Experiment planning interaction tables

Initial screens can be distinguished between methods that are used to determine what factors are most important, and follow-up screens that allow optimization and improvement of crystal quality (Table 14.1). In experimental design, this is known as the Box-Wilson strategy (Box et al., 1978). The first group of screens is generally based on a so-called factorial plan which determines the polynomial coefficients of a function with k variables (factors) fitted to the response surface. It can be shown that the number of necessary experiments n increases with 2 if all interactions are taken into account. Instead of running an unrealistic, large number of initial experiments, the full factorial matrix can... 

The analysis of variances using a CFE 2 plan in which, for each experimental point, we produce only one measurement, frequently presents an important residual variance. This result is a consequence of the fact that each point is the result of a particular combination of interaction effects. If, for each experimental point of the plan, we produce more experiments, then we have the normal possibility to compute a real residual variance (5.169). In this situation, the sum is successfully used as shown in Table 5.52 for the residual variance computation. 

The examples where a CFE 2 plan has been divided into two or four blocks are not explicit enough to develop the idea that the relations of the unification of blocks are selected randomly. In the next example, a CFE 2 plan is developed with the purpose being to show the procedures to select the unification relations of inter-blocks. In this plan, the actions showing a systematic influence will be divided into two blocks or into four blocks with, respectively, eight experiments or four experiments per block. We start this new analysis by building the CFE 2 plan. Table 5.64 contains this CFE 2 plan and also gives the division of the two blocks when we use the ABCD interaction as a unification relation. 

At this point, we have to verify the eorreetness of the selection of the unification relations. When S sSint we can conclude that our selection for the unification relations is good in this case, we can also note that the calculations have been made without errors. Otherwise, if computation errors have not been detected, we have to observe that the selected interactions for the unification of blocks are strong and then they carmot be used as unification interactions. In this case, we have to carry out a new experimental research with a new plan. However, part of the experiments realized in the previous plan can be recuperated. Table 5.68 contains the synthesis of the analysis of the variances for the current example of an esterification reaction. We observe that, for the evolution of the factors, the molar ratio of reactants (B) prevails, whereas all other interactions, except interaction AC (temperature-reaction time), do not have an important influence on the process response (on the reaction conversion). This statement is sustained by all zero hypotheses accepted and reported in Table 5.68. It should be mentioned that the alcohol quality does not have a systematic influence on the esterification reaction efficiency. Indeed, the reaction can be carried out with the cheapest alcohol. As a conclusion, the analysis of the variances has shown that conversion enhancement can be obtained by increasing the temperature, reaction time and, catalyst concentration, independently or simultaneously. 

Figure 15.2 illustrates some of the discovery bioactivity experiments in which a test compound must be successful to advance. If erroneous activity or selectivity data are generated or misinterpreted, the SAR will mislead the project team. SAR is a central strategy of drug-discovery research. If the activity assays are affected by properties in addition to target protein interaction, then the SAR will be a composite of multiple variables. Table 15.1 lists some of the potential effects on SAR from lack of property data application in planning and interpretation of drug-discovery bioassays. 

Design properties. Covering aspects related to redundancy, diversity and functionality of the specific equipment (see example in Table 3). Special considerations. Aspects related to previous experience with/maturity of the equipment, complexity of the design, possible weak points in the design, etc. Human machine interface. Aspects related to accessibility to and maintainability of the equipment, including planned level of human interaction in... 

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