In statistics, a central composite design is an experimental design, useful in response surface methodology, for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment.
Common values are as follows (Myers, 1971): Statistical approaches such as Response Surface Methodology can be employed to maximize the production of a special substance by optimization of operational factors.
In contrast to conventional methods, the interaction among process variables can be determined by statistical techniques.
For instance, in a study, a central composite design was employed to investigate the effect of critical parameters of organosolv pretreatment of rice straw including temperature, time, and ethanol concentration.
The residual solid, lignin recovery, and hydrogen yield were selected as the response variables.