Application of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltration

Abstract

Random Forest (RF) and Neural Network (NN), respectively, were employed to understand and predict the critical flux (Jcrit) of oil-in-water emulsions in crossflow microfiltration. A total of 223 data sets from various studies were compiled, with nine operational parameters and one target variable of critical flux. RF indicated crossflow velocity (CFV) as the most dominant parameter in determining critical flux, outweighing surfactant and oil variations. Exceptions were found in specific cases when casein concentration was the most dominant, since the smaller sizes of casein significantly decreased Jcrit. The NN model predicted the best when all nine input parameters were integrated and the worst when CFV was the sole parameter used for model development, even though CFV was identified as the most dominant. The results here demonstrate the usefulness of machine learning tools to enhance the understanding on and prediction of critical flux without any governing equations.