Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Design Central Composto Assistido por Otimização× | Metodologia de Superfície de Resposta (RSM)× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família≠ | Process / pipeline | Hypothesis test |
| Ano de origem≠ | 1951 (CCD); optimization coupling formalized 1970s–1990s | 1951 |
| Autor original≠ | Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues | George E. P. Box & K. B. Wilson |
| Tipo≠ | Experimental design with mathematical optimization | Second-order polynomial response surface model |
| Fonte seminal≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Outros nomes≠ | CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Relacionados≠ | 3 | 7 |
| Resumo≠ | Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateConjunto de dados ↗ |
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