Machine learning for predicting the dynamic extraction of multiple substances by emulsion liquid membranes

Abstract

As of now, the models are only able to predict emulsion liquid membranes (ELM) for a single contaminant. In this study, to develop models for predicting the extraction efficiency of ELM for multiple substances, we used the linear model, random forest model, extreme gradient boosting model (XGB), and artificial neural network model. A total of 1010 data points were collected, which contained two main types of input features, the preparation conditions and operating parameters of ELM. Among these models, XGB showed excellent prediction accuracy (R2: 0.942, RMSE: 6.478, MAPE: 1.587). These evaluations showed that this model had a powerful ability to predict the extraction efficiency of ELM, and the results show that the predicted extraction efficiency was very close to the real ones. By quantifying the weightiness and marginal effect of the parameters, the models were interpreted using the Shapley additive explanation method. This work showed that the extraction time, volume ratio of organic phase to internal phase, emulsification time and feed concentration had important effects on the extraction efficiency of ELM, and the results indicated that the extraction efficiency can be promoted by increasing the values of the parameters before the optimum value, while it was inhibited after the optimum value. Our work suggested that the proposed model can be helpful for making more rational decisions in determining the preparation conditions and operating parameters of ELM.