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FIGURE 10.1 Types of stirred tank reactor. (a) Multiphase stirred reactor. 1: impeller, 2: baffles, 3: cooling coils, 4: gas sparger. (b) Stirred reactor with gas-inducing impeller (dead-end type). (c) Stirred reactor with helical ribbon impeller (used with or without a draft tube).

TABLE 10.1 Some Industrial Applications of Stirred Reactors

Phases handled

Applications

Liquid Alkylations, Sulfonations, Esterifications, Bulk and solution polymerizations

(styrene, acrilonitrile, ethylene, propylene) and so on Gas-liquid Oxidations (ethylene, paraffins), Chlorinations (acetic acid, dodecane),

Carbonylations (methanol, propanol), Esterifications, manufacture of sulfuric acid, adipic acid, oxamide and so on Gas-liquid- Hydrogenations (olefins, edible oils, several chloro and nitro aromatics), solid Oxidations (p-xylene), Fermentations (alcohol, single cell proteins, antibiotics), Waste water treatment and so on Liquid-liquid Suspension and emulsion polymerizations (styrene, vinyl-chloride),

Oximations, Extractions Liquid-solid Calcium hydroxide (from calcium oxide), Regeneration of ion-exchange resins, Anaerobic fermentations Gas-liquid- Bi-phasic hydroformylations, Carbonylations liquid

Gas-solid Stirred fluidized beds (poly-ethylene, poly-propylene)

are listed below. Computational flow modeling can make substantial contributions to each of these steps.

(1) Reaction engineering models for simulating reactor performance: Reaction engineering models are used to examine the 'sensitivity' of reactor performance to various fluid dynamics related issues such as residence time distributions, short-circuiting and by-pass. These models are also a useful aid to understanding issues related to reactor dynamics and start-up/shutdown operations. If the performance is found to be sensitive to fluid dynamics related issues, computational flow models can be used to obtain accurate information about the desired processes. Some examples of combining information from detailed flow models with reaction engineering models based on a mixing cell framework are discussed in Chapter 1. Such combined reaction engineering models are useful to interpret and extrapolate laboratory-and pilot-scale experiments. Detailed simulations of reaction engineering models at different values of transport parameters (mass transfer coefficient, heat transfer coefficient, mixing and so on) are carried out to identify operable windows and to evolve quantitative demands on reactor hardware.

(2) Resolving conflicting process requirements: For most industrial situations, a reactor has to carry out several functions simultaneously. It is quite common to find that the requirements of these different functions of the reactor may be quite different or sometimes may even conflict with each other. For example, the desired fluid dynamic characteristics for blending and heat transfer are quite different (they require more bulk flow and less shear) from those for gas-liquid dispersion and mass transfer (which require more shear). Such conflicting requirements make the task of evolving a 'wish list' for the desired fluid dynamics difficult. The reactor engineer has to achieve a compromise between conflicting process requirements to achieve the best results. Not much progress can be made without a good understanding of the underlying fluid dynamics of stirred reactors and its relation with design parameters on the one hand and with the processes of interest, on the other. Experimental investigations have contributed significantly to a better understanding of the complex hydrodynamics of stirred vessels in recent years. However, computational models offer unique advantages for understanding the conflicting requirements of different processes and their subsequent prioritization. Using a computation model, one can switch on and off various processes, which is not possible when carrying out experiments. Such numerical experiments can give useful insight into interactions between different processes and can help to resolve the challenges posed by conflicting requirements.

(3) Translating batch data for continuous reactors: In most cases, laboratory-and bench-scale experiments required to validate the reactor concept are carried out in batch mode. It is then necessary to translate (or to use) the data obtained in these experiments to design continuous reactors. The location of feed pipes, outlets and their influence on mixing and performance needs to be understood. Computational flow models can be of great help in this regard.

