)
Su H, Wang X, Kim YG, Kim SB, Seo YG, Kim JS, Kim CJ, Korean J. Chem. Eng., 31(11), 2070 (2014)
Bashipour F, Ghoreishi SM, J. Supercrit. Fluids, 95, 348 (2014)
Wang SC, Artificial neural network. In Interdisciplinary Computing in Java Programming, Springer US, 81-100 (2003).
Himmelblau DM, Korean J. Chem. Eng., 17(4), 373 (2000)
Ghoreishi SM, Hedayati A, Mousavi SO, J. Supercrit. Fluids, 112, 57 (2016)
Khadom AA, Korean J. Chem. Eng., 30(12), 2197 (2013)
Valderrama JO, Munoz JM, Rojas RE, Korean J. Chem. Eng., 28(6), 1451 (2011)
Ilbay Z, Sahin S, Buyukkabasakal K, Korean J. Chem. Eng., 31(9), 1661 (2014)
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Basheer IA, Hajmeer M, J. Microbiol. Methods, 43, 3 (2000)
Kurkova V, Neural Net., 5, 501 (1992)
Satya EJ, Chandrakar N, Korean J. Chem. Eng., 33(4), 1318 (2016)
Babaei AA, Khataee A, Ahmadpour E, Sheydaei M, Kakavandi B, Alaee Z, Korean J. Chem. Eng., 33(4), 1352 (2016)