Plant scale reactor

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. 

These two correlations were based on laboratory-scale and pilot-plant-scale reactors (D < 1 m), and do not take into account vessel and impeller geometry. 

The surface to volume ratio of a laboratory-scale reactor is many times greater than that of a plant-scale reactor. This has two effects -... 

Morris, J.B., Diffey, H.R., Nicholls, B. Rumary, C.H. (1962) The removal of low concentrations of iodine from air on a plant scale. Reactor Science Technology, 16, 437-45. 

Now suppose that we design a plant-scale reactor that is 1000 times larger in volume (19 m3). The feed flowrate is 1000 times larger (3.506 kg/s), and the required heat transfer is 1000 times larger (817 kW). 

In applying the transition stability coefficient for scaling up, the designer requires systematic process steps in a pilot plant scale reactor with a satisfactory performance stability over a temperature interval of interest at stable component concentrations and other variables that may be used to control the process. 

Assume that the scaled-up reactor has the same bottom head type (e.g., ASME standard F D) as the pilot plant scale reactor. Equation 13-25 then becomes... 

A fixed bed pilot plant scale reactor was loaded with a precipitated iron catalyst synthesized by literature methods [7]. Synthesis gas was passed through the catalyst bed using typical industrial conditions [2,8] for various periods of time. The catalyst twenty or more fractions) were then unloaded and stored under nitrogen immediately before further use. 

The choice of experimental reactor is important to the success of the kinetic modeling effort. The short bench-scale reaction tubes sometimes used for studies of this sort give little or no insight into best mathematical form of the kinetic model, conduct the reaction over varying temperatures and partial pressures along the tube, and inevitably operate at velocities that are a small fraction of those to be encountered in the plant-scale reactor. Rate models from laboratory reactors of this sort rarely scale-up well. The laboratory differential reactor suffers from velocity problems but does at least conduct the reaction at known and relatively constant temperature and partial pressures. However, one usually runs into accuracy problems because calculated reaction rates are based upon the small observed differences in concentration between the reactor inlet and outlet. 

As mentioned earlier, both chemical (catalyst, surfactants, stabilizers) and physical (fluid dynamics, energy dissipation rates, circulation time and so on) factors control the performance of the suspension polymerization reactor. It is first necessary to examine the available experimental data to clearly understand the role of these chemical and physical factors. The available data indicates that the yield of usable polymer beads in laboratory scale reactor is more than 85%. Laboratory experiments were then planned to examine the sensitivity of the yield to various parameters of the polymerization recipe under the same hydrodynamic conditions. These experiments showed that the yield is relatively insensitive to small deviations in the chemical recipe. Analysis of the available data on pilot and plant scale indicated a progressive decrease in the yield of usable polymer beads from laboratory to pilot to plant scale. This analysis and some indirect evidence suggested that it may be possible to re-design the plant-scale reactor hardware to generate better fluid dynamics and mixing to increase the yield of particles in the desired size range. 

When reactions are fast relative to the mixing rate, not only are the apparent reaction rates affected but the whole time and temperature history of the reaction mechanism is also affected, yielding different selec-tivities and yields, depending on the intensity of the mixing. This often leads to a scale-up/scale-down problem, where yields of the desirable products in a plant-scale reactor are not as good as those in a small-scale reactor in the laboratory or the pilot plant. If the yield drops from the pilot-scale to the plant-scale reactor when all other important variables (temperature, pressure, and composition) have been held constant, then there is a mixing problem. Fast... 

Lu to r). One should thus proceed cautiously when applying (5.251) in CFD calculations of plant-scale reactors. 

Based on the examples presented, it is clear that intensity function representation of residence tiaie variability is a valuable tool for understanding and discerning fluid mixing characteristics. Effective utilization of this tool requires good experimental technique. Determination of residence time characteristics and use of intensity function representation/ interpretation should be a critical step in the sequential development of exploratory, pilot unit and plant scale reactors. 

The parameters of the system must be evaluated and the appropriate values must be used in tiie model. Some parameters can be obtained independently of the mathematical model. They may be of a basic character, like tiie gravitation constant, or it may be possible to determine them 1 independent measurements, Uke, for instance, solubility data fi om solubility experiments. However, it is usually not possible to evaluate all the parameters from specific experiments, and many of them have to be estimated by taking results from the whole (or a similar system), and tiien using parameter-fitting techniques to determine which set of parameter values makes the model best fit the experimental results. For example, a complex reaction may involve ten or more kinetic constants. These constants can be estimated 1 fitting a model to resnlts from a laboratory reactor. Once the parameter values have been determined, they can be incorporated into a model of a plant-scale reactor. 

Several scale-up systems are known today, and some have already been described in dedicated reviews or books on the topic.In the following discussion, only the systems never described elsewhere will be considered, and the first European experimental pilot plant scale reactors at a semi-industrial scale from SAIREM Manufacturer will also be described. ... 

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