Response Surface Methodology

Abstract

Approved for Public Release: Distribution Unlimited 17. OiSTAiSuTiON STATEMENT (of the ob.trocl -ntoted IA Block 20, It different 1-m. Roporl) If. SUPPLEMENTARY NOTES 19. KEY *ORDS (Comntin~ue onrvrse 00.... It necateottv aid Identity by block number) Response Surface, Review, Design, Analysis. 20 ABSTRACT (Contln..e an roers*9 old* It flec*esorYan id hentity by block number)This paper provides a thorough review of response surface methodology since 1966. The review covers topics pertaining to the design and analysis of response surfaces. Multirespone techniques associated with optimization and lack of fit testing are also covered.

Economic goals and requirements for competitive fusion energy

“…The traditional work-product, a conceptual point-design, may have to be demphasized in favor of a broader characterization of the 'design window', in the sense of Response Surface Methodologies (RSM) [46], extending available parametric efforts [47]. More emphasis will have to be placed on displaying the evolving uncertainty distribution, of which the traditional COE value is (only) the expected value.…”

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

“…More seriously, local SA is a one-factor-at-a-time approach, since the sensitivity of the i-th factor is obtained by assuming all other factors are fixed. This approach contradicts the fundamental principle of RSM, which aims at investigating the effect of both individual factors and their interactions [5].…”

“…These models are typically used in off-line design stage to facilitate the understanding and optimization of the process. Such a data-based model is the central component of the so-called response surface methodology (RSM) for rational process design [2,3,4,5].…”

“…The regression coefficients clearly indicate the sign and magnitude of impact of process factors and factor interactions. Furthermore, a powerful technique, analysis of variance (ANOVA), can be employed to identify whether the impact is statistically significant [5]. In addition, the scope of polynomials may be significantly expanded by applying transformation on the process factors prior to modeling.…”

Control of Superoxide Dismutase Activity during Malting Using Plackett-Burman and Box-Behnken Experimental Design and Its Effect on Reducing Power of Wort

“…Unless the model shows an adequate fit, proceeding with the investigation and optimization of the fitted response surface will likely give poor or misleading results. The residuals from the least squares fit played an important role in judging model adequacy 23 . Fig.…”