Use of artificial neural networks to predict quaternery phase systems from limited experimental data

The aim of the present work was to develop a method for predicting the phase behaviour of four component systems consisting of oil, water and two surfactants from a limited number of screening experiments. Investigations were conducted to asses the potential of artificial neural networks (ANNs) with back-propagation training algorithm to predict the phase behaviour of four component systems. Three inputs only (percentages of oil and water and HLB of the surfactant blend) and four outputs (oil in water emulsion, water in oil emulsion, microemulsion, and liquid crystals containing regions) were used. Samples used for training represented about 15% of the sampling space within the tetrahedron body. The network was trained by performing optimization of the number and size of the weights for neuron interconnections. The lowest error was obtained with 15 hidden neurons and after 4500 training cycles. The trained ANN was tested on validation data and had an accuracy of 85.2-92.9%. In most cases the errors in the prediction were confined to points lying along the boundaries of regions and for the extrapolated predictions outside the ANNs 'experience'. This approach is shown to be highly successful and the ANNs have proven to be a useful tool for the prediction of the phase behaviour of quaternary systems with less experimental effort.