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An equation of state for R227ea from density data through a new extended corresponding states - neural network technique
No full textViscosity equations of pure fluids in an innovative extended corresponding states framework. II. Application to four fluids
No full textThermal conductivity equations of pure fluids in an heuristic extended corresponding states framework. I. Modelling techniques.
No full textEnhancement of the extended corresponding states techniques for thermodynamic modeling. I. Pure fluids.
No full textThis work, limited to pure fluids’ modeling, has two main goals. The first one is to discuss in a rigorous way the modeling
background of the conventional extended corresponding states (ECS) methods proposed in the literature. A critical review is in
the meantime developed allowing to point out the limits of the methods. The second goal is to propose a practicable and plain
solution for the use of ECS as the basic framework to develop a fundamental equation of state (EoS) for a target fluid in a totally
correlative mode. For this purpose the conventional analytical procedure was left out and an optimization procedure, based on
a general function approximator, was applied. The model capability to accurately represent several thermodynamic surfaces of
a number of haloalkanes is verified assuming data generated from the corresponding EoSs. The achieved results show that the
method is robust and straightforward, while the obtained prediction accuracies for the thermodynamic functions are competitive
with those of the available conventional EoSs
Thermal conductivity equations of pure fluids in an heuristic extended corresponding states framework. II. Application to two fluids.
No full textAn extended equation of state modeling method. I. Pure fluids.
No full textA new technique is proposed here to represent the thermodynamic surface of a pure fluid in the fundamental form. The peculiarity of the present method is the extension of a generic equation of state for the target fluid, which is assumed as basic equation, through the distortion of its independent variables by individual shape functions which are represented by a neural network 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 one 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 improves by a factor between 10 and 100, depending on the property and on the region. It has been verified that about hundred density experimental points, together with from ten to twenty 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
An extended equation of state modeling method. II. Mixtures.
No full textThis work is the extension of previous work dedicated to pure fluids. The same method is extended to the representation of thermodynamic properties of a mixture through a fundamental equation of state in terms of Helmholtz energy. The proposed technique exploits the extended corresponding states concept of distorting the independent variables of a dedicated equation of state for a reference fluid using suitable scale factor functions to adapt the equation to experimental data of a target system. An equation of state for the target mixture is used instead of an equation for the reference fluid, completely avoiding the need for a reference fluid. In particular, a Soave-Redlich-Kwong cubic equation with van der Waals mixing rules is chosen. The scale factors, that are functions of temperature, density, and composition of the target mixture, are expressed in the form of a multilayer feedforward neural network, whose coefficients are regressed by minimizing a suitable objective function involving different kinds of mixture thermodynamic data. As a preliminary test, the model is applied to five binary and two ternary haloalkane mixtures, using data generated from existing dedicated equations of state for the selected mixtures. The results show that the method is robust and straightforward for the effective development of a mixture-specific equation of state directly from experimental data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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