(4) Scale-down/scale-up analysis: It is essential to analyze the possible influence of the scale of the reactor on its fluid dynamics and performance. It should be noted that a small-scale reactor would invariably have higher shear and more rapid circulation than a large-scale reactor. Multiphase processes, therefore, are often dispersion controlled in small-scale reactors and are coalescence controlled in large-scale reactors. The interfacial area per unit volume of reactor normally reduces as the scale of the reactor increases. Scale-up/scale-down analysis is useful when planning laboratory and pilot plant tests. It may often be necessary to use a pilot reactor configuration which is not geometrically similar to the large-scale reactor in order to maintain similarity of the desired process. Conventionally, such an analysis is carried out based on certain empirical scaling rules and prior experience. Computational flow modeling can make substantial contributions to this step by providing quantitative information about the fluid dynamics.

(5) Testing new reactor concepts: More often than not, development of reactor technologies relies on prior experience. New reactor concepts are often sidelined due to lack of resources (experimental facilities, time, funding etc.) to test them. Experimental studies have obvious limitations regarding the extent of parameter space that can be studied and regarding extrapolation beyond the studied parameter space. A wide variety of impellers with different shapes are used in practice. Different practices relating to impeller clearance etc. are followed for different impellers and for different applications. Computational flow models, which allow a priori predictions of the flow generated in a stirred reactor of any configuration (impellers of any shape) with just a knowledge of geometry and operating parameters, can make valuable contributions to developing new reactor technologies.

This brief review of steps in the engineering of stirred reactors indicates that the availability of large degrees of freedom regarding reactor configuration and impellers can be effectively exploited to evolve better reactor technologies. This, however, requires detailed knowledge and understanding of the fluid dynamics of stirred reactors. For example, in a recent US patent, Roby (1997) claims development of an

improved reactor for the oxidation of p-xylene to manufacture terephthalic acid. The schematic diagram of the proposed reactor hardware is shown in Fig. 10.2. A brief analysis of the methods claimed to achieve better performance in stirred oxidation reactors, may help one to understand the role and demands on CFD-based flow modeling for engineering stirred reactors. The major contribution of the claimed invention is very high efficiency of oxygen utilization in a single pass. Oxygen is introduced in a draft tube. Gas-liquid mixture is pumped downward at high velocities inside the draft tube. Pumping leads to formation of a jet below the draft tube, which entrains fluid outside the draft tube and impacts the bottom of the reactor vessel, setting up roll cells in the process. These roll cells trap gas bubbles resulting in very high efficiency of oxygen use. The formation of these roll cells is intimately related to details of hardware configuration (design of downward pumping impeller, draft tube construction, jet velocity, clearance between draft tube and reactor bottom, shape of reactor bottom and so on) and operating conditions (impeller speed, gas flow rate and so on). A computational flow model can play a very useful role here in understanding the formation of roll cells and establishing a relationship between the roll cells and reactor hardware. Apart from the formation of roll cells, the inventor emphasized the relationship between reactor performance and fluid dynamics by insisting on the following:

• oxygen should be fed into the reactor at the point of highest shear;

• reactant hydrocarbon should be fed into the reactor at the point of highest turbulence.

Identifying the locations of zones of highest shear and turbulence and how these locations change with scale and configuration of the reactor can best be carried out with the help of a computational flow model. Such a computational model can also be used to evaluate the patented concept of gas containment baffles. The purpose of such a gas containment baffle is again to increase oxygen utilization efficiency and to minimize the oxygen break-through in the vapor space in the reactor. In fact, the computational flow model can be used to evolve new hardware configurations to achieve the desired process objectives provided it can a priori simulate the flow in stirred reactors. Thus, a computational flow model can be used as a powerful reactor-engineering tool, provided it meets the following requirements:

• it can be applied to impellers of any shape;

• it can account for interactions between multiple impellers/reactor internals;

• it can be extended to multiphase systems.

In the following sub-section, state of the art CFD modeling of stirred reactors is reviewed with reference to these requirements.

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