Flow patterns, interpretation

This gives two choices ia interpreting calculated NRR values, ie, a direct comparison of NRR values for different options or a comparison of the NRR value of each option with a previously defined NRR cutoff level for acceptabiUty. The NPV, DTC, and NRR can be iaterpreted as discounted measures of the return, iavestment, and return rate, analogous to the parameters of the earher example. These three parameters characterize a venture over its entire life. Additional parameters can be developed to characterize the cash flow pattern duting the early venture years. Eor example, the net payout time (NPT) is the number of operating years for the cumulative discounted cash flow to sum to zero. This characterizes the early cash flow pattern it can be viewed as a discounted measure of the expected operating time that the investment is at risk. 

The latest tw o-phase flow research and design studies have broadened the interpretation of some of the earlier flow patterns and refined some design accuracy for selected situations. The method presented here serves as a fundamental reference source for further studies. It is suggested that the designer compare several design concept results and interpret which best encompasses the design problem under consideration. Some of the latest references are included in the Reference Section. No one reference has a solution to all two-phase flow problems. 

In this chapter, we describe several ideal types of reactors based on two modes of operation (batch and continuous), and ideal flow patterns (backmix and tubular) for the continuous mode. From a kinetics point of view, these reactor types illustrate different ways in which rate of reaction can be measured experimentally and interpreted operationally. From a reactor point of view, the treatment also serves to introduce important concepts and terminology of CRE (developed further in Chapters 12 to 18). Such ideal reactor models serve as points of departure or first approximations for actual reactors. For illustration at this stage, we use only simple systems. 

Show that the results may be interpreted on the assumptions that the solids are completely mixed, that the gas leaves in equilibrium with the solids and that the adsorption isotherm is linear over the range considered. If the flowrate of gas is 0.679 x 10-6 kmol/s and the mass of solids in the bed is 4.66 g, calculate the slope of the adsorption isotherm. What evidence do the results provide concerning the flow pattern of the gas ... 

As mentioned earlier, obtaining and interpreting the actual experimental flow pattern is usually impractical. Hence, the approach taken is to postulate a flow model which reasonably approximates real flow, and then use this flow model for predictive purposes. Naturally, if a flow model closely reflects a real situation, its predicted response curves will closely match the tracer-response curve of the real vessel this is one of the requirements in selecting a satisfactory model. 

The problem of two-phase-flow classification is complicated by the inevitable differences due to individual interpretations of visual observations, and also by differences in terminology. Fig. 1, taken from the work of Govier et al. (G6), shows the variation in terminology used for vertical gas-liquid flow patterns. It includes a classification of flow regimes proposed by Govier et al. based on pressure-drop behavior rather than visual observations, as illustrated in Fig. 2. In the definitions adopted by the writer, which follow, an attempt has been made to select the most... 

The residence time distribution of the recycle reactor was determined by tracer experiments. This permitted the interpretation of the flow patterns in the reactor, so that the degree of mixing could be quantified. 

This concept was used for the study of the deactivation of n-hexane catalytic cracking on a US Y zeolite catalyst. The interpretation of the flow patterns in the recycle reactor, necessary for the quantification of the degree of mixing, was based upon tracer experiments. 

Unambiguous definitions of activity, selectivity, and conversion are needed to interpret the results correctly. It is also important to know the quality of the results. What is the reproducibility and the statistical validity of the conclusions Were heat and mass transfer disguises eliminated What tests were done to confirm ideal flow pattern, isobaricity, and isothermality Often it is necessary to know whether catalyst performance is adequate. For that reason the space time yield and catalyst life characterization need to be addressed. 

The two most important criteria are an ideal flow pattern and isothermal operation. An ideal flow pattern, either plug flow or perfectly mixed, is essential for a straightforward interpretation of reaction data. Isothermal operation is critical of generally reliable quantitative laboratory catalyst data. 

Several sophisticated techniques and data analysis methodologies have been developed to measure the RTD of industrial reactors (see, for example, Shinnar, 1987). Various different types of models have been developed to interpret RTD data and to use it further to predict the influence of non-ideal behavior on reactor performance (Wen and Fan, 1975). Most of these models use ideal reactors as the building blocks (except the axial dispersion model). Combinations of these ideal reactors with or without by-pass and recycle are used to simulate observed RTD data. To select an appropriate model for a reactor, the actual flow pattern and its dependence on reactor hardware and operating protocol must be known. In the absence of detailed quantitative models to predict the flow patterns, selection of a model is often carried out based on a qualitative understanding of flow patterns and an analysis of observed RTD data. It must be remembered that more than one model may fit the observed RTD data. A general philosophy is to select the simplest model which adequately represents the physical phenomena occurring in the actual reactor. 

Experimental measurements describing the detailed characteristics of the flow pattern including statistical data analysis is thus of vital importance. On the other hand, statistical descriptors such as the variance, covariance, standard deviation and turbulence intensity are of limited usefulness unless we can physically interpret them. 

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