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Sensitivity plots

Figure 7. Sensitivity plotted as a function of angle of incidence on the sensing layer. Figure 7. Sensitivity plotted as a function of angle of incidence on the sensing layer.
Reduced sensitivity Plot a third curve with a shallower gradient. This represents decreased sensitivity such that a greater increment in Paco2 is required in order to achieve the same increment in MV. Also seen with opiates. [Pg.140]

Figure 11. A sensitivity plot for a positive-tone experimental e-beam resist. The data is from Figure 8. Figure 11. A sensitivity plot for a positive-tone experimental e-beam resist. The data is from Figure 8.
Strauss Plot R. Strauss (Chem. Eng., pp. 112-116, Mar. 25, 1968) developed a sensitivity plot, in Fig. 9-14, in which the ordinate is a measure of profitabihty and the abscissa is the change in a variable greater than (or less than) the value used in the base case. Where the abscissa crosses the ordinate is the result of the base case of NPW, return, annual worth, etc. The slope of a line on this spider plot is the degree of change in profitability resulting from a change in a... [Pg.32]

Relative Sensitivity Plot Another type of analysis developed by J. C. Agarwal and I. V. Klumpar (Chem. Eng., pp. 66-72, Sept. 29, 1975) is the relative sensitivity plot. The variables studied are related to those in the base case, and the resulting plot is the relative profitability. [Pg.33]

It has been shown that the two-phase pressure variation for palladium-hydrogen yields values of ASa— and AHa—/ , and that these values are closely temperature independent. The temperature independence results because the value of the integral in Equation 21 can be approximated closely by the corresponding relative partial value at the critical composition. Variations of ASg o and AHh—o with temperature (see Figure 6) are apparently too small to be detected in the plot of In /22(two-phase) against T ly but can be detected by the more sensitive plot of RT In P /2 against T or possibly could be detected by calorimetric determinations of AHa—over a wide temperature range. [Pg.307]

Fig. 7 Sensitivity plot (ratio of signal to analyte vs. analyte) for an LC-assay procedure with UV detection. The means (squares) and the individual (diamonds) sensitivity values, the average sensitivity (solid line) and the 2% limits (dotted lines) are displayed. The larger scattering for the lower concentrations is caused by their larger influence (weight) on the ratio, compared with larger concentrations. Fig. 7 Sensitivity plot (ratio of signal to analyte vs. analyte) for an LC-assay procedure with UV detection. The means (squares) and the individual (diamonds) sensitivity values, the average sensitivity (solid line) and the 2% limits (dotted lines) are displayed. The larger scattering for the lower concentrations is caused by their larger influence (weight) on the ratio, compared with larger concentrations.
Another plot often presented is a plot of calculated data versus observed data (with a line representing identity). This can be a sensitive plot for the assessment of the model chosen. Significant systematic deviations from the identity line may indicate the need for additional parameters in the model. [Pg.2767]

One of the most significant problems with the search for sensitivity predictors lies in the misuse of correlation analysis. It is a fundamental rule of statistical analysis that the data that are used to infer a correlation cannot be used to prove its existence. So if a study of these four substituted benzene compounds suggested that sensitivity is correlated with some spectroscopic transition or some bond parameter, then the existence of this correlation can only be proven by examining its validity using a large number of other materials not used to infer the correlation s existence. A true theory of sensitivity that resulted should be better than one which simply reaffirms the position of four compounds on a sensitivity plot—it should be equally able to tell us the relative sensitivities of new and different explosive compounds and in addition that nonexplosive compounds such as sodium chloride or liquid nitrogen will not explode. [Pg.142]

FIGURE 4.23 Simulated resolution/sensitivity plots for FAIMS with parameters as in Figure 4.12 line is from Figure 4.22b, symbols show the results of resolution control using scalable ripple at wr = 600 Hz (triangles), 60 Hz (squares), and 20 Hz (diamonds). (From Shvartsburg, A.A., Tang, K., Smith, R.D., J. Am. Soc. Mass Spectrom., 16, 1447, 2005.)... [Pg.246]

Photodiode arrays Basically, these arrays are made up of silicon photodiodes linearly set up within an integrated circuit. The numbers in each array are typically 512 or 1024. The photocurrent from each diode causes a capacitance build-up proportional to the incident light flux, which is read by sequentially discharging the array elements via an analog-to-digital converter for data processing. These arrays have a spectral response similar to that of the silicon photodiode, i.e., 180-1100 nm. A typical sensitivity plot is shown in Figure 8. [Pg.3494]

Figure 9 is the sensitivity plot highlighting the relative importance of the random input variables on S 1 TE. In this figure, a positive correlation for a given RIV means that as this variable increases so does SI TE, and vice versa. The following can be concluded for figure 9 ... [Pg.164]

Figure 9 - Sensitivity plot showing random variabies with highest impact on the maximum principal stress in the TE legs (S1 TE). Material designations are 1 = contact material 2 = insulation or substrate material 3 = TE material. Figure 9 - Sensitivity plot showing random variabies with highest impact on the maximum principal stress in the TE legs (S1 TE). Material designations are 1 = contact material 2 = insulation or substrate material 3 = TE material.
Figure 10 is the sensitivity plot highlighting the relative importance of the random input variables on SI Jnsuiator. The following can be concluded ... [Pg.166]

The practice of SM with chemical kinetics models showed that not all active variables can be determined by optimization, as the system as a whole is under-constrained. In other words, the objective function is not sufficiently sensitive to some of the optimization variables—those that are in the tail of the screening sensitivity plot. The low sensitivity manifests itself in the appearance of the valley in the objective function, as discussed in the beginning of this chapter, and this geometric feature makes it difficult to determine numerically the global minimum, and the... [Pg.268]

The sensitivity plots of possible TPMs (e.g.. Figs 4 5) help us to select appropriate TPM at given design condition, at least qualitatively. For example, RR is a poor choice of TPM if the plant is designed at RR=10 (Fig. 4) and composition variation is a poor choice to handle production rate change if the plant is designed atyA/(VA+yB)=0.5 (Fig. 5). [Pg.477]

Figure 8.25 shows the open-loop sensitivity plots for both columns. The one on the left-hand side is for the heterogeneous azeotropic column with 1% changes in its reboiler duty, and the one on the right-hand side is for the preconcentrator/recovery column with 1% changes in its reboiler duty. Tray 7 of the heterogeneous azeotropic column and tray 9 of the preconcentrator/recovery column are selected as the temperature control points because of high sensitivity and nearly linear behavior. [Pg.240]

Figure 9.25 Closed-loop sensitivity plot for the system without m-xylene impurity. Figure 9.25 Closed-loop sensitivity plot for the system without m-xylene impurity.
Figure 10.22 Open-loop sensitivity plots for +0.1% changes in Qi-... Figure 10.22 Open-loop sensitivity plots for +0.1% changes in Qi-...

See other pages where Sensitivity plots is mentioned: [Pg.466]    [Pg.132]    [Pg.339]    [Pg.2306]    [Pg.102]    [Pg.103]    [Pg.976]    [Pg.442]    [Pg.980]    [Pg.501]    [Pg.245]    [Pg.77]    [Pg.80]    [Pg.155]    [Pg.156]    [Pg.171]    [Pg.120]    [Pg.240]    [Pg.264]   
See also in sourсe #XX -- [ Pg.105 ]




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Parameter sensitivity analysis plots

Relative Sensitivity Plot

Selectivity sensitivity plots

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