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This study explored the capability of semi-empirical and neural network approaches for correlating and predicting some equilibrium and non-equilibrium thermophysical properties of liquid lubricants. The equilibrium properties, including the densities and several thermodynamic coefficients for 12 liquid lubricants, were correlated and predicted through a perturbed hard-chain equation of state (PHC EoS) by an attractive term of Yukawa tail. The molecular parameters of PHC EoS were obtained by correlating them with 935 data points for the densities and isothermal compressibilities of studied systems in the 278–353 K range and pressure up to 70 MPa with the average absolute relative deviations (AARDs) of 0.36 % and 5.25 %, respectively. Then, that EoS was employed to predict the densities of other literature sources (with an AARD of 0.81 %) along with several thermodynamic coefficients, including isobaric expansivities (with an AARD of 12.92 %), thermal pressure coefficients (with the AARD of 12.93 %), and internal pressure (with the AARD of 13.67 %), for which the reference values were obtained from Tait-type equations and available in literature. Apart from the equilibrium mentioned above properties, the PHC EoS was combined with a rough hard-sphere-chain (RHSC) model to correlate and predict the 548 data points for the viscosities of 7 selected liquefied lubricants in 283–353 K range and pressures up to 100 MPa with the AARD of 11.85 %. The accuracy of the results from the RHSC-based model has also been compared with an empirical  PηT  equation of Tammann-Tait type and an artificial neural network (ANN), both of which were developed in this work. The ANN of one hidden layer and 13 neurons was trained using the back-propagation algorithm. The results acquired from this approach were very promising and demonstrated the potential of the ANN approach for predicting the viscosity of lubricants, reaching an AARD of 0.81 % for the entire dataset.

Modeling equilibrium and non-equilibrium thermophysical properties of liquid lubricants using semi-empirical approaches and neural network