Chemical reactors mathematical model

A survey of the mathematical models for typical chemical reactors and reactions shows that several hydrodynamic and transfer coefficients (model parameters) must be known to simulate reactor behaviour. These model parameters are listed in Table 5.4-6 (see also Table 5.4-1 in Section 5.4.1). Regions of interfacial surface area for various gas-liquid reactors are shown in Fig. 5.4-15. Many correlations for transfer coefficients have been published in the literature (see the list of books and review papers at the beginning of this section). The coefficients can be evaluated from those correlations within an average accuracy of about 25%. This is usually sufficient for modelling of chemical reactors. Mathematical models of reactors arc often more sensitive to kinetic parameters. Experimental methods and procedures for parameters estimation are discussed in the subsequent section. 

I. Chemical reactors—Mathematical models— Congresses. 2. Catalysis—Mathematical models— Congresses. 

This involves knowledge of chemistry, by the factors distinguishing the micro-kinetics of chemical reactions and macro-kinetics used to describe the physical transport phenomena. The complexity of the chemical system and insufficient knowledge of the details requires that reactions are lumped, and kinetics expressed with the aid of empirical rate constants. Physical effects in chemical reactors are difficult to eliminate from the chemical rate processes. Non-uniformities in the velocity, and temperature profiles, with interphase, intraparticle heat, and mass transfer tend to distort the kinetic data. These make the analyses and scale-up of a reactor more difficult. Reaction rate data obtained from laboratory studies without a proper account of the physical effects can produce erroneous rate expressions. Here, chemical reactor flow models using mathematical expressions show how physical... 

Reactor mathematical modeling is a powerful tool for simulating the physical and chemical phenomena occurring in a reactor. The improvement of hardware and software allows reactor designers to work with more and more detailed and reliable algorithms, by which it is possible to understand behavior and performance even before fabricating the reactor itself. 

Keywords polymerization kinetics, polymerization reactors, mathematical modelling, molecular weight distribution (MWD), chemical composition distribution (CCD), Ziegler-Natta catalysts, metallocenes, microstructure, isotacticity distribution, mass transfer resistances, heat transfer resistances, effects of multiple site types. 

Scale- Up of Electrochemical Reactors. The intermediate scale of the pilot plant is frequendy used in the scale-up of an electrochemical reactor or process to full scale. Dimensional analysis (qv) has been used in chemical engineering scale-up to simplify and generalize a multivariant system, and may be appHed to electrochemical systems, but has shown limitations. It is best used in conjunction with mathematical models. Scale-up often involves seeking a few critical parameters. Eor electrochemical cells, these parameters are generally current distribution and cell resistance. The characteristics of electrolytic process scale-up have been described (63—65). 

Tosun, G., 1992. A mathematical model of mixing and polymerization in a semibatch stirred tank reactor. American Institution of Chemical Engineers Journal, 38, 425 37. 

Many elements of a mathematical model of the catalytic converter are available in the classical chemical reactor engineering literature. There are also many novel features in the automotive catalytic converter that need further analysis or even new formulations the transient analysis of catalytic beds, the shallow pellet bed, the monolith and the stacked and rolled screens, the negative order kinetics of CO oxidation over platinum,... 

In spite of all doubts, mathematical modelling in fine chemicals process development is strongly recommended. The following steps in mathematical modelling of chemical reactors can be distinguished ... 

Following the first preliminary comparison, a next step could be to find a set of parameters, that give the best or optimal fit to the experimental data. This can be done by a manual, trial-and-error procedure or by using a more sophisticated mathematical technique which is aimed at finding those values for the system parameters that minimise the difference between values given by the model and those obtained by experiment. Such techniques are general, but are illustrated here with special reference to the dynamic behaviour of chemical reactors. 

Mathematical models of tubular chemical reactor behaviour can be used to predict the dynamic variations in concentration, temperature and flow rate at various locations within the reactor. A complete tubular reactor model would however be extremely complex, involving variations in both radial and axial... 

Since these two types of processes have drastically different effects on the conversion levels achieved in chemical reactions, they provide the basis for the development of mathematical models that can be used to provide approximate limits within which one can expect actual isothermal reactors to perform. In the development of these models we will define a segregated system as one in which the first effect is entirely responsible for the spread in residence times. When the distribution of residence times is established by the second effect, we will refer to the system as mixed. In practice one encounters various combinations of these two limiting effects. 

Considering theoretically a copolymerization on the surface of a miniemulsion droplet, one should necessarily be aware of the fact that this process proceeds in the heterophase reaction system characterized by several spatial and time scales. Among the first ones are sizes of an individual block and macromolecules of the multiblock copolymer, the radius of a droplet of the miniemulsion and the reactor size. Taking into account the pronounced distinction in these scales, it is convenient examining the macrokinetics of interphase copolymerization to resort to the system approach, generally employed for the mathematical modeling of chemical reactions in heterophase systems [73]. 

Dr. Walas has several decades of varied experience in industry and academia and is an active industrial consultant for the process design of chemical reactors and chemical and petroleum plants. He has written four related books on reaction kinetics, phase equilibria, process equipment selection and design, and mathematical modeling of chemical engineering processes, as well as the sections Reaction Kinetics and Chemical Reactors in the seventh edition of Chemical Engineers Handbook. He is a Fellow of the AlChE and a registered professional engineer. 

