A General Overview of Response Surface Methodology

Abstract

See also the article by Steinberg and Hunter [5]. RSM Books include1. Myers and Montgomery [6] 2. Khuri and Cornell [7] 3. Box and Draper [8] The present article provides a general overview of response surface methodology (RSM). Notation Response Surface MethodologyResponse surface methodology (RSM) is a collection of statistical and mathematical techniques used for the purpose of I. Setting up a series of experiments (design) for adequate predictions of a response y.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

“…It generates a mathematical model; its graphical perspective has led to the term Response Surface Methodology [20]. These graphic drawings of the shape of the surfaces allow a visual explanation of the functional relations between the response and the experimental variables [21] [22]. RSM also permits the location of the optimum conditions and sensitivity analyses of the optimum conditions to variations in the settings of the experimental variables.…”

Response Surface Modeling of Fuel Rich and Fuel Lean Catalytic Combustion of the Stabilized Confined Turbulent Gaseous Diffusion Flames

“…Response surface methodology (RSM) is an assembly of mathematical and statistical technique used for modeling and analyzing a process in which a response of interest is influenced by several variables and the aim is to optimize this response [10] [11] [12] [13]. This technique is one of the major quantitative tools in industrial decision making as it gives better understanding of the process; it helps the process engineer to see the effect of the control variables simultaneously and the interactions among all the variables [10].…”

“…It generates a mathematical model; its graphical perspective has led to the term Response Surface Methodology [14]. Graphic drawings of the shape of the surfaces allow a visual explanation of the functional relations between the response and the experimental variables [11] [12]. RSM also permits the location of the optimum conditions and sensitivity analyses of the optimum conditions to variations in the settings of the experimental variables.…”

A Practical Solution to the Small Sample Size Bias and Uncertainty Problems of Model Selection Criteria in Two-Input Process Multiple Response Surface Methodology Datasets

“…Khuri [3] single handedly wrote a full review of MRSM. Mukhopadhyay & Khuri [2] and Khuri [4] afforded sections of MRSM in their latest response surface methodology reviews.…”

“…Myers et al [5] emphasise that MRSM should always include canonical and response surface analysis before optimisation. Khuri [3] [4] clearly argues that traditional response surface techniques that apply to single response models, in general, are not adequate to analyse multiple response problems. Khuri [3] and Mukhopadhyay & Khuri [2] emphasize the use of multivariate statistical methods in every stage of MRSM processes so that the responses are considered simultaneously in every aspect, especially where correlation exists between the responses.…”