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Article
Kinetic of Alkaloids Extraction from Plant by Batch Pertraction in Rotating Discs Contactor

Author: Khalid M. Abed خالد محسن عبد
Journal: Iraqi Journal of Chemical and Petroleum Engineering المجلة العراقية للهندسة الكيمياوية وهندسة النفط ISSN: 19974884/E26180707 Year: 2014 Volume: 15 Issue: 2 Pages: 75-84
Publisher: Baghdad University جامعة بغداد

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Abstract

A liquid membrane process of Alkaloids extraction from Datura Innoxia solution was studied applying pertraction process in rotating discs contactor (RDC). Decane as a liquid membrane and dilute sulphuric acid as stripping solution were used. The effect of the fundamental parameters influencing the transport process, e.g. type of solvent used, effect of disks speed, amount of liquid membrane and effect of pH for feed and strip solution. The transport of alkaloids was analysed on the basis of kinetic laws of two consecutive irreversible first order reactions. Thus, the kinetic parameters (k1, k1, R_m^max, tmax, J_F^maxand J_S^max) for the transport of alkaloids were determined. The effect of organic membrane type on percentage of Alkaloids transport was found to be in the order (n-decane> n-heptane> n-hexane> ethyl ether). The results showed that the highest alkaloids extraction was obtained when using two stages, (10 rpm) discs speed, (pH=9.5) of feed solution and (pH=2) of acceptor solution in n-decane. Observation showed that the membrane entrance rate constant k1 and percentage of alkaloids transported in strip phase increased with increasing numbers of stages but the exit rate constant k2 decreased. The alkaloids extraction ratio increased with increasing the disks speed from 5 to 10 rpm but decreased at 15 rpm and decreased when increasing the volume of membrane. Also pH of feed and strip solution affected the extraction ratio and rate constants.


Article
Prediction of Extraction Efficiency in Rdc Column Using Artificial Neural Network

Authors: Chalak S. Omar --- Adil. A. A. Al-Hemiri
Journal: Journal of Engineering مجلة الهندسة ISSN: 17264073 25203339 Year: 2008 Volume: 14 Issue: 2 Pages: 2607-2621
Publisher: Baghdad University جامعة بغداد

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Abstract

An application of neural network technique was introduced in modeling extraction efficiency in RDC column, based on a data bank of around 352 data points collected in the open literature. Three models were made, using back-propagation algorithm, the extraction efficiency was found to be a function of seven dimensionless groups: Weber number (we), ( ), ( ), ( ), ( ), ( ) and ( ). Statistical analysis showed that the proposed models have an average absolute error (AARE) and standard deviation (SD) of 12.23% and 10.61% for the first model, 5.35% and 6.21% for the second model, 8.34% and 7.59% for the third model. The developed correlations also show better prediction over a wide range of operating conditions, physical properties and column geometry.

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