Scaleup model-based

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. 

The temperature counterpart of Q>aVR ccj-F/R and if ccj-F/R is low enough, then the reactor will be adiabatic. Since aj 3>a, the situation of an adiabatic, laminar flow reactor is rare. Should it occur, then T i, will be the same in the small and large reactors, and blind scaleup is possible. More commonly, ari/R wiU be so large that radial diffusion of heat will be significant in the small reactor. The extent of radial diffusion will lessen upon scaleup, leading to the possibility of thermal runaway. If model-based scaleup predicts a reasonable outcome, go for it. Otherwise, consider scaling in series or parallel. 

Two aspects of scaleup frequently arise. One is building a model based on pilot plant studies that develop an understanding of the process variables for an existing full-scale mixing installation. The other is taking a new process and studying it in the pilot plant in such a way that pertinent scaleup variables are worked out for a new mixing installation. 

Correlations of nucleation rates with crystallizer variables have been developed for a variety of systems. Although the correlations are empirical, a mechanistic hypothesis regarding nucleation can be helpful in selecting operating variables for inclusion in the model. Two examples are (/) the effect of slurry circulation rate on nucleation has been used to develop a correlation for nucleation rate based on the tip speed of the impeller (16) and (2) the scaleup of nucleation kinetics for sodium chloride crystalliza tion provided an analysis of the role of mixing and mixer characteristics in contact nucleation (17). Pubhshed kinetic correlations have been reviewed through about 1979 (18). In a later section on population balances, simple power-law expressions are used to correlate nucleation rate data and describe the effect of nucleation on crystal size distribution. 

This section has based scaleups on pressure drops and temperature driving forces. Any consideration of mixing, and particularly the closeness of approach to piston flow, has been ignored. Scaleup factors for the extent of mixing in a tubular reactor are discussed in Chapters 8 and 9. If the flow is turbulent and if the Reynolds number increases upon scaleup (as is normal), and if the length-to-diameter ratio does not decrease upon scaleup, then the reactor will approach piston flow more closely upon scaleup. Substantiation for this statement can be found by applying the axial dispersion model discussed in Section 9.3. All the scaleups discussed in Examples 5.10-5.13 should be reasonable from a mixing viewpoint since the scaled-up reactors will approach piston flow more closely. 

Previous chapters have discussed how isothermal or adiabatic reactors can be scaled up. Nonisothermal reactors are more difficult. They can be scaled by maintaining the same tube diameter or by the modeling approach. The challenge is to increase tube diameter upon scaleup. This is rarely possible and when it is possible, scaleup must be based on the modeling approach. If the predictions are satisfactory, and if you have confidence in the model, proceed with scaleup. 

The sizing of pipelines for non-Newtonian liquids may be based on scaleup of tests made under the conditions at which the proposed line is to operate, without prior determination and correlation of rheological properties. A body of theory and some correlations are available for design with four mathematical models ... 

Dimensional similitude is based on principles of similarity and uses dimensionless ratios of the physical and chemical parameters that govern the working model to design the scaled-up prototype. This method is followed when the prototypical unit is expected to be dimensionally similar to the small-scale unit. The degree of success in using dimensional similitude as an approach to scaleup depends mainly on the extent to which the physical parameters necessary to achieve a desired result influence the process. 

However, each set of factors entering in to the rate expression is also a potential source of scaleup error. For this, and other reasons, a fundamental requirement when scaling a process is that the model and prototype be similar to each other with respect to reactor type and design. For example, a cleaning process model of a continuous-stirred tank reactor (CSTR) cannot be scaled to a prototype with a tubular reactor design. Process conditions such as fluid flow and heat and mass transfer are totally different for the two types of reactors. However, results from rate-of-reaction experiments using a batch reactor can be used to design either a CSTR or a tubular reactor based solely on a function of conversion, -r ... 

Experimental eqnilibrinm data are almost always essential. While theoretical models are available to predict snch data, and are very helpfnl in preliminary design, pilot studies and scaleup should be based on confirmed experimental measmements. Many nomographs providing data are available. Wisniak and Tamir [23-26], Sorensen and Arlt [27-29], and Macedo and Rasmussen... 

In formulating and using nonideal reactor models one should keep in mind our overall objective which is to build and operate an ideal reactor. Only ideal reactors are scaleable and their performance predictable. The nonideal flow models and experimental RTD curves are needed to assess deviations from ideality. When these deviations are small, a successful one-or-two parameter model can be constructed to interpret them. When deviations are large, one should concentrate on finding ways to diminish them rather than to interpret them. Multiparameter models are difficult to use and have very little value in scaleup. The exceptions are situations when the variations of some model parameters can be predicted independently based on first principles or based on accumulated experimental evidence. 

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