Small Robust Response-Surface Designs for Quantitative and Qualitative Factors

Aggarwal, M. L.; Gupta, Bhisham C.; Bansal, Anita

doi:10.1080/01966324.2000.10737503

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…(30) were treated as random. Aggarwal and Bansal91 and Aggarwal et al 92 considered robust parameter designs involving both quantitative and qualitative factors. Brenneman and Myers93 considered the single model in the control and noise variables to model the response.…”

Section: Part II Further Developments and The Taguchi Era: 1976–1999mentioning

confidence: 99%

Response surface methodology

Khuri

Mukhopadhyay

2010

WIREs Computational Stats

1,359

568

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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

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Section: Part II Further Developments and The Taguchi Era: 1976–1999mentioning

confidence: 99%

Response surface methodology

Khuri

Mukhopadhyay

2010

WIREs Computational Stats

1,359

568

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“…Examining the effects of quantitative, as well as qualitative variables on a response variable is routinely employed for analyzing data in disciplines such as science, social science and business [32,33]. Developing response models using qualitative and quantitative factors have been reported in several studies [34][35][36]; however, the technique has not been applied extensively in science and engineering applications.…”

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confidence: 99%

Photocatalytic Degradation of Phenol and Phenol Derivatives Using a Nano-TiO2 Catalyst: Integrating Quantitative and Qualitative Factors Using Response Surface Methodology

Choquette-Labbé

Shewa

Lalman

et al. 2014

Water

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Due to the toxicity effects and endocrine disrupting properties of phenolic compounds, their removal from water and wastewater has gained widespread global attention. In this study, the photocatalytic degradation of phenolic compounds in the presence of titanium dioxide (TiO 2 ) nano-particles and UV light was investigated. A full factorial design consisting of three factors at three levels was used to examine the effect of particle size, temperature and reactant type on the apparent degradation rate constant. The individual effect of TiO 2 particle size (5, 10 and 32 nm), temperature (23, 30 and 37 °C) and reactant type (phenol, o-cresol and m-cresol) on the apparent degradation rate constant was determined. A regression model was developed to relate the apparent degradation constant to the various factors. The largest photocatalytic activity was observed at an optimum TiO 2 particle size of 10 nm for all reactants. The apparent degradation rate constant trend was as follows: o-cresol > m-cresol > phenol. The ANOVA data indicated no significant interaction between the experimental factors. The lowest activation energy was observed for o-cresol degradation using 5-nm TiO 2 particles. A maximum degradation rate constant of 0.0138 minwas recorded for o-cresol at 37 °C and a TiO 2 particle size of 13 nm at a D-optimality value of approximately 0.98. The response model adequately related the apparent degradation rate constant to the factors within the range of factors under consideration.

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“…The designs constructed support a second-order model involving the control factors, a linear model involving the noise factors and two-factor interactions involving the qualitative factor. Aggarwal 75 extended the work of Aggarwal and Bansal 73 by constructing small robust designs involving both quantitative and qualitative factors.…”

Section: Rpd Involving Quantitative and Qualitative Factorsmentioning

confidence: 97%

Robust Parameter Design: A Review

Robinson

Borror

Myers

2003

Quality & Reliability Eng

185

106

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Parameter design is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The assumption is that there are both controllable factors (control variables) and uncontrollable/difficult to control factors (noise variables) that operate on the quality characteristic of a process. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic while minimizing the variation imposed on the process via the noise variables. Parameter design was popularized in the mid 1980s by Japanese quality consultant Genichi Taguchi. A panel discussion edited by Nair summarized important responses to Taguchi's ideas and methodology. In the last decade, there have been many applications and new developments in this important area. This review paper focuses largely on the work done since 1992, but a historical perspective of parameter design is also given.

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Small Robust Response-Surface Designs for Quantitative and Qualitative Factors

Cited by 6 publications

References 20 publications

Response surface methodology

Response surface methodology

Photocatalytic Degradation of Phenol and Phenol Derivatives Using a Nano-TiO2 Catalyst: Integrating Quantitative and Qualitative Factors Using Response Surface Methodology

Robust Parameter Design: A Review

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