Rate equations numerical solutions

In the derivation of these unsteady flow equations, no assumptions regarding the nature of the unsteadiness are made. In turbulent flow, therefore, the instantaneous values of the variables will satisfy these equations. Numerical solutions to the above equations are very difficult to obtain and for most purposes it is only the mean values of these variables and the mean heat transfer rate that are required. An attempt is, therefore, usually made to express them in terms of the mean values of the variables. 

In sections 4.2 and 5.1 the symbol A is used instead of 1/r for the exponential coefficients in solutions of rate equations. These solutions are the eigenvalues of the matrix (see section 2.4) determined by the set of rate equations. The following problem can result in some confusion when authors use X as equivalent to or 1/r. The numerical values for As obtained from matrix solutions are always negative while the values for and therefore of x are... 

We further discuss how quantities typically measured in the experiment (such as a rate constant) can be computed with the new formalism. The computations are based on stochastic path integral formulation [6]. Two different sources for stochasticity are considered. The first (A) is randomness that is part of the mathematical modeling and is built into the differential equations of motion (e.g. the Langevin equation, or Brownian dynamics). The second (B) is the uncertainty in the approximate numerical solution of the exact equations of motion. 

The development of combustion theory has led to the appearance of several specialized asymptotic concepts and mathematical methods. An extremely strong temperature dependence for the reaction rate is typical of the theory. This makes direct numerical solution of the equations difficult but at the same time accurate. The basic concept of combustion theory, the idea of a flame moving at a constant velocity independent of the ignition conditions and determined solely by the properties and state of the fuel mixture, is the product of the asymptotic approach (18,19). Theoretical understanding of turbulent combustion involves combining the theory of turbulence and the kinetics of chemical reactions (19—23). 

For noncoustaut diffusivity, a numerical solution of the conseiwa-tion equations is generally required. In molecular sieve zeohtes, when equilibrium is described by the Langmuir isotherm, the concentration dependence of the intracrystalline diffusivity can often be approximated by Eq. (16-72). The relevant rate equation is ... 

Lenhoff, J. Chromatogr., 384, 285 (1987)] or by direct numerical solution of the conservation and rate equations. For the special case of no-axial dispersion with external mass transfer and pore diffusion, an explicit time-domain solution, useful for the case of time-periodic injections, is also available [Carta, Chem. Eng. Sci, 43, 2877 (1988)]. In most cases, however, when N > 50, use of Eq. (16-161), or (16-172) and (16-174) with N 2Np calculated from Eq. (16-181) provides an approximation sufficiently accurate for most practical purposes. 

The numerical solution of these equations is shown in Fig. 23-28. This is a plot of the enhancement fac tor E against the Hatta number, with several other parameters. The factor E represents an enhancement of the rate of transfer of A caused by the reaction compared with physical absorption with zero concentration of A in the liquid. The uppermost line on the upper right represents the pseudo-first-order reaction, for which E = P coth p. 

Analytical solutions also are possible when T is constant and m = 0, V2, or 2. More complex chemical rate equations will require numerical solutions. Such rate equations are apphed to the sizing of plug flow, CSTR, and dispersion reactor models by Ramachandran and Chaud-hari (Three-Pha.se Chemical Reactors, Gordon and Breach, 1983). 

Here, we shall examine a series of processes from the viewpoint of their kinetics and develop model reactions for the appropriate rate equations. The equations are used to andve at an expression that relates measurable parameters of the reactions to constants and to concentration terms. The rate constant or other parameters can then be determined by graphical or numerical solutions from this relationship. If the kinetics of a process are found to fit closely with the model equation that is derived, then the model can be used as a basis for the description of the process. Kinetics is concerned about the quantities of the reactants and the products and their rates of change. Since reactants disappear in reactions, their rate expressions are given a... 

FIG. 18 Scaling plot of L t) from the numerical solution of the rate equations for quenches to different equilibrium states [64] from an initial exponential MWD with L = 2. The final mean lengths are given in the legend. The inset shows the original L t) vs t data. Here = (0.33Lqo). ... 

Numerical approaches for estimating reactivity ratios by solution of the integrated rate equation have been described.124 126 Potential difficulties associated with the application of these methods based on the integrated form of the Mayo-kewis equation have been discussed.124 127 One is that the expressions become undefined under certain conditions, for example, when rAo or rQA is close to unity or when the composition is close to the azeotropic composition. A further complication is that reactivity ratios may vary with conversion due to changes in the reaction medium. 

