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A class of composite designs for response surface methodology
Computational Statistics & Data Analysis, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stelios D. Georgiou +2 more
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Optimization of convective drying by response surface methodology
Computers and Electronics in Agriculture, 2019Abstract In this work, response surface methodology (RSM) is applied to state an optimized system for convective drying of apple slices using desirability function. The interaction of the independent parameters including air temperature (T = 70–90 °C), air velocity (V = 4–5 m/s), and apple slice geometry (G = circle, square, and triangle) with the ...
H. Majdi +2 more
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Stochastic programming methods in the response surface methodology
Computational Statistics & Data Analysis, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José A. Díaz-García +2 more
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Multiple Comparisons with a Control in Response Surface Methodology
Technometrics, 1993Quadratic response surface methodology often focuses on finding the levels of some (coded) predictor variables x = (x l, x 2, …, x k ) that optimize the expected value of a response variable y. Typically the experimenter starts from some best guess or “control” combination of the predictors (usually coded to x = 0) and performs an experiment varying ...
Ping Sa, Don Edwards
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Introduction to Response-Surface Methodology
2017Until now, we have considered how a dependent variable, yield, or response depends on specific levels of independent variables or factors. The factors could be categorical or numerical; however, we did note that they often differ in how the sum of squares for the factor is more usefully partitioned into orthogonal components.
Paul D. Berger +2 more
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A Response Surface Methodology for Modeling Time Series Response Data
Quality and Reliability Engineering International, 2012This article introduces extensions to classical response surface methods specifically for modeling and exploiting time series response data such as found in aircraft flight test. Classical response surface methods focus on the analysis of discrete response data. In some complex, nonlinear systems, analysts are concerned with the behavior of time series
Scott M. Storm +2 more
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Optimization of mead production using Response Surface Methodology
Food and Chemical Toxicology, 2013The main aim of the present work was to optimize mead production using Response Surface Methodology. The effects of temperature (x₁: 20-30°C) and nutrients concentration (x₂: 60-120g /hL) on mead quality, concerning the final concentrations of glucose (Y₁), fructose (Y₂), ethanol (Y₃), glycerol (Y₄) and acetic acid (Y₅), were studied.
Gomes, Teresa +6 more
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On the use of data transformation in response surface methodology
Quality and Reliability Engineering International, 2018AbstractOne of the main objectives of response surface methodology is to find the operating settings that optimize the mean function. When estimating the optimum settings, it is highly important to take the response variance into account. Data transformations are frequently used to eliminate variance heterogeneity.
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Response surface methodology for constrained simulation optimization: An overview
Simulation Modelling Practice and Theory, 2008Abstract This article summarizes ‘generalized response surface methodology’ (GRSM), extending Box and Wilson’s ‘response surface methodology’ (RSM). GRSM allows multiple random responses, selecting one response as goal and the other responses as constrained variables. Both GRSM and RSM estimate local gradients to search for the optimum.
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