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Elimination Model

In a two-compartment model, /3 is equivalent to k in the one-compartment model. Therefore, the terminal half-life for the elimination of a chemical compound following two-compartment model elimination can be calculated from the equation (i = 0.693/ti/i ... [Pg.273]

In the flow-through model, any mineral mass present at the end of a reaction step is sequestered from the equilibrium system to avoid back-reaction. At the end of each step, the model eliminates the mineral mass (including any sorbed species) from the equilibrium system, keeping track of the total amount removed. To do so, it applies Equation 13.11 for each mineral component and sets each nk to a vanishingly small number. It is best to avoid setting nk to exactly zero in order to maintain the mineral entries Ak in the basis. The model then updates the system composition according to Equations 13.5-13.7 and takes another reaction step. [Pg.199]

An attempt has been made by Tsouris and Tavlarides[5611 to improve previous models for breakup and coalescence of droplets in turbulent dispersions based on existing frameworks and recent advances. In both the breakup and coalescence models, two-step mecha-nisms were considered. A droplet breakup function was introduced as a product of droplet-eddy collision frequency and breakup efficiency that reflect the energetics of turbulent liquid-liquid dispersions. Similarly, a coalescencefunction was defined as a product of droplet-droplet collision frequency and coalescence efficiency. The existing coalescence efficiency model was modified to account for the effects of film drainage on droplets with partially mobile interfaces. A probability density function for secondary droplets was also proposed on the basis of the energy requirements for the formation of secondary droplets. These models eliminated several inconsistencies in previous studies, and are applicable to dense dispersions. [Pg.331]

Fig. 17 Plot of the calculated secondary deuterium KIE versus the extent of O—H bond formation for the model elimination reaction at 45°C Models 1 and 2 have different imaginary frequencies and no coupling of the Ca—D bending vibrational motion with the C0—H stretching motion in the transition state. Models 3,4 and 5 have increasing extents of coupling between the Ca—D bending and C —H stretching motion in the transition state. Reproduced, with permission, from Saunders (1997). Fig. 17 Plot of the calculated secondary deuterium KIE versus the extent of O—H bond formation for the model elimination reaction at 45°C Models 1 and 2 have different imaginary frequencies and no coupling of the Ca—D bending vibrational motion with the C0—H stretching motion in the transition state. Models 3,4 and 5 have increasing extents of coupling between the Ca—D bending and C —H stretching motion in the transition state. Reproduced, with permission, from Saunders (1997).
This simplification of the model eliminates some preliminary aspects of the process which sometimes have considerable importance, such as the processes bringing into contact separate reactions partners. We shall return later to this point for reactions in solution but let us consider first reactions in gas phase. [Pg.21]

Counterpropagation (CPG) Neural Networks are a type of ANN consisting of multiple layers (i.e., input, output, map) in which the hidden layer is a Kohonen neural network. This model eliminates the need for back-propagation, thereby reducing training time. [Pg.112]

This assumption is essentially the same for all PK models described in this chapter. See Section 10.7.1.3 for the details regarding this assumption. For the standard two-compartment model, elimination is assumed to occur only from compartment 1. [Pg.240]

As previously discussed, some statisticians prefer to start with a larger model (backward elimination) and from that model, eliminate x, predictor variables that do not contribute significantly to the increase in SSr or decrease in SSg. Others prefer to build a model using forward selection. The strategy is up to the researcher. A general rule is that the lower-order exponents appear first in the model. This ensures that the higher-order variables are removed first if they do not contribute. For example,... [Pg.245]

The costs of electricity generated from new coal-fired power plants at Natchitoches. Louisiana, were estimated with the power plant model — with appropriate changes to represent coal — for comparison with the costs of PEF-generated electricity. The results showed that these costs are comparable, particularly in the case of a PEF owned and operated by a municipality or some other governmental entity. This comparison, being done with the same model, eliminates any bias which may affect a comparison of cost estimates from different sources. [Pg.530]

Figure 3.13 Scheme and setup of onecompartment intravenous bolus model eliminated exclusively by urinary excretion. X, mass (amount) of drug in the blood/body at time t X, mass (amount) of unchanged drug in the urine at time t fC , first-order excretion rate constant. [Pg.43]

Figure 3.13 shows a scheme and setup for a one-compartment intravenous bolus model eliminated exclusively by urinary excretion. [Pg.43]

In 1980, Bahill and coworkers presented a linear fourth order model of the oculomotor plant, based on physiological evidence, that provides an excellent match between model predictions and eye movement data. This model eliminates the differences seen between velocity predictions of the model and the data, and also the acceleration predictions of the model and the data. For ease in presentation, the modification of this model by Enderle and coworkers [1984] will be used. [Pg.258]

While the initial rate is an attractive preliminary tool for model elimination, three important considerations must be noted ... [Pg.176]

Instantaneous diffusion oo-approach) model assumes that the catalyst is virtually distributed at the gas/washcoat interface so that there is infinitely fast mass transport within the washcoat. This model eliminates the washcoat parameters, such as its thickness and porosity, and the diameters of the inner pores. Therefore, oo-approach does not account for internal mass transport limitations that are due to a porous layer. It means that mass fractions of gas-phase species on the surface are obtained by the balance of production or depletion rate with diffusive and convective processes (Deutschmann, 2008 Kee et al., 2001 Wamatz, 1992). Thus, the net production rate of each chemical species due to surface reactions can be balanced with the diffusive flux of that species at the gas-surface boundary, assuming that no deposition or ablation of chemical species occurs on/from the catalyst surface ... [Pg.53]

We start with a microscopic polymer model, whose state is specifiedby a point in 6N-dimensional phase space, z e f with z = (ri,.... r pj,.... p ), a short notation for the positions and momenta of all N particles. The model is described by the microscopic Hamiltonian H(z) with inter- and intramolecular interactions. The coarse-grained model eliminates some of the (huge number of) microscopic degrees of freedom. The level of detail that is retained is specified by the choice of coarsegrained variables x = (xi,..., ) with... [Pg.347]

Due to obvious deficiencies in the calculations with previous APROS versions a new solution model for the thermal stratification of APROS code was developed [23]. The old method used upwind solution for the enthalpy. Due to numeric diflfiision the code lost information about the stratified layer. The new higher order numeric method uses information from three consecutive nodes to solve the transported liquid enthalpy. The new enthalpy solution contains a special weight function, which is calculated from liquid enthalpies of the three nodes. The experiments GDE-41 and GDE-43 were recalculated with the new model (Fig. 4). The model was also tested separately with a standalone PSIS [24]. The calculation results were good. The new model eliminated significantly the numerical diliusion and restricted the spreading of the thermal front. [Pg.192]


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See also in sourсe #XX -- [ Pg.29 , Pg.30 ]




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Backward Elimination Predictors Removed from the Model

Compartment model with gamma-distributed elimination flow rate

Dependent eliminating from models

Eliminating parameters from models

Elimination PBPK models

Elimination reactions computational modeling

Instantaneous absorption models first-order elimination

Model Studies of Reductive Elimination in the Rh System

Pharmacokinetic models, biologically based elimination

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