Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network.

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 logP, acid dissociation constant pK(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.