1999
DOI: 10.13031/2013.13267
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Full Factorial Design Versus Central Composite Design: Statistical Comparison and Implications for Spray Droplet Deposition Experiments

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Cited by 6 publications
(5 citation statements)
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“…Even though FFD requires a greater number of experimental runs compared to central composite design, FFD encompasses all possible interactions between the factors/ variables, whereas some interactions may not be explicitly examined in the latter design. 29 Factorial design is an efficient method for characterizing processes with multiple variables and allows for the separation of significant components from those that are not, as well as the identification of any potential interactions between them. 30 Analyses show that the concentration of THC is >0.3% while that of CBD is <0.3% mass mass −1 and can be considered as a drug-type chemovar.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Even though FFD requires a greater number of experimental runs compared to central composite design, FFD encompasses all possible interactions between the factors/ variables, whereas some interactions may not be explicitly examined in the latter design. 29 Factorial design is an efficient method for characterizing processes with multiple variables and allows for the separation of significant components from those that are not, as well as the identification of any potential interactions between them. 30 Analyses show that the concentration of THC is >0.3% while that of CBD is <0.3% mass mass −1 and can be considered as a drug-type chemovar.…”
Section: Discussionmentioning
confidence: 99%
“…FFD was used for the determination of optimal particle size, type of solvent, and extraction temperature for cannabis biomass. Even though FFD requires a greater number of experimental runs compared to central composite design, FFD encompasses all possible interactions between the factors/variables, whereas some interactions may not be explicitly examined in the latter design . Factorial design is an efficient method for characterizing processes with multiple variables and allows for the separation of significant components from those that are not, as well as the identification of any potential interactions between them .…”
Section: Discussionmentioning
confidence: 99%
“…A uniform precision rotatable central composite design (CCD) with three factors was used (Myers, 1971; Panneton et al, 1999). The three factors or independent variables were airflow rate, airspeed, and air jet angle.…”
Section: Methodsmentioning
confidence: 99%
“…This domain included the 2 by 2 by 2 cube and is referred to the experimental domain later in this paper . The associated variance of the predicted response drastically increases outside this domain (Panneton et al, 1999). The independent variable increment used was 0.004 m 3 s −1 m −1 for airflow, 0.12 m s −1 for airspeed, and 0.2523° for air jet angle.…”
Section: Methodsmentioning
confidence: 99%
“…The optimization approach is conducted using a combination of response surface methodology (RSM) and multilevel factorial design (MLFD) to obtain the optimized reaction parameters, which can be implemented in industrial practice. Among many statistical and mathematical approaches, MLFD is selected because it (1) incorporates all interactions of the three variables at all levels, and (2) offers more flexibility in assessing these interactions when the number of degrees of freedom is sufficient [27]. Moreover, the use of the factorial design also increases the statistical sensitivity and generalizability without decreasing precision [28]; therefore, it is superior compared to the other methods.…”
Section: Introductionmentioning
confidence: 99%