Reactors performance

Reactor performance can be measured by a number of parameters. For electrolytic cells two important parameters are current efficiency C.E. and space-time yield Ygj.. (Current efficiency or current yield has been defined in Section 1.4.2.) 

The space-time yield of a reactor is defined as the mass of product produced by unit reactor volume in unit time. For an electrochemical reactor, it is  

Other important performance indicators are energy consumption Ec (see Section 5.2.1.1), in KWh/kg, and Yc, the chemical yield, which can be defined as  

As a simple example to clarify the difference between C.E. and consider an aqueous acidic electrolyte containing nitrobenzene (A) which is being reduced according to Eq. (1.31) to phenylhydroxylamine (B% the latter undergoing chemical 

Chemical analysis shows that for every kmol of A reacted Ikmol of hydrogen is evolved, and that the mol ratio of C to D is 3 to 1. It follows from Eq. (1.31) that to produce 3 kmol of C will require 12g. To this we have to add 6g due to the kmol of by-product D. Finally the 4 kmol of hydrogen will according to Eq. (1.32) account for another 8g, making a total of 26g. Thus one requires 26/3 = 8.7g per kmol of C. Therefore  

Before exploring how reactor conditions can be chosen, some measure of reactor performance is required. 

For polymerization reactors, the main concern is the characteristics of the product that relate to the mechanical properties. The distribution of molar masses in the polymer product, orientation of groups along the chain, cross-linking of the polymer chains, copolymerization with a mixture of monomers, and so on, are the main considerations. Ultimately, the main concern is the mechanical properties of the polymer product. 

For biochemical reactions, the performance of the reactor will normally be dictated by laboratory results, because of the difficulty of predicting such reactions theoretically6. There are likely to be constraints on the reactor performance dictated by the biochemical processes. For example, in the manufacture of ethanol using microorganisms, as the concentration of ethanol rises, the microorganisms multiply more slowly until at a concentration of around 12% it becomes toxic to the microorganisms. 

For other types of reactors, three important parameters are used to describe their performance7  

The following example will help clarify the distinctions among these three parameters. 

Before we can explore how reactor conditions can be chosen, we require some measure of reactor performance. For polymerization reactors, the most important measure of performance is the distribution of molecular weights in the polymer product. The distribution of molecular weights dictates the mechanical properties of the polymer. For other types of reactors, three important parameters are used to describe their performance  

Example 2.3 Benzene is to be produced from toluene according to the reaction  

Some of the benzene formed undergoes a secondary reaction in series to an unwanted byproduct, diphenyl, according to the reaction 

Stoichiometric factor = stoichiometric moles of toluene required per mole of benzene produced 

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Since process design starts with the reactor, the first decisions are those which lead to the choice of reactor. These decisions are among the most important in the whole design. Good reactor performance is of paramount importance in determining the economic viability of the overall design and fundamentally important to the environmental impact of the process. In addition to the desired products, reactors produce unwanted byproducts. These unwanted byproducts create environmental problems. As we shall discuss later in Chap. 10, the best solution to environmental problems is not elaborate treatment methods but not to produce waste in the first place. 

Having made a choice of the reaction path, we need to choose a reactor type and make some assessment of the conditions in the reactor. This allows assessment of reactor performance for the chosen reaction path in order for the design to proceed. 

Because there are two feeds to this process, the reactor performance can be calculated with respect to both feeds. However, the principal concern is performance with respect to toluene, since it is much more expensive than hydrogen. 

In describing reactor performance, selectivity is usually a more meaningful parameter than reactor yield. Reactor yield is based on the reactant fed to the reactor rather than on that which is consumed. Clearly, part of the reactant fed might be material that has been recycled rather than fresh feed. Because of this, reactor yield takes no account of the ability to separate and recycle unconverted raw materials. Reactor yield is only a meaningful parameter when it is not possible for one reason or another to recycle unconverted raw material to the reactor inlet. By constrast, the yield of the overall process is an extremely important parameter when describing the performance of the overall plant, as will be discussed later. 

