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About 10,000 papers for “Response surface methodology”

Response Surface Methodology

Vijayalakshmi Ghosh, Aachal Lonhare · 2024

An effective experimental design is a crucial element in the development of any product or process. It hinges on a comprehensive understanding of the system being studied. Response surface methodology (RSM) is a statistical approach, which can be employed for modeling and optimization of the processes that involve multiple variables. RSM helps to determine the ideal process conditions by merging experimental designs with interpolation, often employing first- or second-order polynomial equations in a systematic testing process. Conventional approaches that are designed to attain multi-factor experimental design typically entail significant time investment and encounter challenges in pinpointing the genuine optimal conditions, particularly when factor interactions are involved. RSM, however, has the capability to overcome these limitations. In the field of biological sciences, RSM is widely utilized for optimizing tasks such as preparing culture media, enhancing product formation, assessing their practical applications, and improving wastewater treatment processes. Additionally, RSM holds the great capacity to optimize both the components of growth media and various physicochemical parameters and represents a promising avenue for enhancing the production of both novel and existing bioactive metabolites (enzymes, antibiotics, probiotics, bioethanol, etc.) from various microbes. The utilization of the RSM methodology demands for quantitative information and proves highly effective in constructing predictive models and optimizing experimental approaches for substrates and energy utilization. RSM offers a means to systematically assess the influence of various parameters in the form of qualitative or quantitative data on a given process toward enabling the determination of optimal values of these process variables in order to enhance the desired outcome. RSM also finds application in optimization of microbial and polymeric adsorption process to eliminate heavy metal pollution. Formulation (nanoemulsion, liposome, etc.) and development of microbial products using secondary metabolites and essential oils can also be optimized by employing RSM. This chapter will present various cases of quantitative data analysis by RSM pertaining to optimization of process parameters such as culture media composition, substrate concentration, pH, and temperature for increased microbial biomass productions, which will culminate in enhanced production of bioethanol, enzymes, antibiotics, probiotics, etc. Furthermore, the chapter will deal with optimization of quantitative data by RSM in development of formulation using different metabolites and essential oil. This chapter will also deal with optimization of quantitative data related to microbial culture conditions for the applications such as waste water treatment and heavy metal sorption using RSM approach.

Cited by 53

Response Surface Methodology

David M. Steinberg, Ron S. Kenett · Wiley StatsRef: Statistics Reference Online · 2014

Abstract Response surface methodology is a general strategy for combining designed experiments and regression analysis to explore the relationship between one or more response variables and a set of factors that are thought to affect the responses. Some of the key elements in this strategy include sequential progress, a matching of experimental designs to the complexity of needed regression models and rapid progress to the most interesting parts of the factor space. These methods are often used to seek optimal settings of the factors for reaching specific values of the response, such as a maximum or minimum value. Response surface methods are widely used in research and development and industrial applications.

Cited by 13

Response Surface Methodology

Davoud Salar Bashi, Seyyed Ali Mortazavi, Karamatollah Rezaei et al. · 2016

Optimization of\tultrasound-assisted\textraction of phenolic\tcompounds\tfrom\tyarrow\t(Achillea beibrestinii)\t by\tresponse\tsurface\tmethodology

Cited by 0

Response Surface Methodology

J.P.C. Kleijnen · International series in management science/operations research/International series in operations research & management science · 2014

Cited by 66

response surface methodology

2024

Citation: 'response surface methodology' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10082 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

Cited by 0

Response Surface Methodology

Kathleen M. Carley, Natalia Y. Kamneva, Jeff Reminga · 2004

Abstract : There is a problem faced by experimenters in many technical fields, where, in general, the response variable of interest is y, and there is a set of predictor variables x1, x2,...xk. For example, in Dynamic Network Analysis (DNA) Response Surface Methodology (RSM) might be useful for sensitivity analysis of various DNA measures for different kinds of random groups and errors.

Cited by 197

Response surface methodology

André I. Khuri, Siuli Mukhopadhyay · Wiley Interdisciplinary Reviews Computational Statistics · 2010

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

Cited by 2,003

Response Surface Methodology

Pere Grima Cintas, Lluís Marco Almagro, Xavier Tort-Martorell Llabrés · 2012

This chapter provides steps and snapshots for creating response surface design in Minitab. It first describes matrix design creation and data collection. The chapter then explains analysis of response surface design. Finally, it discusses contour plots and response surface plots. Controlled Vocabulary Terms contour plot; data collection; response surface design; surface plot

Cited by 0

Response surface methodology

Esmaeil Zakipour Molkabadi, Zohreh Hamidi Esfahani, Mohammad Ali Sahari et al. · 2015

Article Information Tannase has several important applications in food, feed, chemical and pharmaceutical industries. In the present study, production of tannase by mutant strain, Penicillium sp. EZ-ZH390, was optimized in submerged fermentation utilizing two statistical approaches. At first step, a one factor at a time design was employed to screen the preferable nutriments (carbon and nitrogen sources of the medium) to produce tannase. Screening of the carbon source resulted in the production of 10.74 UmL-1 of tannase in 72 h in the presence of 14 % raspberry leaves powder. A 1.99-fold increase in tannase production was achieved upon further screening of the nitrogen source (in the presence of 1.2 % ammonium nitrate). Then the culture condition variables were studied by the response surface methodology using a central composite design. The results showed that temperature of 30°C rotation rate of 85 rpm and fermentation time 24 h led to increased tannase production. At these conditions, tannase activity reached to 21.77 UmL-1, and tannase productivity was at least 3.55 times (0.26 UmL-1h-1) in compare to those reported in the literature. The present study showed that, at the optimum conditions, Penicillium sp. EZ-ZH390 is an excellent strain for use in the efficient production of tannase. Article history:

Cited by 0

Response surface methodology

Alawad, Aziz · University of Southern Queensland ePrints (University of Southern Queensland) · 2013

There has been a great interest throughout the past years, in using the natural fibres to reinforcement the polymeric composites. A comprehensive study of the tribology science of the fibre composites is essential to ensure its capability to stand any given task. The main objective of the research is to obtain empirical equations for the temperature and friction of the studied Kenaf fibre. Minitab software was implemented in the research to conduct all the needed tests also to produce the figures. The key findings of this research are that, the frictional behavior of composites has a high frictional coefficient at the begging of the tribology test because of the high shear force in the contact zone, friction coefficient and temperature increases when the applied load is increased, friction coefficient decreases with the increment of the rotating speed and the high influence of rotating speed on temperature so that temperature increases when rotating speed increases, in other words temperature will increase 5 co for every 0.1 m/s increment in the rotating speed. [Author's abstract]

Cited by 0