Plant-specific features and modeling assumptions affecting risk, and Use of IPEs for risk-based regulation. [Pg.392]

A great deal can be learned about the absorption process by applying Eqs. (40) and (41) to plasma concentration versus time data. Since there is no model assumption with regard to the absorption process, the calculated values of At/Vd can often be manipulated to determine the kinetic mechanism that controls absorption. This is best illustrated by an example. [Pg.92]

PWR iriubiliiy riiiisily driven by plant operating characteristics, IPE modeling assumptions, and assessment n liic r tciinn of [Pg.397]

In Fig. 5.5a a simple scheme of reaction steps is proposed. Some of the assumptions of our model are summarized in Table 5.1. The short-hand representation of a surface site is a simplification that does not take into account either detailed structural aspects of the oxide surface or the oxidation state of the metal ion and its coordination number. It implies (model assumption 2 in Table 5.1) that all functional surface groups, such as those in a cross-linked polyhydroxo-oxo acid, are treated as if they were identical. [Pg.166]

In contrast to PCA which can be considered as a method for basis rotation, factor analysis is based on a statistical model with certain model assumptions. Like PCA, factor analysis also results in dimension reduction, but while the PCs are just derived by optimizing a statistical criterion (spread, variance), the factors are aimed at having a real meaning and an interpretation. Only a very brief introduction is given here a classical book about factor analysis in chemistry is from Malinowski (2002) many other books on factor analysis are available (Basilevsky 1994 Harman 1976 Johnson and Wichem 2002). [Pg.96]

In either case, the structure of the solvation shell has to be calculated by otiier methods supplied or introduced ad hoc by some fiirther model assumptions, while charge distributions of the solute and within solvent molecules are obtained from quantum chemistry. [Pg.839]

The impedance data have been usually interpreted in terms of the Randles-type equivalent circuit, which consists of the parallel combination of the capacitance Zq of the ITIES and the faradaic impedances of the charge transfer reactions, with the solution resistance in series [15], cf. Fig. 6. While this is a convenient model in many cases, its limitations have to be always considered. First, it is necessary to justify the validity of the basic model assumption that the charging and faradaic currents are additive. Second, the conditions have to be analyzed, under which the measured impedance of the electrochemical cell can represent the impedance of the ITIES. [Pg.431]

The following criteria are usually directly applied to the calibration set to enable a fast comparison of many models as it is necessary in variable selection. The criteria characterize the fit and therefore the (usually only few) resulting models have to be tested carefully for their prediction performance for new cases. The measures are reliable only if the model assumptions are fulfilled (independent normally distributed errors). They can be used to select an appropriate model by comparing the measures for models with various values of in. [Pg.128]

Sensitivity studies allow estimation of the contribution of various parameters to the total uncertainty in the result of a QRA. Such studies can identify major contributors to overall risk for a list of incidents and can identify which models, assumptions, and data are important to the final risk estimate. [Pg.38]

The denominator n 2 is used here because two parameters are necessary for a fitted straight line, and this makes s2 an unbiased estimator for a2. The estimated residual variance is necessary for constructing confidence intervals and tests. Here the above model assumptions are required, and confidence intervals for intercept, b0, and slope, b, can be derived as follows [Pg.136]

Although the pzc contains all the essential structural information about the metal/solution interface, this information is not immediately apparent but must be appropriately decoded. This necessitates a description of (

An example rather than linking average bubble size to just or essentially the (overall) power input of a particular vessel-impeller combination, dedicated CFD (preferably DNS and LES) allows for studying ( tracking ) the response of bubble size to local and spatial variations in the turbulence levels in a stirred vessel. In this way, the validity of certain modeling assumptions may be affirmed or disproved. Particularly, effects of spatial variations in e which [Pg.217]

From a plot of the internalisation flux against the metal concentration in the bulk solution, it is possible to obtain a value of the Michaelis-Menten constant, Am and a maximum value of the internalisation flux, /max (equation (35)). Under the assumption that kd kml for a nonlimiting diffusive flux, the apparent stability constant for the adsorption at sensitive sites, As, can be calculated from the inverse of the Michaelis-Menten constant (i.e. A 1 = As = kf /kd). The use of thermodynamic constants from flux measurements can be problematic due to both practical and theoretical (see Chapter 4) limitations, including a bias in the values due to nonequilibrium conditions, difficulties in separating bound from free solute or the use of incorrect model assumptions [187,188], [Pg.476]

See also in sourсe #XX -- [ Pg.84 ]

See also in sourсe #XX -- [ Pg.49 ]

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