Scaling reactor model

Finally we have the reactor models, which in addition take into account the macro flow effects in the reactor. In principle, even research models have to include these macro flow aspects as they act on the scale of the laboratory reactor, small though this may be. In the large scale reactor models, however, the effects of macro flow are usually more pronounced. 

One goal of catalyst designers is to constmct bench-scale reactors that allow determination of performance data truly indicative of performance in a full-scale commercial reactor. This has been accompHshed in a number of areas, but in general, larger pilot-scale reactors are preferred because they can be more fully instmmented and can provide better engineering data for ultimate scale-up. In reactor selection thought must be given to parameters such as space velocity, linear velocity, and the number of catalyst bodies per reactor diameter in order to properly model heat- and mass-transfer effects. 

Over 25 years ago the coking factor of the radiant coil was empirically correlated to operating conditions (48). It has been assumed that the mass transfer of coke precursors from the bulk of the gas to the walls was controlling the rate of deposition (39). Kinetic models (24,49,50) were developed based on the chemical reaction at the wall as a controlling step. Bench-scale data (51—53) appear to indicate that a chemical reaction controls. However, flow regimes of bench-scale reactors are so different from the commercial furnaces that scale-up of bench-scale results caimot be confidently appHed to commercial furnaces. For example. Figure 3 shows the coke deposited on a controlled cylindrical specimen in a continuous stirred tank reactor (CSTR) and the rate of coke deposition. The deposition rate decreases with time and attains a pseudo steady value. Though this is achieved in a matter of rninutes in bench-scale reactors, it takes a few days in a commercial furnace. 

The predictions checked in the pilot-plant reactor were reasonable. Later, when the production unit was improved and operators learned how to control the large-scale reactor, performance prediction was also very good. The highest recognition came from production personnel, who believed more in the model than in their instruments. When production performance did not agree with model predictions, they started to check their instruments, rather than questioning the model. 

Ross (R2) measured liquid-phase holdup and residence-time distribution by a tracer-pulse technique. Experiments were carried out for cocurrent flow in model columns of 2- and 4-in. diameter with air and water as fluid media, as well as in pilot-scale and industrial-scale reactors of 2-in. and 6.5-ft diameters used for the catalytic hydrogenation of petroleum fractions. The columns were packed with commercial cylindrical catalyst pellets of -in. diameter and length. The liquid holdup was from 40 to 50% of total bed volume for nominal liquid velocities from 8 to 200 ft/hr in the model reactors, from 26 to 32% of volume for nominal liquid velocities from 6 to 10.5 ft/hr in the pilot unit, and from 20 to 27 % for nominal liquid velocities from 27.9 to 68.6 ft/hr in the industrial unit. In that work, a few sets of results of residence-time distribution experiments are reported in graphical form, as tracer-response curves. 

Kolbel et al. (K16) examined the conversion of carbon monoxide and hydrogen to methane catalyzed by a nickel-magnesium oxide catalyst suspended in a paraffinic hydrocarbon, as well as the oxidation of carbon monoxide catalyzed by a manganese-cupric oxide catalyst suspended in a silicone oil. The results are interpreted in terms of the theoretical model referred to in Section IV,B, in which gas-liquid mass transfer and chemical reaction are assumed to be rate-determining process steps. Conversion data for technical and pilot-scale reactors are also presented. 

Chapter 3 introduced the basic concepts of scaleup for tubular reactors. The theory developed in this chapter allows scaleup of laminar flow reactors on a more substantive basis. Model-based scaleup supposes that the reactor is reasonably well understood at the pilot scale and that a model of the proposed plant-scale reactor predicts performance that is acceptable, although possibly worse than that achieved in the pilot reactor. So be it. If you trust the model, go for it. The alternative is blind scaleup, where the pilot reactor produces good product and where the scaleup is based on general principles and high hopes. There are situations where blind scaleup is the best choice based on business considerations but given your druthers, go for model-based scaleup. 

Suppose now that a pilot-plant or full-scale reactor has been built and operated. How can its performance be used to confirm the kinetic and transport models and to improve future designs Reactor analysis begins with an operating reactor and seeks to understand several interrelated aspects of actual performance kinetics, flow patterns, mixing, mass transfer, and heat transfer. This chapter is concerned with the analysis of flow and mixing processes and their interactions with kinetics. It uses residence time theory as the major tool for the analysis. 

Four elements of microchannel scale-up models will be described pressure-drop design, heat-transfer design, reactor design, and mechanical and manufacturing designs. 

As described above, microchannel reactor scale-up requires integrated models, which include the reaction chemistry with heat transfer, pressure drop, flow distribution, and manufacturing tolerances. The culmination of scale-up models is their successful demonstration. 

The deposition rate is not uniform over the grounded electrode. A common requirement for large-scale reactors is a 5% difference in deposition rate over a certain electrode area, preferably the complete electrode. Using this, the 2D model would predict a useful electrode area of about. 0%, i.e., an electrode radius of about 4 cm. 

Silveston et al. (1994) use a one-dimensional plug flow model to represent the packed bed in the final stage. Because the intent of their work was to model the experiments of Briggs et al. discussed earlier, they allowed for heat loss or gain in the bench scale reactor used by Briggs through wall... 

On the basis of different assumptions about the nature of the fluid and solid flow within each phase and between phases as well as about the extent of mixing within each phase, it is possible to develop many different mathematical models of the two phase type. Pyle (119), Rowe (120), and Grace (121) have critically reviewed models of these types. Treatment of these models is clearly beyond the scope of this text. In many cases insufficient data exist to provide critical tests of model validity. This situation is especially true of large scale reactors that are the systems of greatest interest from industry s point of view. The student should understand, however, that there is an ongoing effort to develop mathematical models of fluidized bed reactors that will be useful for design purposes. Our current... 

Another approach to scale-up is the use of simplified models with key parameters or lumped coefficients found by experiments in large beds. For example, May (1959) used a large scale cold reactor model during the scale-up of the fluid hydroforming process. When using the large cold models, one must be sure that the cold model properly simulates the hydrodynamics of the real process which operates at elevated pressure and temperature. 

An example of the application of a unified model to the design of a three-phase, fluidized bed bioreactor is the scale down, scale up procedure. A model of the full scale reactor is developed, then is used to design... 

The reactor model adopted for describing the lab-scale experimental setup is an isothermal homogeneous plug-flow model. It is composed of 2NP + 2 ordinary differential equations of the type of Equation 16.11 with the initial condition of Equation 16.12, NP + 3 algebraic equations of the type of Equation 16.13, and the catalytic sites balance (Equation 16.14) ... 

Only direct numerical simulation (DNS) resolves all scales (Moin and Mahesh 1998). However, DNS is com-putationally intractable for chemical reactor modeling. 

As the next step in multiphasic hydrogenation, the design and implementation of a continuously driven loop reactor as a laboratory-scale plant model led to comparable selectivity applying the same water soluble ruthenium-based catalyst system. 

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