NDLI: Optimization of supercritical extraction of galegine from Galega officinalis L.: Neural network modeling and experimental optimization via response surface methodology

Content Provider	Springer Nature Link
Author	Davoodi, Pooya Ghoreishi, Seyyed Mohammad Hedayati, Ali
Copyright Year	2017
Abstract	Supercritical CO$_{2}$ 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), CO$_{2}$ 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 (R$^{2}$=93.4%) along with modified coefficient of determination (mod-R$^{2}$=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 (R$^{2}$) was 96.6%.
Starting Page	854
Ending Page	865
Page Count	12
File Format	PDF
ISSN	02561115
Journal	Korean Journal of Chemical Engineering
Volume Number	34
Issue Number	3
e-ISSN	19757220
Language	English
Publisher	Springer US
Publisher Date	2016-12-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Supercritical Extraction Galega officinalis L. Artificial Neural Network (ANN) Response Surface Methodology (RSM) Galegine Industrial Chemistry/Chemical Engineering Catalysis Materials Science Biotechnology
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering

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