Modeling and prediction of activity coefficient ratio of electrolytes in aqueous electrolyte solution containing amino acids using artificial neural network

Abstract

Modeling and prediction of activity coefficients of electrolytes and biomolecules is a key to developing the separation and purification processes of biomolecules. In this paper the systems containing amino acids or peptide + water + one electrolyte (NaCl, KCl, NaBr, KBr) are modeled by different types of neural networks and an artificial neural network (ANN) is designed that can predict the mean ionic activity coefficient ratio of electrolytes in presence and in absence of amino acid in different mixtures better than the common polynomial equations proposed for this kind of predictions. It was found that the designed ANN which is a multi-layer perceptron (MLP) type network can be better trained than other types of neural network. The root mean square deviation (RMSD) of the designed neural network in prediction of the mean ionic activity coefficient ratio of electrolytes is less than 0.005 and proves the effectiveness of the ANN in correlation and prediction of activity coefficients in the studied mixtures.