Mathematical analytical solution

Because mathematical-analytical solutions do not exist, stages I-III must be investigated separately to elaborate the kinetic and thermal data ki and A//xi for instance,... 

In applying quantum mechanics to real chemical problems, one is usually faced with a Schrodinger differential equation for which, to date, no one has found an analytical solution. This is equally true for electronic and nuclear-motion problems. It has therefore proven essential to develop and efficiently implement mathematical methods which can provide approximate solutions to such eigenvalue equations. Two methods are widely used in this context- the variational method and perturbation theory. These tools, whose use permeates virtually all areas of theoretical chemistry, are briefly outlined here, and the details of perturbation theory are amplified in Appendix D. 

The solution phase has been characterized in the past by a concentration on methods to obtain analytic solutions to the mathematical... 

The goal of approximate and numerical methods is to provide convenient techniques for obtaining useful information from mathematical formulations of physical problems. Often this mathematical statement is not solvable by analytical means. Or perhaps analytic solutions are available but in a form that is inconvenient for direct interpretation... 

Theoretical representation of the behaviour of a hydrocyclone requires adequate analysis of three distinct physical phenomenon taking place in these devices, viz. the understanding of fluid flow, its interactions with the dispersed solid phase and the quantification of shear induced attrition of crystals. Simplified analytical solutions to conservation of mass and momentum equations derived from the Navier-Stokes equation can be used to quantify fluid flow in the hydrocyclone. For dilute slurries, once bulk flow has been quantified in terms of spatial components of velocity, crystal motion can then be traced by balancing forces on the crystals themselves to map out their trajectories. The trajectories for different sizes can then be used to develop a separation efficiency curve, which quantifies performance of the vessel (Bloor and Ingham, 1987). In principle, population balances can be included for crystal attrition in the above description for developing a thorough mathematical model. 

The overall set of partial differential equations that can be considered as a mathematical characterization of the processing system of gas-liquid dispersions should include such environmental parameters as composition, temperature, and velocity, in addition to the equations of bubble-size and residence-time distributions that describe the dependence of bubble nucleation and growth on the bubble environmental factors. A simultaneous solution of this set of differential equations with the appropriate initial and boundary conditions is needed to evaluate the behavior of the system. Subject to the Curie principle, this set of equations should include the possibilities of coupling effects among the various fluxes involved. In dispersions, the possibilities of couplings between fluxes that differ from each other by an odd tensorial rank exist. (An example is the coupling effect between diffusion of surfactants and the hydrodynamics of bubble velocity as treated in Section III.) As yet no analytical solution of the complete set of equations has been found because of the mathematical difficulties involved. To simplify matters, the pertinent transfer equation is usually solved independently, with some simplifying assumptions. 

This is the general result for a second-order batch reaction. The mathematical form of the equation presents a problem when the initial stoichiometry is perfect, ao =bo- Such problems are common with analytical solutions to ODEs. Special formulas are needed for special cases. 

Several examples of the application of quantum mechanics to relatively simple problems have been presented in earlier chapters. In these cases it was possible to find solutions to the Schrtidinger wave equation. Unfortunately, there are few others. In virtually all problems of interest in physics and chemistry, there is no hope of finding analytical solutions, so it is essential to develop approximate methods. The two most important of them are certainly perturbation theory and the variation method. The basic mathematics of these two approaches will be presented here, along with some simple applications. 

The various contributions can also be classified in accordance with the optimization techniques used. However, this method of organization gives rise to an even more diverse classification, since the techniques used range all the way from rules of thumb (A3-A5, M6-M8, Ol, T2) and analytical solution (S8) to the more recent developments in mathematical programming. Most of the techniques reported are continuous, but some are discrete (C8, R5) and still others are of mixed integer types (G3). Table VI shows such a classification for the papers reviewed. It is clearly beyond the scope of this review to delve into the mathematical bases of these methods. We shall... 

In the study of a process or a phenomenon to solve specific problems, mathematical modeling is the process of representing mathematically the essential elements of a process or a phenomenon of the system and the interactions of the elements with one another. Computer simulation is the process of experimenting with the model by using the computer as a tool, l.e. a computer is used to obtain solutions to the mathematical relationships of the model. The model usually is not a complete representation of the system, which often Involves Inclusion of so many details that one can be overwhelmed by its complexity. Computer is not a required tool to carry out simulation as there are mathematical models which have analytical solutions. 

The design q>roblem can be approached at various levels of sophistication using different mathematical models of the packed bed. In cases of industrial interest, it is not possible to obtain closed form analytical solutions for any but the simplest of models under isothermal operating conditions. However, numerical procedures can be employed to predict effluent compositions on the basis of the various models. In the subsections that follow, we shall consider first the fundamental equations that must be obeyed by all packed bed reactors under various energy transfer constraints, and then discuss some of the simplest models of reactor behavior. These discussions are limited to pseudo steady-state operating conditions (i.e., the catalyst activity is presumed to be essentially constant for times that are long compared to the fluid residence time in the reactor). 

