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The interest of most process engineers in industries is usually to optimize the yield of their processes. Not until 1951, imprecise methodologies were used in industries for this purpose. However, in 1951, G. E. P. Box and K. B. Wilson invented the technique of Response Surface Methodology (RSM) as one used for the optimization of the yield of processes. Being an initial idea, this paper has considered RSM as a foundational idea. In particular, it criticizes this foundational idea from the angle of its intuitive approach to searching for near-optimal settings of industrial processes, should such processes fail to run at optimal settings. RSM uses the tools of canonical transformation and analysis (a trial-and-error routine) for this search. Regardless, the foundational response surface methodology is acknowledged to be primarily efficient for determining the optimum response.
Box GEP, Wilson KB. Response surface methodology. Journal of Storage. 1951;3(5):256-263.
Bradley DN. The response surface methodology (Doctoral Dissertation). Indiana University of South Bend; 2007.
Cook H. Optimizing chip multiprocessor designs using response genetically programmed response surfaces (M.Sc. Thesis). University of Virginia; 2007.
Carley KM, Kamneva NY, Reminya J. Response surface methodology. CASOS Technical Report CMU-ISRI-04-136. USA: Carnagie Mellon University, School of Computer Science. 2004;23-45.
Cox DR. Response surface methodology: Planning of experiments. New York: John Wiley & Sons. 1992;329.
Cochran WG, Cox GM. Response surface methodology. New York: John Wiley & Sons. 1992;335-375.
Cox DR, Reid N. Response surface methodology: The theory of the designs of experiments. Florida: Chapman & Hall/CRS. 1994;148-293.
Raissi S, Farsani RE. Statistical process optimization through multi-response surface methodology. World Academic Journal Science, Engineering and Technology. 2009;51(46):267-271.
Draper NR, Lin DKJ, Ghosh S, Rao CR. Response surface designs. Handbook of Statistics. London: Elsevier Science. 1996;343-374.
Khuri IA. A general overview of response surface methodology. Biom Biostat Int. J. 2017;5(3):87-93.
Wu CFJ, Yuan D. Construction of response surface designs for qualitative and quantitative factors. IIQP Response Report RR-91-03; 1998.
(Accessed 18 December 2012)
Khuri IA. Response surface methodology and related topics: Designs. New York: World Scientific Publishers. 2011;159.
Khuri IA, Mukhopahyay S. Response surface methodology. Wiley Interdisciplinary Research (Computational Statistics). 2010;2(2):128-149.
Bei-xing L, Wei-chang W, Xian-peng Z, Da-xia Z, Wei M, Feng L. Integrating uniform design and response surface methodology to optimize thiacloprid suspension. Sci Rep. 2017;7:46018.
Buyske S, Trout R. Lecture 6: Response surface methodology II; 2011a.
(Accessed on 9 November 2012)
Tharima AF, Rahman MM, Yusoff MZ. Application of response surface methodology for optimizing evacuation time in enclosed car park. IOP Conference Series: Mater. Sci. Eng. 2017;257.
Buyske S, Trout R. Lecture 8: Response surface methodology IV; 2011b.
(Accessed on 9 November 2012)
Garlapati VK, Roy L. Utilization of response surface methodology for modeling and optimization of tablet compression process. J Young Pharm. 2017;9(3):417-421.
Kharul AMS, Mohamed AMA. Overview on the response surface methodology (RSM) in extraction processes. Journal of Applied Science and Process Engineering. 2015;2(1):8-17.
Kathleen SR, Mathius JA, Wuham AS, James WJ. Response surface methodology. Carnegie Association of Science for Operation Research; 2003.
(Accessed on 3 July 2012)
Osei YA. Haber process. New school chemistry for senior secondary schools. Onitsha: Africana-Feb Publishers. 2011;23-59.
Akpan SS, Ugbe TA, Usen JE. An alternative procedure for solving second order response surface design problems. International Journal of Scientific and Engineering Research. 2013;4(9):2233-2245.