This might he worthwhile if the FEED-BYPRODUCT separation is expensive. To use a purge, the FEED and BYPRODUCT must be adjacent to each other in order of volatility (assuming distillation is used as the means of separation). Of course, care should be taken to ensure that the resulting increase in concentration of BYPRODUCT in the reactor does not have an adverse effect on reactor performance. Too much BYPRODUCT might, for example, cause a deterioration in the performance of the catalyst. 

Studies of individual bubbles rising in a two-dimensional gas—Hquid—soHd reactor provide detailed representations of bubble-wake interactions and projections of their impact on performance (Fig. 9). The details of flow, in this case bubble shapes, associated wake stmctures, and resultant bubble rise velocities and wake dynamics are important in characteri2ing reactor performance (26). 

Computer simulation of the reactor kinetic hydrodynamic and transport characteristics reduces dependence on phenomenological representations and idealized models and provides visual representations of reactor performance. Modem quantitative representations of laminar and turbulent flows are combined with finite difference algorithms and other advanced mathematical methods to solve coupled nonlinear differential equations. The speed and reduced cost of computation, and the increased cost of laboratory experimentation, make the former increasingly usehil. 

Scale-Up Principles. Key factors affecting scale-up of reactor performance are nature of reaction zones, specific reaction rates, and mass- and heat-transport rates to and from reaction sites. Where considerable uncertainties exist or large quantities of products are needed for market evaluations, intermediate-sized demonstration units between pilot and industrial plants are usehil. Matching overall fluid flow characteristics within the reactor might determine the operative criteria. Ideally, the smaller reactor acts as a volume segment of the larger one. Elow distributions are not markedly influenced by... 

Dimensional Analysis. Dimensional analysis can be helpful in analyzing reactor performance and developing scale-up criteria. Seven dimensionless groups used in generalized rate equations for continuous flow reaction systems are Hsted in Table 4. Other dimensionless groups apply in specific situations (58—61). Compromising assumptions are often necessary, and their vaHdation must be estabHshed experimentally or by analogy to previously studied systems. 

Reactor Internals and Unit Hardware. Requirements for mixing feed components or separating products may determine minimum pilot unit size. If reactants caimot be premixed before they are passed into the reactor, the effectiveness of the inlet distributor in mixing the reactants can markedly affect reactor performance. This is especially tme for gases, multiple phases, or Hquid streams of greatly different kinematic viscosities. 

In using a spreadsheet for process modeling, the engineer usually finds it preferable to use constant physical properties, to express reactor performance as a constant "conversion per pass," and to use constant relative volatiHties for distillation calculations such simplifications do not affect observed trends in parametric studies and permit the user quickly to obtain useful insights into the process being modeled (74,75). 

The distribution of residence times of reactants or tracers in a flow vessel, the RTD, is a key datum for determining reactor performance, either the expected conversion or the range in which the conversion must fall. In this section it is shown how tracer tests may be used to estabhsh how nearly a particular vessel approaches some standard ideal behavior, or what its efficiency is. The most useful comparisons are with complete mixing and with plug flow. A glossary of special terms is given in Table 23-3, and major relations of tracer response functions are shown in Table 23-4. 

On the other hand, this type of cooling permits the study of increasing or decreasing temperature profiles in the jacket and their influence on the inner temperature profile, reactor performance, and stability. For this type of study a reactor tube is needed that is large enough to accommodate an inner thermowell holding a multiple thermocouple assembly. 

In Chapter 1, Figure 1.4.1 (Berty et al, 1969) shows the actual measurement results of the older 5 diameter recycle reactor performance, using two different types of equipment. 

The predictions checked in the pilot-plant reactor were reasonable. Later, when the production unit was improved and operators learned how to control the large-scale reactor, performance prediction was also very good. The highest recognition came from production personnel, who believed more in the model than in their instruments. When production performance did not agree with model predictions, they started to check their instruments, rather than questioning the model. 

Solutions submitted by workshop participants exhibited more variation in reactor performance than had been expected. 

The basic problem of design was solved mathematically before any reliable kinetic model was available. As mentioned at start, the existence of solutions—that is, the integration method for reactor performance calculation—gave the first motivation to generate better experimental kinetic results and the models derived from them. 

