Component Data

Compilation of pure-component data including vapor pre s sure s. 

This chapter presents quantitative methods for calculation of enthalpies of vapor-phase and liquid-phase mixtures. These methods rely primarily on pure-component data, in particular ideal-vapor heat capacities and vapor-pressure data, both as functions of temperature. Vapor-phase corrections for nonideality are usually relatively small. Liquid-phase excess enthalpies are also usually not important. As indicated in Chapter 4, for mixtures containing noncondensable components, we restrict attention to liquid solutions which are dilute with respect to all noncondensable components. 

B. The next four data cards contain pure-component data for component one. 

C. The next four data cards contain pure-component data for component two. The same format as used in part B is repeated here. 

NMD(1,J) cols 1-6 six-character identification code for component one this must match the code read in with the pure-component data. 

CHECK FOP MISMATCH BETWEEN URE COMPONENT DATA AND VLE DATA... 

PARIN first loads all pure component data by reading two records per component. The total number of components, M, in the library or data deck must be known beforehand. Next the associ-ation/solvation parameters are input for M components. Finally all the established UNIQUAC binary interaction parameters (or noncondensable-condensable interaction parameters) are read. 

PARCH reads update information from formatted cards, or other input file containing card images. Input having existing component indices replace old data with those indices. Otherwise the new component data is added to the library, with the restriction that the total number of components is always less than or equal to 100. 

The DIPPR Pure Component Data Compilation, Numerica (TM) Version 9.2 (1994). 

Table 4. Component Data and Energy Density for 300 A h Niekel Oxide—Iron Cells...

A key feature of this model is that no data for mixtures are required to apply the regular-solution equations because the solubiHty parameters are evaluated from pure-component data. Results based on these equations should be treated as only quaHtative. However, mixtures of nonpolar or slightly polar, nonassociating chemicals, can sometimes be modeled adequately (1,3,18). AppHcations of this model have been limited to hydrocarbons (qv) and a few gases associated with petroleum (qv) and natural gas (see Gas, natural) processiag, such as N2, H2, CO2, and H2S. Values for 5 and H can be found ia many references (1—3,7). 

To determine the deterioration in component performance and efficiency, the values must be corrected to a reference plane. These corrected measurements will be referenced to different reference planes depending upon the point, which is being investigated. Corrected values can further be adjusted to a transposed design value to properly evaluate the deterioration of any given component. Transposed data points are very dependent on the characteristics of the components performance curves. To determine the characteristics of these curves, raw data points must be corrected and then plotted against representative nondimensional parameters. It is for this reason that we must evaluate the turbine train while its characteristics have not been altered due to component deterioration. If component data were available from the manufacturer, the task would be greatly reduced. 

Drago, J. P., Borkowski, R. J., Pike, D. H., and Goldberg F. F. The In-Plant Reliability Data Base for Nuclear Power Plant Components Data Collection and Methodology Report. NUREG/ CR-2641, ORNL/TM-9216, January 1985. 

The review of the data portion of the Indian Point 2 (IP2) and 3 (IPS) PRA (a 1982 internal document prepared by Consolidated Edison and the New York Power Authority) is confined to the plant-specific and generic component failure and service hour data sections because these were the only segments available to the reviewers. The LERs produced during a ten-year span of IP2 s operation were evaluated to determine their applicability to the PRA data needs. It was eventually decided to use only the LERs generated after IP2 became critical (from May 23, 1973 to December 31, 1979) for the component data base development, based on the availability of failure event information and more uniform operability, testing, and reporting criteria. 

In addition, data obtained from infrared, thermal, and fluorescence spectroscopic studies of the outermost layer of skin, stratum corneum (SC), and its components imply enhancer-improved permeation of solutes through the SC is associated with alterations involving the hydrocarbon chains of the SC lipid components. Data obtained from electron microscopy and x-ray diffraction reveals that the disordering of the lamellar packing is also an important mechanism for increased permeation of drugs induced by penetration enhancers (for a recent review, see Ref. 206). 

Upon input of the system expressed as components the computer would retrieve data for compounds of the type 1 components with each other and with the type 2 components. Data are given in table U. 

Fig. 15. Comparison of the measured and predicted exit concentrations obtained by Todd for a polystyrene polymer containing ethylbenzene as the volatile component. Data were obtained in a twin-screw extruder. From Todd (1974).

Liu et al. [42] reported permeation of mixture of hexanes and octanes through silicalite membranes. It was found that the permeances of the mixture could not be predicted by the single-component data. In the separation of alkane isomers, the permeance of 2,2-DMB is significantly reduced in the presence of n-hexane resulting in a permselectivity much higher than the ideal separation factor [7]. 

Table 5.37 lists the various terms of the lattice energy of some clinopyroxene components. Data refer to the C2/c spatial group. Components Mg2Si206 and Fe2Si20g, which crystallize in Pljlc, must thus be considered fictitious. ... 

It is important to be able to view the structure of the data for the classes. This is done in a variety of ways depending on the analytical methods. The graphical technique most commonly used is that of plotting eigenvectors or principal components. SIMCA uses this method and software has been developed for three-dimensional color display of principal components data. Other plotting techniques are also used in SIMCA. 

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