Optimization of Reactor Conversion

Once the structure of the recycle and separation has been established, some important degrees of freedom can be optimized that can have a very significant effect on the overall process economics. Start by considering the optimization of reactor conversion. 

If the reactor conversion is changed so as to optimize its value, then not only is the reactor affected in size and performance but also the separation system, since it now has a different separation task. The size of the recycle will also change. If the recycle requires a compressor, then the capital and operating costs of the recycle compressor will change. In addition, the heating and cooling duties associated with the reactor and the separation and recycle system change. 

As the reactor conversion increases, the reactor volume increases and hence reactor capital cost increases. At the same time, the amount of unconverted feed needing to be separated decreases and hence the cost of recycling unconverted feed decreases. 

Consider the example of a process that involves the multiple reactions in Equation 13.3. Because there is a mixture of FEED, PRODUCT and BYPRODUCT in the reactor effluent, an additional separator is required. The economic trade-offs now become more complex and a new cost must be added to the trade-offs. This is a raw materials efficiency cost due to byproduct formation. If the PRODUCT formation is kept constant, despite varying levels of BYPRODUCT formation, then the cost can be defined to be1112  

The value of PRODUCT formation and the raw materials cost of FEED that reacts to PRODUCT are constant. Alternatively, if the byproduct has no value, the cost of disposal should be included as  

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Optimization of Reactor Conversion for Muitiple Reactions Producing Byproducts... 

The overall inventory. In the preceding chapter, the optimization of reactor conversion was considered. As the conversion increased, the size (and cost) of the reactor increased, but that of separation, recycle, and heat exchanger network systems decreased. The same also tends to occur with the inventory of material in these systems. The inventory in the reactor increases with increasing conversion, but the inventory in the other systems decreases. Thus, in some processes, it is possible to optimize for minimum overall inventory. In the same way as reactor conversion can be varied to minimize the overall inventory, the recycle inert concentration also can be varied. 

Obviously, the use of purges is not restricted to dealing with impurities. Purges can also be used to deal with byproducts. As with the optimization of reactor conversion, changes in the recycle concentration of impurity might change the most appropriate separation sequence. 

Figure 13.23 Optimization of reactor conversion and recycle impurity concentration using a univariate search.

In Fig. 8.3, the only cost forcing the optimal conversion hack from high values is that of the reactor. Hence, for such simple reaction systems, a high optimal conversion would he expected. This was the reason in Chap. 2 that an initial value of reactor conversion of 0.95 was chosen for simple reaction systems. 

Figure 11.14 Summary of various factors which optimize maximum reactor conversions [Mohan and Govind 1988a]...

Optimization of reactor performance based on the following criteria substrate conversion, selectivity, space-time yield, enzyme and coenzyme consumption. 

The use of process simulation, in conjunction with optimization, as discussed in Chapter 18, allows one to determine optimal values of reactor conversion, entering temperature, mode of operation, pressure, molar ratio of reactants in a combined reactor feed, diluent ratio, and purge-to-recycle ratio. 

By a plot of 1/ Ta versus Xa, the reaction time needed to reach a certain conversion is easily determined by graphical integration (area below the curve) as schematically depicted in Figure 4.10.14. Of course, for an optimization of reactor design, it is always helpful to know the exact rate expression, but for a brief estimation the method outlined by Figure 4.10.14 is helpful. 

Next, we study the effects of reactor conversion, which is expressed in terms of the %Xeq to the side reactor configuration design. Figure 16.32a shows that %Xgq = 95 is the optimal conversion. Again, we look at the reactor cost and the separator cost, which are provided in Figure 16.321 . As %Xeq increases, the reactor cost increases mono-tonically because of a larger reactor volume and more catalyst needed for the additional reaction. At the same time, the separator cost decreases because of less reaction in the column base. 

It should be emphasized that these recommendations for the initial settings of the reactor conversion will almost certainly change at a later stage, since reactor conversion is an extremely important optimization variable. When dealing with multiple reactions, selectivity is maximized for the chosen conversion. Thus a reactor type, temperature, pressure, and catalyst are chosen to this end. Figure 2.10 summarizes the basic decisions which must be made to maximize selectivity. ... 

Figure 4.9 shows a plot of Eq. (4.12). As the purge fraction a is increased, the flow rate of purge increases, but the concentration of methane in the purge and recycle decreases. This variation (along with reactor conversion) is an important degree of freedom in the optimization of reaction and separation systems, as we shall see later. 

The problem with this approach is obvious. It involves a considerable amount of work to generate a measure of the quality of the sequence, the total vapor load, which is only a guideline. There are many other factors to be considered. Indeed, as we shall see later, when variables such as reactor conversion are optimized, the sequence might well need readdressing. 

Plots of economic potential versus reactor conversion allow the optimal reactor conversion for a given flowsheet to be identified (Fig. 8.2). Although this approach allows the location of the optimum to be found, it does not give any indication of why the optimum occurs where it does. 

Now there are two global variables in the optimization. These are reactor conversion (as before) but now also the concentration of IMPURITY in the recycle. For each setting of the IMPURITY concentration in the recycle, a set of tradeoffs can be produced analogous to those shown in Figs. 8.3 and 8.4. 

Figure 8.6 shows the component costs combined to give a total cost which varies with both reactor conversion and recycle inert concentration. Each setting of the recycle inert concentration shows a cost profile with an optimal reactor conversion. As the recycle inert concentration is increased, the total cost initially decreases but then... 

Interactions between the reactor and the rest of the process are extremely important. Reactor conversion is the most significant optimization variable because it tends to influence most operations through the process. 

Economic tradeoffs. Interactions between the reactor and the rest of the process are extremely important. Reactor conversion is the most significant optimization variable because it tends to influence most operations through the process. Also, when inerts are present in the recycle, the concentration of inerts is another important optimization variable, again influencing operations throughout the process. ... 

Preliminary process optimization. Dominant process variables such as reactor conversion can have a major influence on the design. Preliminary optimization of these dominant variables is often required. 

The models presented correctly predict blend time and reaction product distribution. The reaction model correctly predicts the effects of scale, impeller speed, and feed location. This shows that such models can provide valuable tools for designing chemical reactors. Process problems may be avoided by using CFM early in the design stage. When designing an industrial chemical reactor it is recommended that the values of the model constants are determined on a laboratory scale. The reaction model constants can then be used to optimize the product conversion on the production scale varying agitator speed and feed position. 

In the Monsanto/Lummus Crest process (Figure 10-3), fresh ethylbenzene with recycled unconverted ethylbenzene are mixed with superheated steam. The steam acts as a heating medium and as a diluent. The endothermic reaction is carried out in multiple radial bed reactors filled with proprietary catalysts. Radial beds minimize pressure drops across the reactor. A simulation and optimization of styrene plant based on the Lummus Monsanto process has been done by Sundaram et al. Yields could be predicted, and with the help of an optimizer, the best operating conditions can be found. Figure 10-4 shows the effect of steam-to-EB ratio, temperature, and pressure on the equilibrium conversion of ethylbenzene. Alternative routes for producing styrene have been sought. One approach is to dimerize butadiene to 4-vinyl-1-cyclohexene, followed by catalytic dehydrogenation to styrene ... 

Optimization of a Batch Polymerization Reactor at the Final Stage of Conversion... 

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