Quantitative reactor model

The key to successful heterogeneous catalytic reactor design and operation is to have a quantitative reactor model capable of predicting the efliect of reactor scale and operating conditions on volumetric productivity and selectivity of the reactor. This does not imply that an ab initio model for each scale has to be merged into a detailed, complex model for the whole reactor. It... 

Another important effect that can be analyzed is the relation between the equilibrium reactor temperature and the equilibrium jacket temperature. It is known that temperature difference between cooling jacket and the reactor must be increased as the volume of the reactor increases. Figure 8 shows this effect clearly. When the reactor has a small volume the difference Tg — Tj is very small, consequently the heat transfer process is slower and the operation control is easier. Table 2 quantitatively summarizes the effects previously commented for a typical reactor modelled by Eq.(23) with the parameters defined in table 1. As the reactor volume varies from 0.0126 to 42.41 m , lower jacket temperatures are required and the operation control is more difficult. 

Quantitative Predictions. In this section we use the riser data to determine intrinsic activity and coking parameters (kj, Aj) in the model, and then predict MAT and FFB conversion and coke yields. Typically, we will have either the MAT or FFB activity and coke information and the task is to predict the riser performance. As the models use intrinsic parameters, it is immaterial which test information is available. The intrinsic parameters can be easily extracted by fitting the data to the appropriate reactor model. We will use the riser data as it gives the intrinsic information directly. The fitted rate parameters are summarized in Table III. The other model parameters, such as the activation energies, heats of reaction, the coke deactivation exponent, n, (also given in Table III), were estimated independently. The details of parameter estimates are described in the Appendix. 

Again, the simple isothermal 1-D plug-flow reactor model provides a good basis for quantitative descriptions. This model allows to explore the potential of using series connections of several membrane reactor segments. The corresponding mass balance for a component i and a segment k can be formulated as follows ... 

In general, a reaction kinetics following a LHHW model is suitable, but the identification of parameters remains demanding. For some catalysts power-law models may be appropriate, for others not. For example, reaction orders identical with stoichiometric coefficients were suitable for Pd/Al203 doped with different metals. On the contrary, for Pd/MgO reaction orders with respect to phenol ranging from -0.5 to 0.5 were observed [17]. However, the bibliographic search was not able to find a quantitative kinetic model for Pd-type catalysts suitable for reactor design. 

Although emulsion polymerization has been carried out for at least 50 years and has enormous economic importance, the detailed quantitative behavior of these reactors is still not well understood. For example, there are many more mechanisms and phenomena reported experimentally than have been incorporated in the existing theories. Considerations such as non-micellar particle formation, non-uniform particle morphologies, polymer chain end stabilization of latex particles, particle coalescence, etc. have been discussed qualitatively, but not quantitatively included in existing reactor models. 

The situation may become even more complicated if a second liquid phase is present it may either serve as the reaction space containing the catalyst while the product as well as a part of the reactants exist in the first liquid phase, or it may act as a solvent into which a desired intermediate is extracted from the reacting liquid phase (cf. Section 3.1.1.1). To describe quantitatively the course of a homogeneous catalytic reaction in a multiphase chemical reactor it is necessary to combine the following information in a suitable reactor model ... 

Rational reactor selection and design requires information on thermodynamics, chemical kinetics, heat and mass transport, and reactor hydrodynamics. In practice, a quantitative analysis is based on reactor models and engineering correlations. In this chapter we limit ourselves to a qualitative discussion, emphasizing principles rather than quantitative calculations. 

Chapter 9. The fundamental reactor modeling principles covered in Chapters 2-8 provide the framework in which we think about chemical reactors. We understand which phenomena cause which observed reactor behaviors, and which design variables should be changed if We wish to alter the reactor performance. But when we want to make quantitative predictions of reactor performance, we require values for the model parameters. It is a simple fact that most of the parameters needed for the chemistries and reactor configurations of interest are Uot available in the literature. To make these models useful in standard industrial practice, therefore, we must be able to conveniently determine or estimate these parameters from experimental data collected on the system of interest. Chapter 9 covers this important topic iof parameter estimation, which is not usually addressed in a systematic manner in introductory treatments of reactor analysis and design. 

The use of reactor models as a basis for design, associated with the ever-increasing possibilities of computers. This is an aspect that will be dealt with extensively further in this chapter. To place this aspect in the right perspective, earlier stages of design in which decisions are taken on the basis of sound judgment and semi-quantitative considerations will be discussed first. 

The qualitative analysis of intraparticle heat transport suggests severe limitations of packed-bed laboratory reactors compared with the thin-film catalyzed microchannel, as discussed previously. It is imperative that a quantitative study of reactor heat transfer limitations is performed. With PrOx as a model reaction, this study was realized through the non-isothermal reactor modeling of the microreactor and the packed-bed reactors with both 2 and 4 mm radii. In the model, the operating... 

Other studies also showed that tracing of the liquid phase can provide clear indications of stagnancy or bypassing by use of the intensity function. More quantitative analysis of the data requires a reactor model (34). 

It is always preferable to use models which have a stronger content of our knowledge about the physics and chemistry of the process. We normally do that by breaking up M into a kinetic model M that relates local reaction rates to local concentrations, temperature, pressure and catalyst conditions, and a reactor model that quantitatively describes the transport processes and heat and mass balances in the reactor. While in many cases we have the tools to derive an approximation for M. from first principles we can very seldom do that for M. Aside from a few cases of gaseous reactions, we cannot predict ML. For the reactor modeling we have to accept the fact that reaction rate expressions are empirical correlations. What the study of reaction mechanisms has contributed to us is some a priori information as to what form M might have. 

Computer simulation of the reactor kinetic hydrodynamic and transport characteristics reduces dependence on phenomenological representations and idealized models and provides visual representations of reactor performance. Modem quantitative representations of laminar and turbulent flows are combined with finite difference algorithms and other advanced mathematical methods to solve coupled nonlinear differential equations. The speed and reduced cost of computation, and the increased cost of laboratory experimentation, make the former increasingly usehil. 

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