Data plotting

Using the curve given by the square points in Fig. XVI-2, make the qualitative reconstruction of the original data plot of volume of mercury penetrated per gram versus applied pressure. 

A systematic comparison of two sets of data requires a numerical evaluation of their likeliness. TOF-SARS and SARIS produce one- and two-dhnensional data plots, respectively. Comparison of sunulated and experimental data is accomplished by calculating a one- or two-dimensional reliability (R) factor [33], respectively, based on the R-factors developed for FEED [34]. The R-factor between tire experimental and simulated data is minimized by means of a multiparameter simplex method [33]. 

Assuming that an equilibrium is now well established, the simulation may be restarted (not newly started) to begin with the sampling of structural and thermodynamic data. In our model case, data acquisition was performed for 3 ns (trajectory data plot not shown). For the production phase, also, the time evolution of the variables mentioned above should be monitored to detect stability problems or con-... 

Figure 8.9 Osmotic pressure data plotted as n/RTc2 versus concentration for nitrocellulose in three different solvents. [Data from A. Dobry,/. Chem. Phys. 32 50 (1935).]...

Figure 10.8 shows two sets of data plotted according to these conventions, after correction for the effect of interference. In Fig. 10.8a, HC2/T is plotted against C2 for three different fractions of polystyrene in methyl ethyl ketone. Figure 10.8b shows Kc2/Rg versus C2 for solutions of polystyrene in cyclohexane at five different temperatures. These results are discussed further in the following example.

Figure 10.8 Light-scattering data plotted to give slope-intercept values which can be interpreted in terms of M and B. (a) Polystyrene in methyl ethyl ketone. [From B. A. Brice, M. Halwer, and R. Speiser,/. Opt. Soc. Am. 40 768 (1950), used with permission.] (b) Polystyrene in cyclohexane at temperatures indicated. Units of ordinates are given in Example 10.4. [Reprinted with permission from W. R. Krigbaum and D. K. Carpenter,7. Phys. Chem. 59 1166 (1955), copyright 1955 by the American Chemical Society.]...

The graphics capabiUties of the CAD/CAM environment offer a number of opportunities for data manipulation, pattern recognition, and image creation. The direct appHcation of computer graphics to the automation of graphic solution techniques, such as a McCabe-Thiele binary distillation method, or to the preparation of data plots are obvious examples. Graphic simulation has been appHed to the optimisation of chemical process systems as a technique for energy analysis (84). 

Using the vapor-liquid equihbrium data, plot a y-x diagram. Draw a number of operating lines at a slope of 0.75. Note the composition at the 45° intersection, and step off seven plates on each to find the equilibrium value of the bottoms. Some of the results are tabulated in the following table ... 

From these data, plots are made of volumetric efficiency and agitation speed at each throughput level from these plots the condition which gives the maximum volumetric efficiency is selected for scale-up. 

Viscosity Centipoises Gunness and Other Data Plotted Versus Reciprocal Viscosity in Maxweli. (Average viscosity of iiquid on the piates) Drickamer and Bradford Correlation Plotted in Ludwig. (Molal average viscosity of the feed)... 

Fig. 5.3-2 Flood data plotted lognormally, from Wall.

The data plotted in the figure clearly support the predicted positive dependence of crystal size on agitation rate. Precipitation in the crystal film both enhances mass transfer and depletes bulk solute concentration. Thus, in the clear film model plotted by broken lines, bulk crystal sizes are initially slightly smaller than those predicted by the crystal film model but quickly become much larger due to increased yield. Taken together, these data imply that while the initial mean crystal growth rate and mixing rate dependence of size are... 

A data plot, as displayed in Fig. 4-15, may then constitute a useful support for the simple selection of candidate CSPs that should be available in a laboratory. For purposes of comparison, luminance data were scaled by normalizing the data in the range [0,100] by means of the following equation ... 

Figure 62.9 Generator fan failure data plotted on log normal hazard data...

If a data plot on a chosen hazard paper departs significantly from a straight line by being bowed up or down, it is an indication that the data should be re-plotted on a... 

If estimated of distribution parameters are desired from data plotted on a hazard paper, then the straight line drawn through the data should be based primarily on a fit to the data points near the center of the distribution the sample is from and not be influenced overly by data points in the tails of the distribution. This is suggested because the smallest and largest times to failure in a sample tend to vary considerably from the true cumulative hazard function, and the middle times tend to lie close to it. Similar comments apply to the probability plotting. 

Failure times are typically recorded to the nearest hour, day, month, hundred miles, etc. This is so because the method of measurement has limited accuracy or because the data are rounded. For example, if units are inspected periodically for failure, the exact time of failure is not known but only the period in which the failure occurred. For data plotting, the amount that failure times are rounded off should be considerably smaller than the spread in the distribution of time to failure. 

Figure 13-6. The vullage dependence ol the cuinenl densities in the dark (open triangles), under illumination (open circles) and their difference (filled squares) for an fTO/MEH-PPV/Mg OLED. The inset shows the same data plotted as tire logarithm of the current (difference)- Reproduced with permission front [97J.

Figure 69, Spectral and concentration data plotted side-by-side to show the congruence of the points in the two different data spaces.

The performance data for plug versus mix reactor were obtained. The data were collected as the inverse of qx vs inverse of substrate concentration. Table E.1.1 shows the data based on obtained kinetic data. From the data plotted in Figure E.1.1, we can minimise the volume of the chemostat. A CSTR works better than a plug flow reactor for the production of biomass. Maximum qx is obtained with a substrate concentration in the leaving stream of 12g m 3. 

Fatigue data are normally presented as a plot of the stress (S) versus the number of cycles (N) that cause failure at that stress the data plotted defined as an S-N curve (Fig. 2-43). The use of an S-N curve is used to establish a fatigue endurance limit strength. The curve asymptotically approaches a parallel to the abscissa, thus indicating the endurance limit as the value that will produce failure. Below this limit the material is less susceptible to fatigue failure. 

A comparison of standard deviation s to standard counting error sc is thus a useful criterion for the reliability of analytical results obtained by x-ray emission. To illustrate the simplest possible application of this criterion, consider again the x-ray data plotted in Figure 10-3, which are given in Table 10-1. The individual N s summarized in the table would, in x-ray emission spectrography, appear eventually as analytical results that is, as the Us of Figure 10-1 with x as their mean. For these 393 individual ATs, the standard deviation is... 

The values used in plotting Figs. 2-1 and 2-2 can be used to illustrate the method for first-order and second-order data. Plots of t/E versus time are shown in Fig. 2-9. The second-order data define a precise straight line, and those for n = 1 are linear to E < 0.4. The latter graph has a slope of 0.6, giving n = 1.2. 

The chemical compositions of geothermal water plot in Figs. 23-2.9. All data plot near the equilibrium curves. This indicates that the geothermal waters considered... 

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