Selective reactor modeling

The selectivity is 100% in this simple example, but do not believe it. Many things happen at 625°C, and the actual effluent contains substantial amounts of carbon dioxide, benzene, toluene, methane, and ethylene in addition to styrene, ethylbenzene, and hydrogen. It contains small but troublesome amounts of diethyl benzene, divinyl benzene, and phenyl acetylene. The actual selectivity is about 90%. A good kinetic model would account for aU the important by-products and would even reflect the age of the catalyst. A good reactor model would, at a minimum, include the temperature change due to reaction. 

Steady performance data from the second reactor are shown in Figure 11.10, where the pressure drop did not rise exponentially and the conversion and selectivity remained at 75 and 83%, respectively. The reactor was further analyzed after operation, shown in Figure 11.11, to confirm the lack of carbon deposition. Reactor models were pivotal to developing a robust design for this high-temperature and... 

Having set up a model to describe the dynamics of the system, a very important first step is to compare the numerical solution of the model with any experimental results or observations. In the first stages, this comparison might be simply a check on the qualitative behaviour of a reactor model as compared to experiment. Such questions might be answered as Does the model confirm the experimentally found observations that product selectivity increases with temperature and that increasing flow rate decreases the reaction conversion ... 

In this chapter, we develop some guidelines regarding choice of reactor and operating conditions for reaction networks of the types introduced in Chapter 5. These involve features of reversible, parallel, and series reactions. We first consider these features separately in turn, and then in some combinations. The necessary aspects of reaction kinetics for these systems are developed in Chapter 5, together with stoichiometric analysis and variables, such as yield and fractional yield or selectivity, describing product distribution. We continue to consider only ideal reactor models and homogeneous or pseudohomogeneous systems. 

The increase in efficiency between the first- and second-generation reactors was attributed to less water in the feed and lower operating temperatures. Reactor models indicated that the major source of heat loss was by thermal conduction. The selective methanation reactor lowered the carbon monoxide levels to below 100 ppm, but at the cost of some efficiency. The lower efficiency was attributed to slightly higher operating temperatures and to hydrogen consumption by the methanation process. Typical methane levels in the product stream were 5-6.2%. ... 

In the reactor model, the reaction rates were assumed constant in the axial direction at any position x. Hence, the height of the packed bed is only related to the conversation per pass through the cell. On the other hand, the thickness of the packed bed influences the local reaction rate as well as the selectivity, and becomes a key parameter for the PBER design. To achieve a high selectivity and a reasonable reaction rate, the suitable thickness can be obtained by using computer iteration. The predictions of the model are in reasonable agreement with the experiment results.18... 

The C,-fraction of naphtha crackers is used as a feedstock in the Mitsubishi fluid bed process for the production of maleic anhydride. This process was commercialized in 1970. Many data related to this process including the catalyst screening, laboratory experiments, pilot plant design, reactor behaviour and the development of higher selectivity catalysts may be found in the patent literature (12-17).The patents thus give a nearly complete picture of the scale-up process. The data have been used in the present investigation to test the fluid bed reactor model. 

By assuming a reactor model, it is possible to determine reaction rates from experimental results. Then, various factors affecting yields, selectivities and reaction rates become evident. Experimental rate laws are deduced from results, e.g. in the classical form involving reaction orders and activation energies. At this stage, computers are used for solving numerically the mathematical models of reaction and reactor (Sect. 4) and for making a statistical analysis of experimental results (Sect. 5). 

In this section, we present examples to illustrate the usefulness of multi-mode homogeneous reactor models in predicting micromixing effects on yield and selectivity, reactor runaway, etc. 

First, select a reactor arrangement and catalyst configuration. The next step is to select a reactor model for calculating the reaction volume. An exact model of reactor performance must include mass transfer of reactants from the fluid to the catalyst sites within the pellet, chemical reaction, and then mass transfer of products back into the fluid. Table 7.13 lists the steps, and Figure 7.5 illustrates the processes involved. Here, only simple models are of interest to estimate the reaction volvune for a preliminary design. The reaction volume is that volume occupied by the catalyst pellets and the space between them. We must provide additional volume for internals to promote uniform flow and for entrance and exit sections. The total volume is called the reactor volume. After calculating the reactor volume, the next step is to determine the reactor length and diameter. 

I have made an attempt to provide sufficient information to understand and to define the specific role of computational flow modeling in reactor engineering applications. Discussions on the main features of reactor engineering, computational flow modeling and their interrelationship will help to select appropriate models, and to apply these computational models to link reactor hardware to reactor performance. Mathematical modeling of flow processes (including turbulent flows, multiphase flows and reactive flows) and corresponding numerical methods to solve these model... 

In these balance equations all terms should be described at the same level of accuracy. It certainly does not pay to have the finest description of one term in the balance equations if the others can only be very crudely described. Current demands for increased selectivity and volumetric productivity require more precise reactor models, and also force reactor operation to churn turbulent flow which to a great extent is uncharted territory. An improvement in accuracy and a more detailed description of the molecular scale events describing the rate of generation terms in the heat- and mass balance equations has... 

The selected mathematical model is represented by a discretization method for approximating the differential equations by a system of algebraic equations for the variables at some set of discrete locations in space and time. Many different approaches are used in reactor engineering , but the most important of them are the simple finite difference methods (FDMs), the flrrx conservative finite volume methods (FVMs), and the accurate high order weighted residual methods (MWRs). 

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