Micromixing reactors

In addition to these two macromixing reactor models, in this chapter, we also consider two micromixing reactor models for evaluating the performance of a reactor the segregated flow model (SFM), introduced in Chapters 13 to 16, and the maximum-mixedness model (MMM). These latter two models also require knowledge of the kinetics and of the global or macromixing behavior, as reflected in the RTD. 

Process development was carried out at laboratory scale using a slit-type interdigital micromixer-reactor [31]. The whole flow guidance was provided by the micromixer and the outcoming solution was collected in a vessel. 

Fig. 6.29 High-p.T operation for the radicai side-chain bromination of m-nitrotoiuene (NT) in a micromixer-reactor setup. The iarge increase in operationai temperature increases conversion at good selectivities, which tend to deciine siightiy with temperature.

The experimental results agree with the kinetic models and the hypothesis of a well micromixed reactor. They are also in good agreement with previous studies conducted in batch reactors (14, 15). Thus one obtains similar values for the main fundamental kinetic parameters, and these are compatible with those already published in the literature (see Table I). The values of the kinetic parameters shown on Table I (this work) were determined by an optimization procedure which indicates the precision of the estimation. However this work shows that the parameters obtained for self-inhibition (pyrolysis of pure neopentane) and inhibition (pyrolysis of a mixture of neopentane and isobutene) at no extent of reaction) are markedly different. We believe that these differences can be explained by the presence, in the reaction mixture, of impurities (essentially ethylenic products). These... 

Flow models presented in Sec. 2.4 may be used for predicting chemical conversion. Elementary patterns involved in the model are generally assumed to behave as ideal well micromixed reactors. Mixing earliness is implicitly accounted for by the arrangement of these elementary zones with respect to each other and the internal streams connecting them. This method is very popular and is successful for representing and scaling up chemical reactors provided the model has a sound physical basis. 

The above kinetic model allows the calculation of monomer conversion, polymer molecular weight, and branching frequency. The effects of polymerization conditions and the reactor types (e.g., batch, continuous segregated, and continuous micromixed reactors) have been investigated using the above kinetic model [71]. 

Guichardon etal. (1994) studied the energy dissipation in liquid-solid suspensions and did not observe any effect of the particles on micromixing for solids concentrations up to 5 per cent. Precipitation experiments in research are often carried out at solids concentrations in the range from 0.1 to 5 per cent. Therefore, the stirred tank can then be modelled as a single-phase isothermal system, i.e. only the hydrodynamics of the reactor are simulated. At higher slurry densities, however, the interaction of the solids with the flow must be taken into account. 

As the flow of a reacting fluid through a reactor is a very complex process, idealized chemical engineering models are useful in simplifying the interaction of the flow pattern with the chemical reaction. These interactions take place on different scales, ranging from the macroscopic scale (macromixing) to the microscopic scale (micromixing). 

In what follows, both macromixing and micromixing models will be introduced and a compartmental mixing model, the segregated feed model (SFM), will be discussed in detail. It will be used in Chapter 8 to model the influence of the hydrodynamics on a meso- and microscale on continuous and semibatch precipitation where using CFD, diffusive and convective mixing parameters in the reactor are determined. 

In the SFM the reactor is divided into three zones two feed zones fj and (2 and the bulk b (Figure 8.1). The feed zones exchange mass with each other and with the bulk as depicted with the flow rates mi 2, i,3 and 2,3 respectively, according to the time constants characteristic for micromixing and mesomix-ing. As imperfect mixing leads to gradients of the concentrations in the reactor, different supersaturation levels in different compartments govern the precipitation rates, especially the rapid nucleation process. 

Using the SFM, the influence of micromixing and mesomixing on the precipitation process and properties of the precipitate can be investigated. Mass and population balances can be applied to the individual compartments and to the overall reactor accounting for different levels of supersaturation in different zones of the reactor. 

In order to account for both micromixing and mesomixing effects, a mixing model for precipitation based on the SFM has been developed and applied to continuous and semibatch precipitation. Establishing a network of ideally macromixed reactors if macromixing plays a dominant role can extend the model. The methodology of how to scale up a precipitation process is depicted in Figure 8.8. 

Each stage of particle formation is controlled variously by the type of reactor, i.e. gas-liquid contacting apparatus. Gas-liquid mass transfer phenomena determine the level of solute supersaturation and its spatial distribution in the liquid phase the counterpart role in liquid-liquid reaction systems may be played by micromixing phenomena. The agglomeration and subsequent ageing processes are likely to be affected by the flow dynamics such as motion of the suspension of solids and the fluid shear stress distribution. Thus, the choice of reactor is of substantial importance for the tailoring of product quality as well as for production efficiency. 

The reactor has been successfully used in the case of forced precipitation of copper and calcium oxalates (Jongen etal., 1996 Vacassy etal., 1998 Donnet etal., 1999), calcium carbonate (Vacassy etal., 1998) and mixed yttrium-barium oxalates (Jongen etal., 1999). This process is also well adapted for studying the effects of the mixing conditions on the chemical selectivity in precipitation (Donnet etal., 2000). When using forced precipitation, the mixing step is of key importance (Schenk etal., 2001), since it affects the initial supersaturation level and hence the nucleation kinetics. A typical micromixer is shown in Figure 8.35. 

Baldyga, J. and Poherecki, R., 1995. Turbulent micromixing in chemical reactors - a review. Chemical Engineering Journal, 58, 183-195. 

Bourne, J.R. and Yu, S., 1994. Investigation of micromixing in stirred tank reactors using parallel reactions. Industrial and Engineering Chemistry Research, 33, 41-55. 

Guichardon, P., Falk, L., Fournier, M.C. and Villermaux, J., 1994. Study of micromixing in a liquid-solid suspension in a stirred reactor. American Institute of Chemical Engineers Symposium Series, 299, 123-130. 

Harada, M., Arima, K., Eguchi, W. and Nagata, S., 1962. Micromixing in a continuous flow reactor. Memoir of the Faculty of Engineering, Kyoto University, Japan, 24, 431. 

Kim, W.-S. and Tarbell, J.M., 1996. Micromixing effects on barium sulphate precipitation in an MSMPR reactor. Chemical Engineering Communications, 146, 33-56. 

Pohorecki, R. and Baldyga, J., 1988. The effects of micromixing and the manner of reactor feeding on precipitation in stirred tank reactors. Chemical Engineering Science, 43, 1949-1954. 

Rice, R.W. and Baud, R.E., 1990. The role of micromixing in the scale-up of geometrically similar batch reactors. American Institution of Chemical Engineers Journal, 36, 293-298. 

Ritchie, B.W. and Togby, A.H., 1979. A three-environment micromixing model for chemical reactors with arbitrary separate feed streams. Chemical Engineering Journal, 17, 173. 

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