Comparison of Different Machine Learning Approaches to Predict Viscosity of Tri-n-Butyl Phosphate Mixtures Using Experimental Data

Abstract

Tri-n-butyl phosphate (TBP) is a solvent that is commonly used in a variety of industries, including the nuclear and chemical industries, for its ability to dissolve and purify various inorganic acids and metals. It is often used in hydrometallurgical processes to separate and purify these substances. Machine learning models offer a promising alternative to traditional methods for predicting the viscosity of TBP mixtures. By training machine learning models on a dataset of viscosity measurements, it is possible to accurately predict the viscosity of TBP mixtures at different compositions, densities, and temperatures, which can save time and resources and reduce the risk of exposure to toxic solvents. This paper aimed at proposing Machine Learning (ML) techniques to automatically predict the viscosity of TBP mixtures using experimental data. For comparison peruses, we trained five different ML algorithms including Support Vector Regressor (SVR), Random Forest (RF), Logistic Regression (LR), Gradient Boosted Decision Trees (XGBoost), and Neural Network (NN). We collected a total of 511 measurements for TBP mixtures with temperature-based density, at different compositions, containing hexane, dodecane, cyclohexane, n-heptane, toluene, and ethylbenzene measured at temperatures of T= (288.15, 293.15, 298.15, 303.15, 308.15, 313.15, 318.15, 323.15, and 328.15) K. The results revealed that the NN model with 25 and 50 neurons in the hidden layers could achieve the best viscosity predictions for a system of TBP mixtures. The NN model outperformed other regular ML models in terms of Mean Square Error (MSE) of 0.157 % and adjusted R2 of 99.72 % on the test data set. This paper demonstrated that the NN model can be an appropriate option to accurately predict the viscosity of TBP + Ethylbenzene with a margin of deviation as low as 0.049 %.