Porous electrodes are systems with distributed parameters, and any loss of efficiency is dne to the fact that different points within the electrode are not equally accessible to the electrode reaction. Concentration gradients and ohmic potential drops are possible in the electrolyte present in the pores. Hence, the local current density, i (referred to the unit of true surface area), is different at different depths x of the porous electrode. It is largest close to the outer surface (x = 0) and falls with increasing depth inside the electrode. [Pg.338]

Butkovskii, A. G., Characteristics of Systems With Distributed Parameters, Nauka, Moscow, 1979 [in Russian], [Pg.352]

FIG. 19 Ion distribution function P(r) (left) and mean electrostatic potential i/4r) (right) for systems with Manning parameter = 4.2. The nine curves correspond to different numbers Ns of 2 2 salt molecules added to the box Ns 8, 17, 34, 68, 135, 270, 380, 540, 760. In the distribution function the salt content increases from bottom to top in the mean electrostatic potential it increases from top to bottom. [Pg.93]

MACROKINETICS OF ELECTROCHEMICAL PROCESSES (SYSTEMS WITH DISTRIBUTED PARAMETERS) [Pg.334]

The crucible contents of the TGA were modelled as a dynamic system with distributed parameters assuming plate geometry. The radius of the sample was taken as the characteristic length for the heat transport. The overall rate of reaction was approximated by an irreversible reaction of T order biomass -> solids + volatiles, assuming the validity of the Arrhenius expression for the temperature dependency of the rate constant. [Pg.1078]

Ishida, M., and Kawamura, K., "Energy and Exergy Analysis of a Chemical Process System with Distributed Parameters Based on the Enthalph-Directed Factor Diagram," Ind. Eng. Chem. Process Des. Dev., 21, 690 (1982). [Pg.444]

Process Model. To simplify computational problems and to allow simultaneous examination of physiological control phenomena, it was necessary to approximate the actual distributed parameter capillary-tissue system with lumped parameter models. [Pg.302]

The formulation described above provides a useful framework for treating feedback control of combustion instability. However, direct application of the model to practical problems must be exercised with caution due to uncertainties associated with system parameters such as and Eni in Eq. (22.12), and time delays and spatial distribution parameters bk in Eq. (22.13). The intrinsic complexities in combustor flows prohibit precise estimates of those parameters without considerable errors, except for some simple well-defined configurations. Furthermore, the model may not accommodate all the essential processes involved because of the physical assumptions and mathematical approximations employed. These model and parameter uncertainties must be carefully treated in the development of a robust controller. To this end, the system dynamics equations, Eqs. (22.12)-(22.14), are extended to include uncertainties, and can be represented with the following state-space model [Pg.361]

A new model has been developed, dealing with the current density and the consequent Joule heat distribution between the specimen and the die [25]. Thermal balances, as given in Eq. (6.2), where Joule heat is expressed in terms of voltage gradient, are coupled to the current density balances, i.e., Kirchhoff law with distributed parameters in a 2D cylindrical coordinate system [Pg.407]

Electrochemical macrokinetics deals with the combined effects of polarization characteristics and of ohmic and diffusion factors on the current distribution and overall rate of electrochemical reactions in systems with distributed parameters. The term macrokinetics is used (mainly in Russian scientific publications) to distinguish these effects conveniently from effects arising at the molecular level. [Pg.334]

This, more physical model that visualizes failure to result from random "shocks," was specialized from the more general model of Marshall and Olkin (1967) by Vesely (1977) for sparse data for the ATWS problem. It treats these shocks as binomially distributed with parameters m and p (equation 2.4-9). The BFR model like the MGL and BPM models distinguish the number of multiple unit failures in a system with more than two units, from the Beta Factor model, [Pg.128]

Figure 13. Stochastic simulation of the fluorescence intensity (top) and frequency (middle) correlation functions for a model of spectral diffusion in a glass. The molecule is coupled to a three dimensional distribution of tunneling systems with distributed microscopic parameters. The bottom panel shows the timescale (horizontal) and amplitude of the frequency jumps (relative to the lifetime limited linewidth, vertical scale). The inset in the top panel shows the line-shape and the lifetime limited linewidth as a small bar (from Ref. 77). |

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