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Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network
Author(s) -
Michel Monika,
Chimuka Luke,
Kowalkowski Tomasz,
Cukrowska Ewa M.,
Buszewski Bogusław
Publication year - 2013
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201200105
Subject(s) - partition coefficient , chemistry , solubility , extraction (chemistry) , undecane , artificial neural network , octanol , acid dissociation constant , ether , backpropagation , membrane , dissociation constant , chromatography , aqueous solution , biological system , thermodynamics , organic chemistry , artificial intelligence , computer science , physics , biology , biochemistry , receptor
An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients log P , acid dissociation constant p K a , water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.