Inspection of the numerical solutions of the equations shows that, with the exception of Es= 0 kcal/mole, the rate of surface temperature increase with time is very large once the surface temperature reaches approximately 420°K—on the order of 108°K/sec. Because typical autoignition temperatures are of the order of 625°K for composite propellants, the particular value of the ignition temperature does not affect the computed numerical value of the ignition-delay time. 

Both of the numerical approaches explained above have been successful in producing realistic behaviour for lamellar thickness and growth rate as a function of supercooling. The nature of rough surface growth prevents an analytical solution as many of the growth processes are taking place simultaneously, and any approach which is not stochastic, as the Monte Carlo in Sect. 4.2.1, necessarily involves approximations, as the rate equations detailed in Sect. 4.2.2. At the expense of... 

This peculiar form applies when a dimeric molecule dissociates to a reactive monomer that then undergoes a first-order or pseudo-first-order reaction. This scheme is considered in Section 4.3. Unless one can work at either of the limits, the form is such that a numerical solution or the method of initial rates will be needed, since the integrated equation has no solution for [A]r. 

Many reaction schemes with one or more intermediates have no closed-form solution for concentrations as a function of time. The best approach is to solve these differential equations numerically. The user specifies the reaction scheme, the initial concentrations, and the rate constants. The output consists of concentration-time values. The values calculated for a given model can be compared with the experimental data, and the rate constants or the model revised as needed. Methods to obtain numerical solutions will be given in the last section of this chapter. 

Equations (2.22) and (2.23) become indeterminate if ks = k. Special forms are needed for the analytical solution of a set of consecutive, first-order reactions whenever a rate constant is repeated. The derivation of the solution can be repeated for the special case or L Hospital s rule can be applied to the general solution. As a practical matter, identical rate constants are rare, except for multifunctional molecules where reactions at physically different but chemically similar sites can have the same rate constant. Polymerizations are an important example. Numerical solutions to the governing set of simultaneous ODEs have no difficulty with repeated rate constants, but such solutions can become computationally challenging when the rate constants differ greatly in magnitude. Table 2.1 provides a dramatic example of reactions that lead to stiff equations. A method for finding analytical approximations to stiff equations is described in the next section. 

Practical problems involving variable-density PFRs require numerical solutions, and for these it is better to avoid expanding Equation (3.4) into separate derivatives for a and u. We could continue to use the molar flow rate, Na, as the dependent variable, but prefer to use the molar flux,... 

Fortunately, it is possible to develop a general-purpose technique for the numerical solution of Equation (3.9), even when the density varies down the tube. It is first necessary to convert the component reaction rates from their normal dependence on concentration to a dependence on the molar fluxes. This is done simply by replacing a by and so on for the various... 

Among these models, the Fukui-Kaneko model is regarded as the most accurate one because it is derived from the linearized Boltzmaim equation. However, the flow rate coefficient, Qp, in the Fukui-Kaneko model, is not a unified expression, as defined in Eqs (6)-(8). This would cause some inconveniences in a practical numerical solution of the modified Reynolds equation. Recently, Huang and Hu [17] proposed a representation of Qp, as shown in Eq (10), for the whole range of D from 0.01 to 100, by a data-fitting method, to replace the segmented expressions of the Fukui-Kaneko model... 

Since the measurements of conductance change are not directly related to the composition of the solution, as an alternative method numerical integration of the differential rate equations implied by the proposed mechanism was employed. The second order rate coefficients obtained by this method are... 

The parameter y reflects the sensitivity of the chemical reaction rate to temperature variations. The parameter represents the ratio of the maximum temperature difference that can exist within the particle (equation 12.3.99) to the external surface temperature. For isothermal pellets, / may be regarded as zero (keff = oo). Weisz and Hicks (61) have summarized their numerical solutions for first-order irreversible... 

Equations 12.6.2 to 12.6.4 and the relation between s, y, and are sufficient to calculate the global rate at specified values of TB and CB. Unfortunately, information on the last relation is rather limited. The curves presented in Figure 12.10 and reference 61 give the desired relation for first-order kinetics, but numerical solutions for other reaction orders are not available to this extent we will presume that numerical solutions may be generated if needed for design purposes. 

In the general case, when arbitrary interaction profiles prevail, the particle deposition rate must be obtained by solving the complete transport equations. The first numerical solution of the complete convective diffusional transport equations, including London-van der Waals attraction, gravity, Brownian diffusion and the complete hydrodynamical interactions, was obtained for a spherical collector [89]. Soon after, numerical solutions were obtained for a panoplea of other collector geometries... 

---