Single effectiveness factor

The mass transport influence is easy to diagnose experimentally. One measures the rate at various values of the Thiele modulus the modulus is easily changed by variation of R, the particle size. Cmshing and sieving the particles provide catalyst samples for the experiments. If the rate is independent of the particle size, the effectiveness factor is unity for all of them. If the rate is inversely proportional to particle size, the effectiveness factor is less than unity and

experimental points allow triangulation on the curve of Figure 10 and estimation of Tj and ( ). It is also possible to estimate the effective diffusion coefficient and thereby to estimate Tj and ( ) from a single measurement of the rate (48). 

Sindlady, heating surface area needs are not direcdy proportional to the number of effects used. For some types of evaporator, heat-transfer coefficients decline with temperature difference as effects are added the surface needed in each effect increases. On the other hand, heat-transfer coefficients increase with temperature level. In a single effect, all evaporation takes place at a temperature near that of the heat sink, whereas in a double effect half the evaporation takes place at this temperature and the other half at a higher temperature, thereby improving the mean evaporating temperature. Other factors to be considered are the BPR, which is additive in a multiple-effect evaporator and therefore reduces the net AT available for heat transfer as the number of effects is increased, and the reduced demand for steam and cooling water and hence the capital costs of these auxiUaries as the number of effects is increased. 

For most practical purposes, the onset of plastic deformation constitutes failure. In an axially loaded part, the yield point is known from testing (see Tables 2-15 through 2-18), and failure prediction is no problem. However, it is often necessary to use uniaxial tensile data to predict yielding due to a multidimensional state of stress. Many failure theories have been developed for this purpose. For elastoplastic materials (steel, aluminum, brass, etc.), the maximum distortion energy theory or von Mises theory is in general application. With this theory the components of stress are combined into a single effective stress, denoted as uniaxial yielding. Tlie ratio of the measure yield stress to the effective stress is known as the factor of safety. 

The subscript refers to a spherical particle. One should also remember that we limited ourselves to a first-order irreversible reaction. Other expressions can be derived but are beyond the scope of this book. Nevertheless, Eq. (35) has important practical implications, since it is possible to discuss the effectiveness of the system by a single dimensionless parameter, (fig. Figure 5.35 shows the effectiveness factor as a function of O,. 

In this exercise we shall estimate the influence of transport limitations when testing an ammonia catalyst such as that described in Exercise 5.1 by estimating the effectiveness factor e. We are aware that the radius of the catalyst particles is essential so the fused and reduced catalyst is crushed into small particles. A fraction with a narrow distribution of = 0.2 mm is used for the experiment. We shall assume that the particles are ideally spherical. The effective diffusion constant is not easily accessible but we assume that it is approximately a factor of 100 lower than the free diffusion, which is in the proximity of 0.4 cm s . A test is then made with a stoichiometric mixture of N2/H2 at 4 bar under the assumption that the process is far from equilibrium and first order in nitrogen. The reaction is planned to run at 600 K, and from fundamental studies on a single crystal the TOP is roughly 0.05 per iron atom in the surface. From Exercise 5.1 we utilize that 1 g of reduced catalyst has a volume of 0.2 cm g , that the pore volume constitutes 0.1 cm g and that the total surface area, which we will assume is the pore area, is 29 m g , and that of this is the 18 m g- is the pure iron Fe(lOO) surface. Note that there is some dispute as to which are the active sites on iron (a dispute that we disregard here). 

The effectiveness factor for a single pore is identical with that for the particle as a whole. Thus the reaction rate per unit mass of catalyst can be written as... 

In Kadanoff s [130, 131] two-dimensional block-spin model four neighbouring spins are assumed to have identical spins, either up or down, near the critical point. The block of four then acts like a single effective spin. The lattice constant of the effective new lattice is double the original lattice constant. The coherence length measured in units of the new lattice constant will hence be at half of its original measure. Repetition of this procedure allows further reduction in by factors of two, until finally one has an effective theory with = 1. At each step it is convenient to define renormalized block spins such that their magnitude is 1 instead of 4. The energy of such blocked spins is... 

The presence (or absence) of pore-diffusion resistance in catalyst particles can be readily determined by evaluation of the Thiele modulus and subsequently the effectiveness factor, if the intrinsic kinetics of the surface reaction are known. When the intrinsic rate law is not known completely, so that the Thiele modulus cannot be calculated, there are two methods available. One method is based upon measurement of the rate for differing particle sizes and does not require any knowledge of the kinetics. The other method requires only a single measurement of rate for a particle size of interest, but requires knowledge of the order of reaction. We describe these in turn. 

Vayenas, C. G. and S. Pavlou. 1987a. Optimal catalyst distribution and generalized effectiveness factors in pellets single reactions with arbitrary kinetics. Chem. Eng. Sci. 42(11) 2633-2645. 

The first column of Table 14.3 gives the response notation (or, equivalently, the factor combination). The next eight columns list the eight factor effects of the model the three main effects (A, B, and C), the three two-factor interactions (AB, AC, and BC), the single three-factor interaction (ABC), and the single offset term (MEAN, analogous to PJ in the equivalent linear model). 

Calculate the grand average (MEAN), the two classical main effects (A and B), and the single two-factor interaction (AB) for the two-factor two-level full factorial design shown in the square plot in Section 14.1. (Assume coded factor levels of -1 and +1). 

Empirical Modeling. The effect of process variables on the rate of depKJsition and properties of electrolessly depKJsited metals is usually studied by one-factor-at-a-time experiments (one-factor experiments are discussed further later in the book). In these experiments the effect of a single variable (factor), such as Xj, in the multivariable process with the response y, y = fixi, %2, X3,. .., x ), is studied by varying the value (level) of this variable while holding the values of the other independent variable fixed, y Any prediction (extrapolation) of the effect of a single variable on... 

Figure 7-13 Plots of effectiveness factor 17 versus Thiele modulus

Calculate the effectiveness factor of a single catalyst pore and of a catalyst slab for zeroth-order... 

The effectiveness factors for the reactions and the components are changing along the length of the reactor. The data above shows the effectiveness factors and rates at the exit. Here the effectiveness factor of a component is computed from the rate of its consumption at the exit. For components which are formed in a single reaction and are not involved in any other reactions, such as styrene, benzene, toluene and carbon dioxide, the effectiveness factor is determined by the effectiveness factor of their reaction, i.e., by the reactions (1) to (3) and (6). Note that for most of the reactions (except for reactions (5)... 

A central arrow in Figure 4 points to a particular single effect. Branches off the central arrow lead to boxes representing specific process steps. Next, principle factors of each process step that can cause or influence the effect are drawn as subbranches of each branch, until a complete cause-and-effect diagram is developed. This should be as detailed a summary as possible. An example of a more complex cause-and-effect diagram is illustrated in Figure 5. A separate summary for each critical product characteristic (e.g., weight variation, dissolution, friability) should be made. 

Thus, we see that in order to obtain the mean field equations of motion, the density matrix of the entire system is assumed to factor into a product of subsystem and environmental contributions with neglect of correlations. The quantum dynamics then evolves as a pure state wave function depending on the coordinates evolving in the mean field generated by the quantum density. As we have seen in the previous sections, these approximations are not valid and no simple representation of the quantum-classical dynamics is possible in terms of single effective trajectories. Consequently, in contrast to claims made in the literature [54], quantum-classical Liouville dynamics is not equivalent to mean field dynamics. 

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