The solution phase has been characterized in the past by a concentration on methods to obtain analytic solutions to the mathematical equations. These efforts have been most fruitful in the area of the linear equations such as those just given. However, many natural phenomena are nonlinear. While there are a few nonlinear problems that can be solved analytically, most cannot. In those cases, numerical methods are used. Due to the widespread availability of software for computers, the engineer has quite good tools available. 

Hong and Noolandi (1978a) first gave an analytical solution for the diffusion equation of an e-ion pair in the absence of an external field—that is, of Eq. (7.30) with F = -eVer2, where is the dielectric constant of the medium. They then extended their solution in the presence of an external field of arbitrary strength (Hong and Noolandi, 1978b). Since the method involves fairly complex mathematical manipulations, we will only present its outline and some important conclusions. 

There also exists an alternative theoretical approach to the problem of interest which goes back to "precomputer epoch" and consists in the elaboration of simple models permitting analytical solutions based on prevailing factors only. Among weaknesses of such approaches is an a priori impossibility of quantitative-precise reproduction for the characteristics measured. Unlike articles on computer simulation in which vast tables of calculated data are provided and computational tools (most often restricted to standard computational methods) are only mentioned, the articles devoted to analytical models abound with mathematical details seemingly of no value for experimentalists and present few, if any, quantitative results that could be correlated to experimentally obtained data. It is apparently for this reason that interest in theoretical approaches of this kind has waned in recent years. 

Steady-state mathematical models of single- and multiple-effect evaporators involving material and energy balances can be found in McCabe et al. (1993), Yannio-tis and Pilavachi (1996), and Esplugas and Mata (1983). The classical simplified optimization problem for evaporators (Schweyer, 1955) is to determine the most suitable number of effects given (1) an analytical expression for the fixed costs in terms of the number of effects n, and (2) the steam (variable) costs also in terms of n. Analytic differentiation yields an analytical solution for the optimal n, as shown here. 

A rigorous mathematical formalism of chemical bonding is possible only through the quantum mechanical treatment of molecules. However, obtaining analytical solutions for the Schrodinger wave equation is not possible even for the simplest systems with more than one electron and as a result attempts have been made to obtain approximate solutions a series of approximations have been introduced. As a first step, the Bom-Oppenheimer approximation has been invoked, which allows us to treat the electronic and nuclear motions separately. In solving the electronic part, mainly two formalisms, VB and molecular orbital (MO), have been in use and they are described below. Both are wave function-based methods. The wave function T is the fundamental descriptor in quantum mechanics but it is not physically measurable. The squared value of the wave function T 2dT represents probability of finding an electron in the volume element dr. 

Rate equations, such as equation 17.85, make no attempt to distinguish mechanisms of transfer within a pellet. All such mechanisms are taken into account within the rate constant k. A more fundamental approach is to select the important factors and combine them to form a rate equation, with no regard to the mathematical complexity of the equation. In most cases this approach will lead to the necessity for numerical solutions although for some limiting conditions, useful analytical solutions are possible, particularly that presented by Rosen(41). ... 

One choice of basis function, based on a quadrilateral patch, is illustrated in Figure 15.2c. In the figure the element in the fth row andyth column of the mesh is assumed to have a magnitude that varies within the patch the derivative properties may be important as well. The choice of fifix, y) is not arbitrary it is made to reflect certain mathematical qualities derived, perhaps, from prior knowledge of the general behavior of similar systems as well as properties that simplify the solution process to follow. One immediately practical constraint is that the fifix, y) must satisfy the boundary conditions. Another property is that the patches meet smoothly at the intersections this is usually obtained by continuity of fifix, y) to first and second order in the derivatives. It is also convenient in many applications to choose combinations of products of functions separately dependent on x and y, reminiscent of the analytic solution, Eq. (15.2). 

Even with constant dispersion coefficients, accounting for the velocity profile still creates difficulties in the solution of the partial differential equation. Therefore it is common to take the velocity to be constant at its mean value u. With all the coefficients constant, analytical solution of the partial differential equation is readily obtainable for various situations. This model with flat velocity profile and constant values for the dispersion coefficients is called the dispersed plug-flow model, and is characterized mathematically by Eq. (1-4). The parameters of this model are Dr, Dl and u. 

Another reason for searching for analytical solutions is that we can only solve numerically a problem that is well posed mathematically. We must program a valid mathematical expression of the problem on the computer or the answers may be nonsense. The need for proper descriptions of the equations, initial and bomdary conditions, and stoichiometric relations among the variables is the same whether one is interested in an analytical or a numerical solution. 

In this chapter we consider the performance of isothermal batch and continuous reactors with multiple reactions. Recall that for a single reaction the single differential equation describing the mass balance for batch or PETR was always separable and the algebraic equation for the CSTR was a simple polynomial. In contrast to single-reaction systems, the mathematics of solving for performance rapidly becomes so complex that analytical solutions are not possible. We will first consider simple multiple-reaction systems where analytical solutions are possible. Then we will discuss more complex systems where we can only obtain numerical solutions. 

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