Response surface methodology

Abstract

The purpose of this article is to provide a survey of the various stages in the development of response surface methodology (RSM). The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951–1975, during which the so‐called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976–1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical Models > Linear Models Statistical Models > Nonlinear Models

Optimizing functions of coagulants in treatment of wastewater from metalworking fluids: Prediction by RSM method

“…Classical methods have some limitations, like being highly timeconsuming because of the experiments and the high cost of consumables that can be removed by statistical experimental designs. Furthermore, in this method, it is impossible to examine the interactions among variables (6,7). Response surface methodology (RSM) is a proper approach that also omits these limitations.…”

Precision of full polynomial response surface designs on models with missing coefficients

“…Following the notations of Khuri and Mukhopadhyay [8], the approximating polynomial model is of the form y = f ′ (x) β + ε…”

Predicting instrumental mass fractionation (IMF) of stable isotope SIMS analyses by response surface methodology (RSM) [Dataset]

“…15,16 Introduced by Box and Wilson, RSM has been applied to build up multivariate statistical models in a wide variety of industrial, engineering and experimental processes. 17 Successful RSM applications can be found in, e.g., Riley [18][19][20][21][22][23][24][25][26][27][28][29][30] In addition, the mathematical and statistical aspects of RSM and related experimental techniques are covered in e.g., Box, Box, and [31][32][33][34][35][36][37] Simultaneously, the statistical prediction models have been recognized as powerful tools for e.g., exploring the underlying causal relationships below the datasets, building and/or assessing new knowledge and improving previous models. 38 While explanatory statistical modelling is based on the causal relationships among previous theoretical constructions, the predictive statistical modelling works on associations of measurable variables.…”