This exponential temperature dependenee represents one of the most severe non-linearities in chemical engineering systems. Keep in mind that the apparent temperature dependence of a reaction may not be exponential if the reaction is mass-transfer limited, not chemical-rate limited. If both zones are eneountered in the operation of the reactor, the mathematical model must obviously include both reaction-rate and mass-transfer effeets. 

A chemical reactor is cooled by both jacket cooling water and condenser cooling water. A mathematical model of the system has yielded the following openloop transfer functions (time is in minutes) ... 

In Fig. 1, various elements involved with the development of detailed chemical kinetic mechanisms are illustrated. Generally, the objective of this effort is to predict macroscopic phenomena, e.g., species concentration profiles and heat release in a chemical reactor, from the knowledge of fundamental chemical and physical parameters, together with a mathematical model of the process. Some of the fundamental chemical parameters of interest are the thermochemistry of species, i.e., standard state heats of formation (A//f(To)), and absolute entropies (S(Tq)), and temperature-dependent specific heats (Cp(7)), and the rate parameter constants A, n, and E, for the associated elementary reactions (see Eq. (1)). As noted above, evaluated compilations exist for the determination of these parameters. Fundamental physical parameters of interest may be the Lennard-Jones parameters (e/ic, c), dipole moments (fi), polarizabilities (a), and rotational relaxation numbers (z ,) that are necessary for the calculation of transport parameters such as the viscosity (fx) and the thermal conductivity (k) of the mixture and species diffusion coefficients (Dij). These data, together with their associated uncertainties, are then used in modeling the macroscopic behavior of the chemically reacting system. The model is then subjected to sensitivity analysis to identify its elements that are most important in influencing predictions. 

In practice, of course, it is rare that the catalytic reactor employed for a particular process operates isothermally. More often than not, heat is generated by exothermic reactions (or absorbed by endothermic reactions) within the reactor. Consequently, it is necessary to consider what effect non-isothermal conditions have on catalytic selectivity. The influence which the simultaneous transfer of heat and mass has on the selectivity of catalytic reactions can be assessed from a mathematical model in which diffusion and chemical reactions of each component within the porous catalyst are represented by differential equations and in which heat released or absorbed by reaction is described by a heat balance equation. The boundary conditions ascribed to the problem depend on whether interparticle heat and mass transfer are considered important. To illustrate how the model is constructed, the case of two concurrent first-order reactions is considered. As pointed out in the last section, if conditions were isothermal, selectivity would not be affected by any change in diffusivity within the catalyst pellet. However, non-isothermal conditions do affect selectivity even when both competing reactions are of the same kinetic order. The conservation equations for each component are described by... 

We will attempt to keep the mathematical details as brief as possible so that we will not lose sight of the principles of the design and operation of chemical reactors. The student will certainly see more applied mathematics here than in any other undergraduate course except Process Control. However, we wiU try to indicate clearly where we are going so students can see that the mathematical models developed here are essential for describing the application at hand. 

In 1976 he was appointed to Associate Professor for Technical Chemistry at the University Hannover. His research group experimentally investigated the interrelation of adsorption, transfer processes and chemical reaction in bubble columns by means of various model reactions a) the formation of tertiary-butanol from isobutene in the presence of sulphuric acid as a catalyst b) the absorption and interphase mass transfer of CO2 in the presence and absence of the enzyme carboanhydrase c) chlorination of toluene d) Fischer-Tropsch synthesis. Based on these data, the processes were mathematically modelled Fluid dynamic properties in Fischer-Tropsch Slurry Reactors were evaluated and mass transfer limitation of the process was proved. In addition, the solubiHties of oxygen and CO2 in various aqueous solutions and those of chlorine in benzene and toluene were determined. Within the framework of development of a process for reconditioning of nuclear fuel wastes the kinetics of the denitration of efQuents with formic acid was investigated. 

Edward G. Jefferson, Future Opportunities in Chemical Engineering Eli Ruckenstein, Analysis of Transport Phenomena Using Scaling and Physical Models Rohit Khanna and John H. Seinfeld, Mathematical Modeling of Packed Bed Reactors Numerical Solutions and Control Model Development... 

Possibly the chemical industry does not have as much need for mathematical models in process simulation as does the petroleum refining industry. The operating conditions for most chemical plants do not seem subject to as broad a choice, nor do they seem to require frequent reappraisals. However, this is a matter which must be settled on the basis of individual circumstances. Sometimes the initial selection of operating conditions for a new plant is sufficiently complex to justify development of a mathematical model. Gee, Linton, Maire, and Raines describe a situation of this sort in which a mathematical model was developed for an industrial reactor (Gl). Beutler describes the subsequent programming of this model on the large-scale MIT Whirlwind computer (B6). These two papers seem to be the most complete technical account of model development available. However, the model should not necessarily be thought typical since it relies more on theory, and less on empiricisms, than do many other process models. 

M.E. Coltrin, RJ. Kee, and G. H. Evans. A Mathematical Model of the Fluid Mechanics and Gas-Phase Chemistry in a Rotating Disk Chemical Vapor Deposition Reactor. J. Electrochem. Soc., 136(3) 819-829,1989. 

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