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Journal articles on the topic 'Response Surface Methodology'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Jin, J., X. Wang, Y. Han, Y. Cai, Y. Cai, H. Wang, L. Zhu, L. Xu, L. Zhao, and Z. Li. "Combined beef thawing using response surface methodology." Czech Journal of Food Sciences 34, No. 6 (December 21, 2016): 547–53. http://dx.doi.org/10.17221/138/2016-cjfs.

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Based on four thawing methods (still air, still water, ultrasonic wave, and microwave) and single-factor tests, we established a four-factor three-level response surface methodology for a regression model (four factors: pH, drip loss rate, cooking loss rate, protein content). The optimal combined thawing method for beef rib-eye is: microwave thawing (35 s work/10 s stop, totally 170 s) until beef surfaces soften, then air thawing at 15°C until the beef centre temperature reaches –8°C, and finally ultrasonic thawing at 220 W until the beef centre temperature rises to 0°C. With this method, the drip loss rate is 1.9003%, cooking loss rate is 33.3997%, and protein content is 229.603 μg, which are not significantly different from the model-predicted theoretical results (P ≥ 0.05).

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2

Shibata, Mario. "Response Surface Methodology." Nippon Shokuhin Kagaku Kogaku Kaishi 60, no. 12 (2013): 728–29. http://dx.doi.org/10.3136/nskkk.60.728.

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3

Myers, Raymond H., and Douglas C. Montgomery. "Response Surface Methodology." IIE Transactions 28, no. 12 (December 1996): 1031–32. http://dx.doi.org/10.1080/15458830.1996.11770760.

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4

Copeland, Karen A. F. "Response Surface Methodology." Journal of Quality Technology 28, no. 2 (April 1996): 262. http://dx.doi.org/10.1080/00224065.1996.11979672.

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5

Khuri, André I., and Siuli Mukhopadhyay. "Response surface methodology." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 2 (March 2010): 128–49. http://dx.doi.org/10.1002/wics.73.

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6

Dube, Vinitkumar Dilipkumar. "Optimization of Biodiesel (MOME) Using Response Surface Methodology (RSM)." International journal of Emerging Trends in Science and Technology 04, no. 11 (November 13, 2016): 4736–41. http://dx.doi.org/10.18535/ijetst/v3i11.02.

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7

Manuel, Jeremia, Raffi Paramawati, and Maria D. P. Masli. "UTILIZATION OF RESPONSE SURFACE METHODOLOGY IN THE OPTIMIZATION OF ROSELLE ICE CREAM MAKING [Penggunaan Response Surface Methodology dalam Optimisasi Pembuatan Es Krim Rosella]." Jurnal Teknologi dan Industri Pangan 25, no. 2 (December 2014): 125–33. http://dx.doi.org/10.6066/jtip.2014.25.2.125.

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8

Doti, Baqe, Daudi Nyaanga, Samwel Nyakach, Jane Nyaanga, and Oscar Ingasia. "Biochar production and quality optimization using response surface methodology technique." Applied Research Journal of Environmental Engineering 4, no. 1 (March 31, 2022): 1–16. http://dx.doi.org/10.47721/arjee20220401011.

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The dependency on fossil fuels can be reduced by the use of renewable energy sources like biomass and it can make a remarkable contribution to the reduction of CO2 emissions and as a result reducing the carbon footprint hence eliminating the greenhouse gas effect. Biomass materials that go to waste can be recovered through the pyrolysis process in order to produce biochar which can be used as a source of energy for cooking. The aim of this study was to carry out optimization of biochar production and quality using the Response Surface Methodology technique. The parameters varied were feedstock moisture content (FMC) (10%, 15% and 20%), pyrolysis residence time (PRT) (in minutes) 90, 135 and 180 and chimney inclination angle (CIA) (30o, 45o and 60o). An experimental insulated metallic carbonization kiln (1 m high and 0.5 m diameter) was developed and used. Response Surface Methodology technique by using Box-Behnken Design was used to develop a mathematical equation to predict the production and quality of the biochar with respect to varied parameters which was later optimized to determine the optimal conditions for biochar production and quality. The biochar quality was based on its moisture content (MC), volatile matter (VM), ash content (AC), fixed carbon (FC) and pH. The combined optimal conditions were 10% feedstock moisture content, 126.93 min pyrolysis residence time and 30o chimney inclination angle resulting to production of 44.35%, MC = 3.82%, VM = 23.52%, AC = 2.94%, FC = 67.89% and pH = 9.28. The mathematical equation developed had composite desirability (CD) of 0.9490 at a p-value≤0.05 which made it viable. These research findings are of importance since optimization reduces the wastage of resources resulting into increase in the efficiency of the pyrolysis system. Keywords: Renewable Energy, Pyrolysis, Biochar, Optimization, Response Surface Methodology

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9

Zhang, Q., Y. Lin, S. Shen, Z. Xing, and X. Ruan. "Simulation and Optimization on Cellulase Immobilization Using Response Surface Methodology." International Journal of Environmental Science and Development 6, no. 9 (2015): 664–67. http://dx.doi.org/10.7763/ijesd.2015.v6.677.

