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Tri-n-butyl phosphate (TBP) is a solvent that is commonly used in a varietyof industries, including the nuclear and chemical industries, for its abilityto dissolve and purify various inorganic acids and metals. It is often used inhydrometallurgical processes to separate and purify these substances. Machinelearning models offer a promising alternative to traditional methods forpredicting the viscosity of TBP mixtures. By training machine learning modelson a dataset of viscosity measurements, it is possible to accurately predictthe viscosity of TBP mixtures at different compositions, densities, andtemperatures, which can save time and resources and reduce the risk of exposureto toxic solvents. This paper aimed at proposing Machine Learning (ML)techniques to automatically predict the viscosity of TBP mixtures usingexperimental data. For comparison peruses, we trained five different MLalgorithms including Support Vector Regressor (SVR), Random Forest (RF),Logistic Regression (LR), Gradient Boosted Decision Trees (XGBoost), and NeuralNetwork (NN). We collected a total of 511 measurements for TBP mixtures withtemperature-based density, at different compositions, containing hexane,dodecane, cyclohexane, n-heptane, toluene, and ethylbenzene measured attemperatures 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 50neurons in the hidden layers could achieve the best viscosity predictions for asystem of TBP mixtures. The NN model outperformed other regular ML models interms of Mean Square Error (MSE) of 0.157 % and adjusted R2 of 99.72 % on thetest data set. This paper demonstrated that the NN model can be an appropriateoption to accurately predict the viscosity of TBP + Ethylbenzene with a marginof deviation as low as 0.049 %.

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