Base-incorporation factor, determination

In another study, a mathematical model was constructed that incorporated experimentally determined glutaraldehyde persistence, rates of water production, and other factors. The model was used to calculate the levels of glutaraldehyde in a specified environment (lagoons) as a function of time, based on the amount of glutaraldehyde applied down hole [476]. 

Another principal difficulty is that the precise effect of local dynamics on the NOE intensity cannot be determined from the data. The dynamic correction factor [85] describes the ratio of the effects of distance and angular fluctuations. Theoretical studies based on NOE intensities extracted from molecular dynamics trajectories [86,87] are helpful to understand the detailed relationship between NMR parameters and local dynamics and may lead to structure-dependent corrections. In an implicit way, an estimate of the dynamic correction factor has been used in an ensemble relaxation matrix refinement by including order parameters for proton-proton vectors derived from molecular dynamics calculations [72]. One remaining challenge is to incorporate data describing the local dynamics of the molecule directly into the refinement, in such a way that an order parameter calculated from the calculated ensemble is similar to the measured order parameter. 

Acceptable limits may differ considerably for the measured property values, however. For a majority of cases, responses can be classified as having either a maximum acceptable value, a minimum acceptable value, or a most desirable value within an acceptable range. For each response, these cases may be incorporated into a desirability factor having the range 0 < d < 1 with d 0 denoting an unacceptable response value and d 1 representing totally acceptable behavior. Intermediate values are determined by equations (22) based on the acceptable limits, the desired response value, and the type of response behavior desired by the formulator. 

Screening assessments incorporate variability and uncertainty implicitly, by using worst-case assumptions and safety factors. As mentioned earlier, these have rarely been based on a quantitative analysis and may not take account of the full range of uncertainties, so in principle they should be reviewed to determine whether they provide adequate margins of safety. 

Current quality system models call for audits to be conducted at planned intervals to evaluate effective implementation and maintenance of the quality system and to determine if processes and products meet established parameters and specifications. International standards provide guidance on auditing [13]. Audit procedures should be developed and documented to ensure that the planned audit schedule takes into account the relative risks of the various quality system activities. Factors that can be incorporated into a risk-based approach to planning audit frequency and scope include the following [6] ... 

In order for a process to be controllable by machine, it must represented by a mathematical model. Ideally, each element of a dynamic process, for example, a reflux drum or an individual tray of a fractionator, is represented by differential equations based on material and energy balances, transfer rates, stage efficiencies, phase equilibrium relations, etc., as well as the parameters of sensing devices, control valves, and control instruments. The process as a whole then is equivalent to a system of ordinary and partial differential equations involving certain independent and dependent variables. When the values of the independent variables are specified or measured, corresponding values of the others are found by computation, and the information is transmitted to the control instruments. For example, if the temperature, composition, and flow rate of the feed to a fractionator are perturbed, the computer will determine the other flows and the heat balance required to maintain constant overhead purity. Economic factors also can be incorporated in process models then the computer can be made to optimize the operation continually. 

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