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10

Aruna, M. "Optimization of Parameters for Student Assessment Using Response Surface Methodology." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1492–97. http://dx.doi.org/10.5373/jardcs/v11sp10/20192994.

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11

M, Aruna, and Rashmi Rani. "Optimization of Parameters for Student Assessment Using Response Surface Methodology." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 540–45. http://dx.doi.org/10.5373/jardcs/v11sp11/20193064.

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12

Yumnam, S. "OPTIMIZATION OF TANNASE POSITIVE PROBIOTIC PRODUCTION BY SURFACE RESPONSE METHODOLOGY." Biotechnologia acta 7, no. 5 (2014): 62–70. http://dx.doi.org/10.15407/biotech7.05.062.

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13

Salimana, MAR, A. Zaidonb, ES Bakarb, SH Leeb, PM Tahira, NF Leemona, MF Kaipina, and AH Julianaa. "RESPONSE SURFACE METHODOLOGY MODEL OF." JOURNAL OF TROPICAL FOREST SECIENCE 29, no. 3 (July 31, 2017): 318–24. http://dx.doi.org/10.26525/jtfs2017.29.3.318324.

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14

Myers, Raymond H., André I. Khuri, Walter H. Carter, and Andre I. Khuri. "Response Surface Methodology: 1966-1988." Technometrics 31, no. 2 (May 1989): 137. http://dx.doi.org/10.2307/1268813.

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15

Steinberg, David M., and Dizza Bursztyn. "Response Surface Methodology in Biotechnology." Quality Engineering 22, no. 2 (March 5, 2010): 78–87. http://dx.doi.org/10.1080/08982110903510388.

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16

Myers, Raymond H., André I. Khuri, and Walter H. Carter. "Response Surface Methodology: 1966–l988." Technometrics 31, no. 2 (May 1989): 137–57. http://dx.doi.org/10.1080/00401706.1989.10488509.

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17

Park, Kwon Hyun, Min Soo Heu, and Jin-Soo Kim. "Development of Salted Semi-dried Common Gray Mullet Mugil cephalus using Response Surface Methodology." Korean Journal of Fisheries and Aquatic Sciences 48, no. 6 (December 31, 2015): 839–48. http://dx.doi.org/10.5657/kfas.2015.0839.

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18

PRAJINA N V, PRAJINA N. V., and T. D. JOHN T D JOHN. "Multi Response Optimization of Cutting Forces in End Milling Using Response Surface Methodology and Desirability Function." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 126–30. http://dx.doi.org/10.15373/22778179/may2013/45.

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19

Hussain, Sarfraz, Salim-ur Rehman, Qaisar Raza, Itrat Fatima, Syeda Mahvish Zahra, Farhat Rashid, and Ayesha Rafique. "Optimization of sensory properties of chemically preserved Mushrooms through response surface methodology." International Journal of Scientific Innovations 01, no. 01 (December 31, 2017): 006–14. http://dx.doi.org/10.32594/ijsi.2017.0102.

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20

Cynthia. S. J, Cynthia S. J., and John Don Bosco. S. "Process Optimization for Tamarindus Indica. L Pulp Extraction Using Response Surface Methodology." International Journal of Scientific Research 2, no. 4 (June 1, 2012): 183–85. http://dx.doi.org/10.15373/22778179/apr2013/64.

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21

He, Zhen, Jing Wang, Jinho Oh, and Sung H. Park. "Robust optimization for multiple responses using response surface methodology." Applied Stochastic Models in Business and Industry 26, no. 2 (March 2010): 157–71. http://dx.doi.org/10.1002/asmb.788.

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22

Fang, Sheng En, and Ricardo Perera. "Damage Identification Using Response Surface Methodology." Key Engineering Materials 413-414 (June 2009): 669–76. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.669.

