A new equation of state for molten alkali metal alloys

Density prediction of liquid alkali metals and their mixtures using an artificial neural network method over the whole liquid range

“…shows another comparison between this work and other EoSs, namely, GMA (Goharshadi-Morsali-Abbaspour) The AAD% values for density prediction in this work and those calculated using other EoSs[24,58,59] for cesium, rubidium and potassium at different temperatures.…”

“…Table 5 shows the AAD% values between the experimental molar density data and calculated values by this work and those calculated using perturbed Lennard-Jones chain (PLJC) EoS with fitted parameters (PLJC-1) [21], PLJC EoS with the predicted parameters from correlations (PLJC-2) [21], perturbed chain statistical associating fluid theory (PC-SAFT) EoS [29], and also with an EoS based on LJ (6-3) potential function [24]. The PLJC EoS belongs to a class of theoretically based EoSs that are formulated based on continuum space (as opposed to lattice space) liquid state perturbation theories.…”

“…During the last decades, many studies have been devoted to prediction of thermodynamic properties of liquid alkali metals [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], and their alloys [25][26][27][28][29]. Different authors used different equations and methods to predict and reproduce the thermodynamic properties of these systems.…”

Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges

“…Eslami used the Parsafar equation of state to estimate isothermal compressibility, isobaric expansion, and density of liquid metals in high temperature and pressure [15]. Sabzevari and Mousavi predicted the density of these liquids by using artificial neural network and a new equation of state in different operational conditions [16,17].…”