Equation Of State For Pure Fluids Research Articles

Generating a Machine-Learned Equation of State for Fluid Properties.

Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental data. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine-learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids.

Generalized corresponding states model for bulk and interfacial properties in pure fluids and fluid mixtures

We have formulated a general approach for transforming an analytical equation of state (EOS) into the crossover form and developed a generalized cubic (GC) EOS for pure fluids, which incorporates nonanalytic scaling laws in the critical region and in the limit ρ→0 is transformed into the ideal gas equation EOS. Using the GC EOS as a reference equation, we have developed a generalized version of the corresponding states (GCS) model, which contains the critical point parameters and accentric factor as input as well as the Ginzburg number Gi. For nonionic fluids we propose a simple correlation between the Ginzburg number Gi and Zc, ω, and molecular weight Mw. In the second step, we develop on the basis of the GCS model and the density functional theory a GCS-density functional theory (DFT) crossover model for the vapor–liquid interface and surface tension. We use the GCS-DFT model for the prediction of the PVT, vapor–liquid equilibrium (VLE) and surface properties of more than 30 pure fluids. In a wide range of thermodynamic states, including the nearest vicinity of the critical point, the GCS reproduces the PVT and VLE surface and the surface tension of one-component fluids (polar and nonpolar) with high accuracy. In the critical region, the GCS-DFT predictions for the surface tension are in excellent agreement with experimental data and theoretical renormalization-group model developed earlier. Using the principle of the critical-point universality we extended the GCS-DFT model to fluid mixtures and developed a field-variable based GCS-FV model. We provide extensive comparisons of the GCS-FV model with experimental data and with the GCS-XV model formulated in terms of the conventional density variable—composition. Far from the critical point both models, GCS-FV and GCS-XV, give practically similar results, but in the critical region, the GCS-FV model yields a better representation of the VLE surface of binary mixtures than the GCS-XV model. We also show that by considering the Ginzburg number Gi as an independent CS parameter the GCS model is capable of reproducing the phase behavior of finite neutral nuclear matter.

Improving cubic EOSS near the critical point by a phase‐space cell approximation

AbstractCubic equations of state (EOSs) are widely used to model the thermodynamic properties of pure fluids and mixtures. However, because they fail to account for the long‐range fluctuations existing in a fluid near the critical point, they do not accurately predict the fluid properties in the critical region. Recently, an approximate renormalization group method was developed that can account for these fluctuations. A similar method is applied to provide corrections to a generalized cubic EOS for pure fluids, which is able to represent all classic cubic EOSs. The proposed approach requires two additional parameters: c̄RG and Δ. The value of c̄RG is correlated to experimental critical compressibility data, while Δ is set equal to 1. The method is applied to predict the saturated liquid density of fluids of different polarity, and the corrections to the original EOS are found to significantly improve the predictions of this property both far from and close to the critical point. Finally, a correlation is presented for the direct evaluation of the parameter c̄RG from the value of the critical compressibility factor.