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As a combination of statistical and mathematical techniques, response surface methodology gives explicit functions to express the relationship between the inputs and outputs of a physical system. This methodology has been widely applied to design optimization, response prediction and model validation but so far little literature related to its application in structural damage identification has been found. Therefore this paper presents a systematic damage identification procedure consisting of four steps of feature selection, parameter screening, primary response surface modeling and updating, reference-state response surface modeling with damage identification realization. 2k factorial design and central composite design are adopted to construct response surface models for parameter screening and model updating purposes, respectively. The proposed method is verified against an experimental reinforced concrete frame and it is found that the proposed method works well in damage prediction.

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23

Propst, Annabeth, John A. Cornell, and Douglas C. Crocker. "How to Apply Response Surface Methodology." Technometrics 34, no. 3 (August 1992): 359. http://dx.doi.org/10.2307/1270049.

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24

Myers, Raymond H. "Response surface methodology in quality improvement." Communications in Statistics - Theory and Methods 20, no. 2 (January 1991): 457–76. http://dx.doi.org/10.1080/03610929108830509.

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25

Huang, Yufen, and Chao-Yen Hsieh. "Influence analysis in response surface methodology." Journal of Statistical Planning and Inference 147 (April 2014): 188–203. http://dx.doi.org/10.1016/j.jspi.2013.11.008.

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26

Edwards, Don. "How to Apply Response Surface Methodology." Journal of Quality Technology 23, no. 4 (October 1991): 370–71. http://dx.doi.org/10.1080/00224065.1991.11979357.

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27

Atkinson, Anthony. "How to Apply Response Surface Methodology." Journal of Quality Technology 25, no. 1 (January 1993): 65–67. http://dx.doi.org/10.1080/00224065.1993.11979421.

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28

Yusa Ali, Dego, Purnama Darmadji, and Yudi Pranoto. "OPTIMASI NANOENKAPSULASI ASAP CAIR TEMPURUNG KELAPA DENGAN RESPONSE SURFACE METHODOLOGY DAN KARAKTERISASI NANOKAPSUL." Jurnal Teknologi dan Industri Pangan 25, no. 1 (June 2014): 23–30. http://dx.doi.org/10.6066/jtip.2014.25.1.23.

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29

Kumar, Dr N. Mahesh, and Mr P. Chinna Rao. "Optimization of EDM Process Parameters using Response Surface Methodology for AISI D3 Steel." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1651–56. http://dx.doi.org/10.31142/ijtsrd23535.

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30

Shim, Hee-Jin, Je-Min Lee, Min-Tae Kim, Kook-Jin Jang, and Jung-Kyu Kim. "OS4-8-5 Optimization of Brazed Joint Parameters Using Dual Response Surface Methodology." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS4–8–5–1—_OS4–8–5–5. http://dx.doi.org/10.1299/jsmeatem.2007.6._os4-8-5-1.

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31

P.B.Wagh, P. B. Wagh, Dr R. R. Deshmukh Dr. R.R.Deshmukh, and R. D. Gurav R.D.Gurav. "Mathematical Modeling and Process Parameters Optimization for Surface Roughness in Edm for En31 Material by Response Surface Methodology." Indian Journal of Applied Research 3, no. 10 (October 1, 2011): 1–3. http://dx.doi.org/10.15373/2249555x/oct2013/50.

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32

Wahyono, Agung, Elly Kurniawati, Kasutjianingati Kasutjianingati, Kang-Hyun Park, and Woo-Won Kang. "OPTIMASI PROSES PEMBUATAN TEPUNG LABU KUNING MENGGUNAKAN RESPONSE SURFACE METHODOLOGY UNTUK MENINGKATKAN AKTIVITAS ANTIOKSIDANNYA." Jurnal Teknologi dan Industri Pangan 29, no. 1 (June 2018): 29–38. http://dx.doi.org/10.6066/jtip.2018.29.1.29.

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33

Chung, Tsair-Wang, and Irwan Saleh Kurniawan. "Analysis of Antioxidant Property from Water Extraction of Garcia Mangostana Using Response Surface Methodology." International Journal of Chemical Engineering and Applications 14, no. 4 (December 2023): 41–44. http://dx.doi.org/10.18178/ijcea.2023.14.4.804.

