  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 %.

Computational Physics;Data Analysis, Statistics and Probability