Experimental design full factorial

The final structure of resins produced depends on the reaction condition. Formaldehyde to phenol (F/P) and hydroxyl to phenol (OH/P) molar ratios as well as ruction temperahne were the most important parameters in synthesis of resols. In this study, the effect of F/P and OH/P wt%, and reaction temperature on the chemical structure (mono-, di- and trisubstitution of methyrol group, methylene bridge, phenolic hemiformals, etc.) was studied utilizing a two-level full factorial experimental design. The result obtained may be applied to control the physical and chemical properties of pre-polymer. 

A two levels of full factorial experimental design with three independent variables were generated with one center point, which was repeated[3]. In this design, F/P molar ratio, Oh/P wt%, and reaction temperature were defined as independent variables, all receiving two values, a high and a low value. A cube like model was formed, with eight comers. One center point (repeated twice) was added to improve accuracy of the design. Every analysis results were treated as a dependent result in the statistical study. 

A two level full factorial experimental design with three variables, F/P molar ratio, OH/P wt %, and reaction temperature was implemented to analyses the effect of variables on the synthesis reaction of PF resol resin. Based on the composition of 16 components of 10 samples, the effect of three independent variables on the chemical structure was anal3 ed by using 3 way ANOVA of SPSS. The present study provides that experimental design is a very valuable and capable tool for evaluating multiple variables in resin production. 

The absorption spectra of Aspt, Ace-K, Caf and Na-Benz were recorded from 190 to 300 nm. The calibration set was generated by a three-level full factorial design (4).The absorbance valnes were recorded eveiy 5 nm. The calibration samples were measured in random order, so that experimental errors due to drift were not introduced. 

In this chapter we explore factorial-based experimental designs in more detail. We will show how these designs can be used in their full factorial form how factorial designs can be taken apart into blocks to minimize the effect of (or, if desired, to estimate the effect of) an additional factor and how only a portion of the full factorial design (a fractional replicate) can be used to screen many potentially useful factors in a very small number of experiments. Finally, we will illustrate the use of a Latin square design, a special type of fractionalized design. 

Figure 12.4 The spatial representations of four different types of experimental designs that are useful for process analyzer calibration (A) full-factorial, (B) Box-Behnken, (C) face-centered cube, and (D) central composite.

The second approach is to perform traditional pre-formulational studies using full factorial or Plackett Burman experimental designs [15]. Here, the preferred analytical methodology tends to be thermal and spectroscopic, rather than chromatographic, although the latter methodologies are still utilised. Differential scanning calorimetry (DSC), isothermal calorimetry (ITC) or Fourier-transform infrared (FT-IR) spectroscopy have all been utilised successfully. 

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... 

FIGURE 5.90. Concentration of component B versus concentration of component A in a three-level, full-factorial experimental design. 

When few factors (/ from two to four) are studied, the full factorial design is the most common approach. The full factorial scheme is the basis for all classical experimental designs, which may be used in more complex situations. For a general two-level full factorial design, each factor has to be considered at a low level (coded as —1) and a high level... 

When all of the variables are quantitative, an estimate of the experimental error can be obtained by adding to the full factorial, fractional factorial or Plackett-Burman design, a number of runs at the center of the design. The center of the design is the midpoint between the low and high settings of the two-level factors in the experiment. Thus, if there are p variables, and the levels of the variables have been coded (-1, +1), then the center of the design is (Xj, = (0, 0,. .., 0). If the... 

For this reason one prefers to apply an experimental design. In the literature a number of different designs are described, such as saturated fractional factorial designs and Plackett-Burman designs, full and fractional factorial designs, central composite designs and Box-Behnken designs [5]. 

Figure 5.3 A full factorial experimental design spanning 3 factor...

THE EXPERIMENTAL CONDITIONS FOR A FULL FACTORIAL TWO LEVEL DESIGN TO TEST THREE HPLC FACTORS... 

In terms of absolute magnitude the main effects tend to be higher than two factor interactions which in turn are higher than three factor interactions and so on. At some point it is true to say that after a certain order interaction effects become negligible and can thus be disregarded in the experimental design. To do this full factorial designs are fractionated to allow the estimation of only a certain level of interaction effects. 

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