The effect of physical processes on reactor performance is more complex than for two-phase systems because both gas-liquid and liquid-solid interphase transport effects may be coupled with the intrinsic rate. The most common types of three-phase reactors are the slurry and trickle-bed reactors. These have found wide applications in the petroleum industry. A slurry reactor is a multi-phase flow reactor in which the reactant gas is bubbled through a solution containing solid catalyst particles. The reactor may operate continuously as a steady flow system with respect to both gas and liquid phases. Alternatively, a fixed charge of liquid is initially added to the stirred vessel, and the gas is continuously added such that the reactor is batch with respect to the liquid phase. This method is used in some hydrogenation reactions such as hydrogenation of oils in a slurry of nickel catalyst particles. Figure 4-15 shows a slurry-type reactor used for polymerization of ethylene in a sluiTy of solid catalyst particles in a solvent of cyclohexane. 

The following details establish reactor performance, considers the overall fractional yield, and predicts the concentration profiles with time of complex reactions in batch systems using the Runge-Kutta numerical method of analysis. 

The space velocity SV is often used with conversion to descrihe die overall reactor performance. It is common in die petroleum and petrochemical industries to plot conversion against space velocity to descrihe die effect of feed rate on die performance of a flow system. 

Adesina [14] considered the four main types of reactions for variable density conditions. It was shown that if the sums of the orders of the reactants and products are the same, then the OTP path is independent of the density parameter, implying that the ideal reactor size would be the same as no change in density. The optimal rate behavior with respect to T and the optimal temperature progression (T p ) have important roles in the design and operation of reactors performing reversible, exothermic reactions. Examples include the oxidation of SO2 to SO3 and the synthesis of NH3 and methanol CH3OH. 

The major process parameters at selected periods in the four experiments are listed in Tables II, IV, VII, and VIII. Carbon recoveries ranged from 63 to 91%. Most of the losses occurred in connection with the recycle compressor system, and they decreased correspondingly the volume of product gas metered. Such losses, however, did not affect significantly the incoming gas to the main reactor or reactor performance. 

In order to be economically viable, a continuous emulsion polymerization process must be able to produce a latex which satisfies application requirements at high rates without frequent disruptions. Since most latex products are developed in batch equipment, the problems associated with converting to continuous systems can be significant. Making such a change requires an understanding of the differences between batch and continuous reactors and how these differences influence product properties and reactor performance. 

Recipe additions can also be important with semi-continuous reactors. Addition rates influence reactor performance, and incorrect addition location can lead to non-uniform reaction within the reactor, localized flocculation, and reactor short-circuiting. 

Ideally one would like a continuous reactor system to operate indefinitely at the desired steady-state. Unfortunately, a number of factors can cause shorter runs. Formation of wall polymer and latex flocculation is one such problem. This phenomenon can reduce reactor performance (for example, loss of heat transfer), lower product quality, and shorten run time. 

Reactor design can have a significant influence on reactor performance in a number of ways. Some aspects of reactor design such as heat transfer, structural design, etc., are reasonably well-understood. Other phenomena such as mixing details, latex flocculation, and the formation wall polymer are not completely understood. 

A number of the above features are included to reduce flocculation and the formation of wall polymer. While fundamental knowledge on flocculation or the formation of wall polymer is inadequate to establish the effects of all reactor design variables, the features of the Bayer reactor seem qualitatively correct. More fundamental work will be necessary to develop an understanding of the influence of design on reactor performance and product quality. 

The objectives of this presentation are to discuss the general behavior of non isothermal chain-addition polymerizations and copolymerizations and to propose dimensionless criteria for estimating non isothermal reactor performance, in particular thermal runaway and instability, and its effect upon polymer properties. Most of the results presented are based upon work (i"8), both theoretical and experimental, conducted in the author s laboratories at Stevens Institute of Technology. Analytical methods include a Semenov-type theoretical approach (1,2,9) as well as computer simulations similar to those used by Barkelew LS) ... 

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