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Moving from a former work for pure fluids a new modeling technique has been developed for obtaining a fundamental mixture equation of state in the Helmholtz energy form. This model can be considered an evolution of the extended corresponding states method, which is modified from the conventional analytical mode to a heuristic one through the integration of a general function approximator for the representation of the scale factor functions of a target mixture. The assumed approximator is a multilayer feedforward neural network (MLFN) with two outputs, one for each scale factor. A reference pure fluid, conformal with the components of the studied mixture, is chosen and the independent variables of its dedicated equation of state (DEoS) are distorted by the scale factors, which are individual functions of temperature, density, and composition. The MLFN scale factor functions can be obtained from regression on any kind of thermodynamic data of the target mixture. The model capability to accurately represent the thermodynamic surfaces of five binary and two ternary haloalkane mixtures is studied assuming data generated from the corresponding DEoSs. The obtained prediction accuracies for the mixture thermodynamic properties are competitive with those of the available conventional DEoSs. The proposed modeling technique is then robust and straightforward for the effective development of a mixture DEoS from thermodynamic quantities distributed in the range of interest.

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A multiparameter viscosity equation for propane, valid in wide temperature and pressure ranges, was developed through an optimization technique for the functional form. The obtained results are very satisfactory, showing an average absolute deviation of 0.28% for the currently available 1024 primary data points. This is a significant improvement with respect to the reference equation available in the literature. As usual, both the development and the evaluation of the viscosity equation requires a highly accurate equation of state in order to convert the independent variables used for the experimental data, in most applications, (T,P), into the independent variables of the viscosity equation, (T,ρ). The heuristic technique used to develop the equation allows to select consistent data sets and thus it is a powerful tool for screening the available experimental data. The present limit for the accuracy achievable in the representation of the viscosity surface of a pure fluid is set by the uncertainty level of the experimental data rather than by the effectiveness of the proposed modeling method.

A new thermal conductivity equation λ=λ(T,ρ) in a multiparameter format was developed for carbon dioxide through the application of an optimization technique of the functional form. The proposed equation is valid for temperatures from the triple point (Tt=216.592K; Pt=0.51795MPa) to 1000K and pressures up to 200MPa. The calculation of density, which is an independent variable of the equation, from the experimental (T,P) conditions is performed with a high accuracy equation of state for the fluid. The thermal conductivity equation shows an average absolute deviation of 1.19% on the selected 1407 primary data points. Its performances are slightly better than those of the corresponding conventional model by Vesovic et al. [J. Phys. Chem. Ref. Data 19, 763 (1990)] available from the literature; moreover the new equation is simpler to use in particular for the near-critical region. Tables of generated values of carbon dioxide thermal conductivity are provided for check of the code implementations and for quick evaluations.

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A new technique is proposed here to represent the thermodynamic surface of a pure fluid in the fundamental Helmholtz energy form. The peculiarity of the present method is the extension of a generic equation of state for the target fluid, which is assumed as the basic equation, through the distortion of its independent variables by individual shape functions, which are represented by a neural network used as function approximator. The basic equation of state for the target fluid can have the simple functional form of a cubic equation, as, for instance, the Soave-Redlich-Kwong equation assumed in the present study. A set of nine fluids including hydrocarbons, haloalkane refrigerants, and strongly polar substances has been considered. For each of them the model has been regressed and then validated against volumetric and caloric properties generated in the vapor, liquid, and supercritical regions from highly accurate dedicated equations of state. In comparison with the underlying cubic equation of state, the prediction accuracy is improved by a factor between 10 and 100, depending on the property and on the region. It has been verified that about 100 density experimental points, together with from 10 to 20 coexistence data, are sufficient to guarantee high prediction accuracy for different thermodynamic properties. The method is a promising modeling technique for the heuristic development of multiparameter dedicated equations of state from experimental data. KEY WORDS: cubic equation of state; extended equation of state; feed forward neural network; fundamental equation of state; Helmholtz energy equation; pure fluids; thermodynamic properties.

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