Measurement design data

In which documents do you define your measurement design data ... 

Where test hardware (e.g., jigs, fixtures, templates, patterns) or test software are used as suitable forms of inspection, they are checked to prove that they are capable of verifying the acceptability of the product prior to release for use during production and installation and are rechecked at prescribed intervals. The extent and frequency of such checks is established, and records are maintained as evidence of control (see 4.16). Measurement design data are made available when required by the purchaser (client) or his/her representative for verification that the product is functionally adequate. 

One of the basic problems of crystalhzer design is the choice of method for measuring design data. The mode of operation, type of apparatus and scale of operation all have to be considered. Basically there are three choices to measure the data on the laboratory, pilot-plant or industrial-plant scale. The last of these choices is not uncommon, because many crystalhzers are designed on the basis of current experience. Generally, however, the choice hes somewhere between the laboratory unit and a pilot plant. 

Due to large improvements in computer technology in combination with new designs of area x-ray detector systems it is possible to extend the 2D-CT systems up to the third dimension. Therefor special algorithms and techniques for 3D-CT of the measured projection data and 3D visualisation and measurement of the results had to be developed. 

Finally, it cannot be overemphasized that despite instmmental measurements and data manipulations, it is the perception of the eye that still is the final arbiter as to whether or to what degree two colors match. Instmmental methods do serve well for the typical industrial task of maintaining consistency under sufficiently weU-standardized conditions however, a specific technique may not serve in extreme or unusual conditions for which it was not designed. 

The translation of the statistical design into physical units is shown in Table 5. Again the formulations were prepared and the responses measured. The data were subjected to statistical analysis, followed by multiple regression analysis. This is an important step. One is not looking for the best of the 27 formulations, but the... 

Some models require calibration to optimize input parameters they are best used in a research setting where it is possible to make measurements with which to calibrate the model for a particular site. Appropriate measured hydrologic data are seldom available to calibrate a model for a particular landfill site. Therefore, engineering models used for ET cover design should not require calibration. 

Permeabilities measured for pure gases can serve as a rough guide for selection of membrane materials. For design, data must be obtained on gas mixtures, where selectivities are often found to be much lower than those calculated from pure-component measurements. This effect is often due to plasticisation of the membrane by sorption of the most soluble component of the gas. This allows easier penetration by the less-permeable components. The problem of concentration polarisation, which is often encountered in small-scale flow tests, may also be responsible. Concentration polarisation results when the retention time of the gas in contact with the membrane is long. This allows substantial depletion of the most permeable component on the feed side of the membrane. The membrane-surface concentration of that component, and therefore its flux through the membrane, decreases. 

As analytical chemists, we are often called upon to participate in studies that require the measurement of chemical or physical properties of materials. In many cases, it is evident that the measurements to be made will not provide the type of information that is required for the successful completion of the project. Thus, we find ourselves involved in more than Just the measurement aspect of the investigation —we become involved in carefully (re)formulating the questions to be answered by the study, identifying the type of information required to answer those questions, making appropriate measurements, and interpreting the results of those measurements. In short, we find ourselves involved in the areas of experimental design, data acquisition, data treatment, and data interpretation. 

Ginn, C. M. R., Willett, R, and Bradshaw, J. (2000) Combination of molecular similarity measures using data fusion. Perspec. Drug Disc. Design 20, 1-16. 

Both die concentration and measurement raw data should be examined. It is important to see if the concentrations obtained are near the concentrations specified in the design and if the measurements look reasonable. Figure 5 90 is a plot of the reference concentrations which match well with the three evel factorial design specified. The corresponding raw spectral data plotted in Figure 5.91 appear to be reasonable (i.e., no apparent anomalous samples or variables). 

The equations discussed in the previous chapter, that described the variance per unit length of a solute band after passing through an LC column, were all significantly different. It is, therefore, necessary to identify the specific equation that most accurately describes the dispersion that takes place, so that it can be employed with confidence in the design of optimized columns. The different equations were tested against an extensive set of accurately measured experimental data by Katz et at (1) and, in order to identify the most pertinent equation, their data and some of their conclusions will be considered in this chapter. 

The discussion given in BS 4618, Sections 2.3 and 2.48 on design data for plastics applies in principle to rubbers, and stresses the advantage of measuring resistivity as a function of temperature, humidity, electric stress and time of electrification. 

The last step in the problem formulation phase is the development of an analysis plan or proposal that identifies measures to evaluate each risk hypothesis and that describes the assessment design, data needs, assumptions, extrapolations, and specific methods for conducting the analysis. There are three categories of measures that can be selected. Measures of effect (also called measurement end points) are measures used to evaluate the response of the assessment end point when exposed to a stressor. Measures of exposure are measures of how exposure may be occurring, including how a stressor moves through the environment and how it may co-occur with the assessment end point. Measures of ecosystem and receptor characteristics include ecosystem characteristics that influence the behavior and location of assessment end points, the distribution of a stressor, and life history characteristics of the assessment end point that may affect exposure or response to the stressor. These diverse measures increase in importance as the complexity of the assessment increases. 

Step 3. Prepare and weigh eight clean, dry planchets as in Step 1. Deliver 1 mL of water with a 1 mL volumetric pipette designated TD to each of 4 planchets and 0.010 mL of water with a 10-X pipette (TD) to each of the other 4 planchets and weigh them again. Record the measurements in Data Table 1.2. 

Therefore, the sorption characteristics Y(X) presented in Fig. 74—77 can only be used as design data for the injector type measured in the respective case. They can not be used for scale-up ... 

Design data are 1kg water per hour, P<300 bar and T<600 °C kept constant by a fluidized sandbath in which a 6 m tubular reactor coil with an inner diameter of 2 mm is submerged. 33 thermocouples measure the reaction temperature profiles. Water, organic material and the pressurized air can be preheated. 

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