Nominal concentration scale

However, the calculation according to the Eqs. (15.11) and (15.12) does not give a good correspondence to the experiment, espeeially for the temperature range ofT= 373 13 K in PC case. As it is known [38], in empirical modifications of Kemer equation it is usually supposed, that nominal concentration scale differs from mechanically effective filler fraction, which can be written accounting for the designations used above for natural nanocomposites as follows [41]. 

Shoiild the particles have a tendency to cohere slightly during sedimentation, each sampling time, representing a different nominal detention time in the clarifier, will produce different suspended-sohds concentrations at similar rates. These data can be plotted as sets of cui ves of concentration versus settling rate for each detention time by the means just described. Scale-up will be similar, except that detention time will be a factor, and both depth and area of the clarifier will influence the results. In most cases, more than one combination of diameter and depth will be capable of producing the same clarification result. 

Detention efficiency. Conversion from the ideal basin sized by detention-time procedures to an actual clarifier requires the inclusion of an efficiency factor to account for the effects of turbulence and nonuniform flow. Efficiencies vaiy greatly, being dependent not only on the relative dimensions of the clarifier and the means of feeding but also on the characteristics of the particles. The cui ve shown in Fig. 18-83 can be used to scale up laboratoiy data in sizing circular clarifiers. The static detention time determined from a test to produce a specific effluent sohds concentration is divided by the efficiency (expressed as a fraction) to determine the nominal detention time, which represents the volume of the clarifier above the settled pulp interface divided by the overflow rate. Different diameter-depth combinations are considered by using the corresponding efficiency factor. In most cases, area may be determined by factors other than the bulksettling rate, such as practical tank-depth limitations. 

A continuous cross-flow filtration process has been utilized to investigate the effectiveness in the separation of nano sized (3-5 nm) iron-based catalyst particles from simulated Fischer-Tropsch (FT) catalyst/wax slurry in a pilot-scale slurry bubble column reactor (SBCR). A prototype stainless steel cross-flow filtration module (nominal pore opening of 0.1 pm) was used. A series of cross-flow filtration experiments were initiated to study the effect of mono-olefins and aliphatic alcohol on the filtration flux and membrane performance. 1-hexadecene and 1-dodecanol were doped into activated iron catalyst slurry (with Polywax 500 and 655 as simulated FT wax) to evaluate the effect of their presence on filtration performance. The 1-hexadecene concentrations were varied from 5 to 25 wt% and 1-dodecanol concentrations were varied from 6 to 17 wt% to simulate a range of FT reactor slurries reported in literature. The addition of 1-dodecanol was found to decrease the permeation rate, while the addition of 1-hexadecene was found to have an insignificant or no effect on the permeation rate. 

The parameter ad is included to allow for catalyst deactivation as shown by Liebman. The data for the example (physical constants) are shown in Table 1. All temperatures and concentrations were scaled using a nominal reference concentration (Ar = 1 x 10-6 gmol cm-3) and a nominal reference temperature (Tr = 100.0 K). 

The results provided by three-dimensional MRTM are consistent with the numerical output of one-dimensional MRTM. The concentration-depth curves are shown to be similar for a nominal test case that is independent of temporal and spatial scales. Besides the numerical output that the model generates, the visualization component of the model gives an almost instantaneous look into the spatial distribution of the contaminant. This visualization is made by sliding three planes (horizontal, longitudinal, and transversal) across the entire simulation domain. Concentrations are scaled from 0.0 to the maximum values so that the trace concentrations can be easily visualized. The numerical value of the maximum concentration is also output in the visualization window, together with the current position of the visualization plane. When the trace compound is hazardous (e.g., a heavy metal such as mercury), it is also necessary to monitor the spatial distribution of very low concentrations. The current three-dimensional, MRTM visualization method provides the means to track these types of trace concentrations. 

The formulation and product presentation may require adjustments as the product progresses through clinical studies. Concentration of the active and dose content may be adjusted for materials in later clinical studies. If the changes are nominal, then there may be little need for adjustment to the process. Significant changes warrant further development as the lyophiliza-tion processing conditions require refinement and may require subsequent scale-up studies. 

The meaning of each code is specified using a six-part name. The six parts are component (what is measured), kind of property (e.g., concentration versus titer), time aspect (single point in time versus timed collection), system (specimen) type, type of scale (e.g., quantitative, ordinal, and nominal), and method. For example, the LOINC name for a... 

Another division of the factors can be made into mixture-related, quantitative (continuous), or qualitative (discrete) factors (4,5,16,18,24). A mixture-related factor in CE is usually related to a mixture of solvents, for example, the composition of the background electrolyte solution. A quantitative factor can vary on a continuous scale, for example, the buffer pH, the electrolyte concentration, the additive concentration, the capillary temperature, or the voltage. A qualitative factor, on the other hand, varies on a discrete nominal scale, for example, batch or manufacturer of a reagent, solvent, or capillary. 

The scales are hierarchically arranged from least information provided (nominal) to most information provided (ratio). Any scale can be degraded to a lower scale, eg, interval data can be treated as ordinal. For the USMLE, concentrate on identifying nominal and interval scales. 

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