Factor selection quantitative factors

After a complete study of quantitative factors, the selection of the building or buildings must be considered. Standard factory buildings are to be desired but if none can be found satisfactory to handle the space and process requirements of the chemical engineer, an architect specializing in this area should be consulted to design a building around the process - as opposed to a beautiful structure into which a process must fit. 

The selected factors are either mixture-related, quantitative (continuous), or qualitative (discrete).A mixture-related factor is, for instance, the fraction organic solvent in the buffer system. Examples of quantitative factors are the electrolyte concentration, the buffer pH, the capillary temperature, and the voltage, and of qualitative factors the manufacturer or the batch number of a reagent, solvent, or capillary. Sample concentration (see Table 1) is a factor sometimes included. However, the aim of the method tested is to determine this concentration through the measured signal, from a calibration procedure. Thus, one evaluates the influence of the sample concentration on the sample concentration, which we do not consider a good idea. 

In robustness tests, usually the factors are examined at two extreme levels.For mixture-related and quantitative factors, these levels usually are chosen symmetrically around the nominal. The range between the extreme levels is selected so that it represents the variability that can occur when transferring the method.However, specifications to estimate such variability are not given in the ICH guidelines. Often the levels are chosen based on personal experience, knowledge, or intuition. Some define the extreme levels as nominal level +x%. However, this relative variation in factor levels is not an appropriate approach, since the absolute variation then depends on the value of the nominal level. Another possibility is to define the levels based on the precision or the uncertainty, with which... 

The factors in Table 2 were selected from the chiral separation methods for propranolol, praziquantel, and warfarin. All factors were quantitative and their extreme levels situated symmetrically around the nominal. 

The factors in Table 3 were selected from a non-aqueous chiral separation method for timolol. One qualitative factor (1), i.e., the type of CE equipment, was examined. Two HPCE systems, A and B, with different software versions for equipment control, data acquisition, and handling were compared. Six quantitative factors ((2) till (7)), for which the extreme levels usually were situated symmetrically around the nominal, also were... 

In a second step the levels for the chosen factors are selected. For quantitative factors one considers a low and a high extreme level that is respectively smaller and larger than the nominal one. The nominal level is the level for the factor as it is given in the description of the procedure or the one that is most likely to occur in the case it is not specified in the analytical procedure. The levels for the factors are chosen in such a way that they represent the maximum difference in the values of the factors that could be expected to occur when a method is transferred from one laboratory to another without the occurrence of major errors [4]. 

The network design phase already determined the countries where plants should be located or closed. Thus, site selection takes place within an individual country. As the location factors pertinent to the site selection phase are different from those used in the network design phase, the first step again is to establish the relevant location factors. These are mostly of qualitative nature but also include quantitative factors such as local factor cost differences, property and construction costs. Findings from industrial location science (cf. Chap. 2.2.2) can be used as a starting point to define the location factors, but industry-, company- and project-specific factors... 

Having selected the system response, we start choosing factors, levels of the factors and center point of the design (basic level or the null point). By factor we understand the controllable independent variable that corresponds to one possibility of influence on the object of research. A factor is considered defined if its name and domain of factors are determined. A factor may take several values in this field. The chosen factor values, both qualitative and quantitative, are called factor variation levels. Factor variation levels in the design of experiments are coded values. Under factor interval of variation we understand the difference between two factor levels, which in their coded form have value one. When selecting the factors one should pay attention to the conditions they must meet. 

All GC detectors are more or less selective, which is a complicating factor in quantitative analysis by GC. However, some of them display such a high selectivity towards certain elements or functionalities that they can be used advantageously for identification... 

Optimum conditions. However, modeling a qualitative factor has no meaning because only discrete levels are possible and no intermediate values occur. Therefore, only mixture-related and quantitative factors are examined in the optimization step. Sequential optimization methods select successive experiments in the factor domain, which implies that again only mixture-related and quantitative factors can be examined. 

In robustness testing, the extreme levels are most frequently chosen symmetrically around the nominal for mixture-related and quantitative factors. However, for some factors, an asymmetric interval might better represent the reality or better reflect the change in response occurring. A first example is the capillary temperature. Suppose a capillary temperature of 15 °C is prescribed. Symmetric levels, selected based on uncertainty are, for instance, 10 °C and 20 °C. However, many cooling systems do not allow temperatures of more than 10 °C below room temperature therefore, 10 °C may not be attained accurately by the instrument. The lowest extreme level could then be taken equal to the nominal (15 °C). 

During an optimization phase in method development, the three factors in Table 2.3 were selected to develop the enantioseparation of a nonsteroidal anti-inflammatory drug (28). All examined factors were quantitative (A-C). 

The four factors in Table 2.4 were selected from a robustness test on a CE method to determine rufloxacin hydrochloride in coated tablets (29). AU factors were quantitative (A-D) and their extreme levels are situated symmetrically around the nominal. 

It is better to replicate experiments within the design, thus estimating the experimental repeatability. If all factors are quantitative it is the centre point that is selected. This has the advantages that if the experimental standard deviation changes within the domain one could reasonably hope that the centre point would represent a mean value and also that if the response within the domain is curved, this curvature may be detected. 

The same mechanism al.so applies qualitatively to chlorination of other alkanes. The only difference is in the nature of the C-H bonds available in the alkane to be broken. Tliey are generally less strong than those in methane, following a DH° order of Cll.i > I" > 2 > 3 . (Note I = primary, 2° = secondary, and 3 = tertiary. Thc.se arc commonly used symbols.) The weakest (3 ) are the most readily broken tlms. alkanes with different types of C-H bonds display a built-in xflerimiy of 3" - 2" > I" in their reactions with chlorine. This. section describes this. selectivity quantitatively, illustrating how both reactivity differences and. siaiisiic il factors combine to produce the observed ratios of products in. several rcprescnlativc sy.siems. 

The coded units are obtained by selecting for each factor a value that constitutes a center of interest or a reference value, and then selecting certain values that are below and above that center of interest by an equivalent amount. This is a straightforward process for quantitative factors but may not be possible for some qualitative factors that can exist at only two levels. In this case the center of interest is considered as theoretical or conceptual. The coded units for any v, arc defined by... 

Through the work mentioned above, storage coal spontaneous combustion evaluation system, evaluation factors and quantitative evaluation method are initially obtained. Through exploration solve the evaluation problem of coal spontaneous combustion tendency of port. In this paper, the results selected six kinds of coal can use for the later experimental study on its performance, and simulation study on its storage in silos. Provide support for the security operation of 3nd silo in Huanghua port. 

Supplier selection process is difficult because the criteria for selecting suppliers could be conflicting. Figure 6.1 illustrates the various factors which could impact the supplier selection process (Sonmez, 2006). Supplier selection is a multiple criteria optimization problem that requires trade-off among different qualitative and quantitative factors to find the best set of suppliers. For example, the supplier with the lowest unit price may also have the lowest quality. The problem is also complicated by the fact that several conflicting criteria must be considered in the decision making process. 

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