Enthalpy/entropy computation

Table 3.2 presents a selection of the most used thermodynamic options for phase equilibrium with suitable enthalpy and entropy methods. The accuracy of both phase equilibrium and enthalpy/entropy computation must be examined when using EOS models. For example, often a cubic EOS underestimates the enthalpy of vaporisation. In this case other methods are more accurate, as those based on three-parameters corresponding states law (Lee-Kesler, Curl-Pitzer, etc.). Mixtures rich in components with particular behaviour, as or CH, need special methods for accurate simulation. When binary interaction parameters for liquid activity models are absent, the UNI FAC predictive method may be employed. It is worth to note that UNIFAC is suitable only for exploratory purposes, but not for final design. When high non-ideal mixtures are involved at higher pressure then the combination of EOS with liquid activity models is recommended (see Chapter 6). 

The improvement in AT-values does not automatically ensure accuracy of other properties. In this example the estimation of liquid volume is poor for SRK, but acceptable for PR. Without interaction coefficients the prediction of the liquid volume is even better Note that when the volumetric properties are important, as in reservoir engineering, special equation of state or mixing rules should be applied, as Teja-Sandler EOS given in Chapter 5. The same observation holds for the enthalpy of vaporisation, which could be in serious error. Another method for enthalpy/entropy computation should be used, as for example based on the principle of corresponding states. 

Whether AH for a projected reaction is based on bond-energy data, tabulated thermochemical data, or MO computations, there remain some fundamental problems which prevent reaching a final conclusion about a reaction s feasibility. In the first place, most reactions of interest occur in solution, and the enthalpy, entropy, and fiee energy associated with any reaction depend strongly on the solvent medium. There is only a limited amount of tabulated thermochemical data that are directly suitable for treatment of reactions in organic solvents. Thermodynamic data usually pertain to the pure compound. MO calculations usually refer to the isolated (gas phase) molecule. Estimates of solvation effects must be made in order to apply either experimental or computational data to reactions occurring in solution. 

The h-S diagram becomes most convenient in following rocket motor processes and this is the reason for its introduction. The conveniences obtained are generally hidden by machine computation programs which essentially deal with the enthalpy-entropy process for the expansion process, h is the sensible enthalpy only. Theoretical performance calculations are performed in terms of the total enthalpy which is here defined as the sum of the sensible and chemical enthalpies only. 

The physical property monitors of ASPEN provide very complete flexibility in computing physical properties. Quite often a user may need to compute a property in one area of a process with high accuracy, which is expensive in computer time, and then compromise the accuracy in another area, in order to save computer time. In ASPEN, the user can do this by specifying the method or "property route", as it is called. The property route is the detailed specification of how to calculate one of the ten major properties for a given vapor, liquid, or solid phase of a pure component or mixture. Properties that can be calculated are enthalpy, entropy, free energy, molar volume, equilibrium ratio, fugacity coefficient, viscosity, thermal conductivity, diffusion coefficient, and thermal conductivity. 

It can be anticipated that the computation of A//soi and AAsoi is more delicate than the prediction of AGsoi, which benefits from the enthalpy-entropy compensation. Accordingly, the suitability of the QM-SCRF models to predict the enthalpic and entropic components of the free energy of solvation is a challenging issue, which could serve to refine current solvation continuum models. This contribution reports the results obtained in the framework of the MST solvation model [15] to estimate the enthalpy (and entropy) of hydration for a set of neutral compounds. To this end, we will first describe the formalism used to determine the MST solvation free energy and its enthalpic component. Then, solvation free energies and enthalpies for a series of typical neutral solutes will be presented and analyzed in light of the available experimental data. Finally, collected data will be used to discuss the differential trends of the solvation in water. 

Receptor Flexibility in Computational Design Induced-Fit Versus Conformational Selection Enthalpy-Entropy Compensation (EEC) Allostery Conclusion... 

Carbon Allotropes.—Thermodynamic functions of single-crystal graphite have been assessed in the t emperature range 0—3000 K.7 The experimental specific heats have been described by a computer-fitted single equation enthalpies, entropies, and free energies have also been calculated. 

Fullerenes and metallofullerenes were for the first time observed in the gas phase about 20 years ago and then prepared in crystalline form about 15 years ago. An enormous amount of observed and computed data has been obtained during the period. The chapter surveys various computational aspects of fullerene science including rich isomerism and the enthalpy-entropy interplay both of which represent essential features of fullerene and metaUofuUerene formations. 

The contributions Z ° and are represented by generalised functions containing as parameters the reduced temperature and pressure. These have been obtained by using a special form of the BWR-EOS. Mixture critical parameters and acentric factor are calculated by means of mixing rules, which do not have interaction parameters. Tables of values for hand calculations may be found in Reid et al. (1987). Graphical representations of contributions are presented in Perry (1997). Note that this method can be used to compute phase properties (specific volume, enthalpy, entropy) for both vapour and liquid phase. It has been accepted as accurate option for enthalpy and entropy of hydrocarbons and slightly polar components. 

This chapter reviews the fundamental concepts in thermodynamics that a user should master to obtain reliable results in simulation. The thermodynamic network (equations 5.39 to 5.42, and 5.68 to 5.74) links the fundamental thermodynamic properties of a fluid, as enthalpy, entropy, Gibbs free energy and fiigacity, with the primary measurable state parameters, as temperature, pressure, volumes, concentrations. The key consequence of the thermodynamic network is that a comprehensive computation of properties is possible with a convenient PVT model and only a limited number of fundamental physical properties, as critical co-ordinates and ideal gas heat capacity. 

The molecular properties, such as geometry, vibrational frequencies, and rotational constants, are needed to compute thermodynamic properties such as enthalpy, entropy, and Gibbs free energy through calculation of the partition functions of the substances using statistical mechanics methods. 

Another valuable source is the thermochemical property database assembled by Burcat and Ruscic [29], which is available online at ftp // ftp.technion.ac.il/pub/supported/aetdd/thermodynamics/. This collection is regularly updated by Prof. Burcat. It contains data for 1500 species, presented in the form of polynomial coefficients that can be used to compute the enthalpy, entropy, and heat capacity as a function of temperature. While Burcat s tables include a number of aluminum-oxygen compounds, they do not happen to include the aluminum-chlorine species that we have been using as an example. Of course, there are many other handbooks and compilations of thermodynamic properties. However, the vast majority of these focus on organic compounds and/or condensed phase species. Standard handbooks, such as the CRC Handbook of Chemistry and Physics, rarely have any information not included in the sources cited above. 

Thermodynamic properties are characteristics of a system (e.g., pressure, temperature, density, specific volume, enthalpy, entropy, etc.). Because properties depend only on the state of a system, they are said to be path independent (unlike heat and work). Extensive properties are mass dependent (e.g., total system energy and system mass), whereas intensive properties are independent of mass (e.g., temperature and pressure). Specific properties are intensive properties that represent extensive properties divided by the system mass, for example, specific enthalpy is enthalpy per unit mass, h = H/m. In order to apply thermodynamic balance equations, it is necessary to develop thermodynamic property relationships. Properties of certain idealized substances (incompressible liquids and ideal gases with constant specific heats) can be calculated with simple equations of state however, in general, properties require the use of tabulated data or computer solutions of generalized equations of state. 

This paper describes our progress to date on development of a computer model based on a thermodynamlcally-conslstent correlation that will accurately and reliably predict enthalpies, entropies, densities and ultimately K-values for synthetic gas systems over the range of typical synthetic gas processing conditions. 

---