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

• Published in: Separation and purification technology Vol. 313; p. 123458
• Main Authors: Li, Heng, Wang, Yunkun, Wang, Yunqian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.05.2023
• Subjects: Contaminants; Emulsion liquid membrane; Extraction efficiency; Machine learning; Model interpretability; ANN; Emulsion liquid membrane; FFD; LR; ELM; Em; SD; Extraction efficiency; Model interpretability; ME; R2; SS; SHAP; AI; MAPE; RMSE; RSM; VROPIP; XGB; RF; PD; Contaminants; Machine learning; IPC; TR
• ISSN: 1383-5866; 1873-3794
• DOI: 10.1016/j.seppur.2023.123458

[Display omitted] •Machine learning was used to predict the extraction of multiple substances by emulsion liquid membranes.•Extreme gradient boosting model showed excellent predictive ability on extraction models that predict multiple substances at the same time.•The model used the most comprehensive influence parameters possible and used SHAP values for interpretive analysis of the model.•Extraction time and volume ratio of emulsion to internal phase had a greater effect on the extraction of emulsion liquid membrane. 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.