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Mangosteen (Garcia mangostana) is an exotic fruit that can be found widely in Southeast Asia. Mangosteen pericarp contains bioactive compound that has pharmacological properties, including antioxidants, anticarcinogenic, and also suggested its applicability for skincare products. Water extraction is more applicable for industry due to simple process, low cost, and neutral reaction. In this study, water extraction on the pericarp of mangosteen was applied and the operating parameters were discussed by using Response Surface Methodology (RSM) for high recovery of antioxidant extract from the mangosteen pericarp. The experimental design used three factors, solid-to-liquid ratio (g/ml), temperature (oC) and extraction time (hour), were analyzed to discuss two responses, DPPH radical scavenging effect (DPPH) and Ferric Reducing Antioxidant Power (FRAP). Under the operating conditions, the highest FRAP is 0.818 abs at the factors of 1:10 (g/ml), 65 oC, and 3-hour. DPPH is significantly high for all RSM pattern. The optimum parameters determined by using RSM are at 1:10 (g/ml), 59.74 oC, and 2.87 hours with DPPH 81.01% and FRAP 0.789 abs.

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34

孙, 惠佳. "Orange Peel Enzyme by Response Surface Methodology." Hans Journal of Food and Nutrition Science 10, no. 01 (2021): 40–50. http://dx.doi.org/10.12677/hjfns.2021.101006.

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35

Lindstrom, Jan, Hanna Kokko, Esa Ranta, and Harto Linden. "Density Dependence and the Response Surface Methodology." Oikos 85, no. 1 (April 1999): 40. http://dx.doi.org/10.2307/3546790.

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36

Zhang, Pei Pei, and Zi Dong Yin. "A New Response Surface Methodology: XU-RSM." Advanced Materials Research 396-398 (November 2011): 126–29. http://dx.doi.org/10.4028/www.scientific.net/amr.396-398.126.

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This paper aims to develop a new Response Surface Methodology (RSM)— XU-RSM— adapted to Reliability-Based Design Optimization (RBDO) based on Diffuse Approximation (DA) and First-Order Reliability Method (FORM). The essential of the new response surface methodology is to use simultaneously the points both in a design space (X-space) and an independent standard normal random space (U-space). Mixing the points allows for more precise local approximation due to denser sampling points and leads to a faster convergence. Finally, a mathematic example is given to show how to use the new method and the comparison of results of evolutions.

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37

Jeang, Angus. "Optimal tolerance design by response surface methodology." International Journal of Production Research 37, no. 14 (September 1999): 3275–88. http://dx.doi.org/10.1080/002075499190284.

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38

Ch'ng, C. K., S. H. Quah, and H. C. Low. "The MM-Estimator in Response Surface Methodology." Quality Engineering 17, no. 4 (October 2005): 561–65. http://dx.doi.org/10.1080/08982110500225323.

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39

Farooq Anjum, M., Imran Tasadduq, and Khaled Al-Sultan. "Response surface methodology: A neural network approach." European Journal of Operational Research 101, no. 1 (August 1997): 65–73. http://dx.doi.org/10.1016/s0377-2217(96)00232-9.

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40

Vadde, K. K., V. R. Syrotiuk, and D. C. Montgomery. "Optimizing protocol interaction using response surface methodology." IEEE Transactions on Mobile Computing 5, no. 6 (June 2006): 627–39. http://dx.doi.org/10.1109/tmc.2006.82.

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41

Borror, Connie M. "Response Surface Methodology and Related Topics edited." Journal of Quality Technology 39, no. 3 (July 2007): 297–98. http://dx.doi.org/10.1080/00224065.2007.11917695.

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42

Mohammed, Bashar S., Veerendrakumar C. Khed, and Muhd Fadhil Nuruddin. "Rubbercrete mixture optimization using response surface methodology." Journal of Cleaner Production 171 (January 2018): 1605–21. http://dx.doi.org/10.1016/j.jclepro.2017.10.102.

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43

Mochi, Vanessa Trevizan, José Ricardo Pacheco, and Marco Aurelio Cremasco. "Response Surface Methodology Applied to Ozone Generation." Ozone: Science & Engineering 32, no. 5 (September 30, 2010): 372–78. http://dx.doi.org/10.1080/01919512.2010.508676.

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44

Ingelin, M. E., and O. M. Lukow. "Mixograph Absorption Determination by Response Surface Methodology." Cereal Chemistry Journal 76, no. 1 (January 1999): 9–15. http://dx.doi.org/10.1094/cchem.1999.76.1.9.

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45

Jeang, A. "Robust Tolerance Design by Response Surface Methodology." International Journal of Advanced Manufacturing Technology 15, no. 6 (June 14, 1999): 399–403. http://dx.doi.org/10.1007/s001700050082.

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46

Dijkstra, Albert. "Lies, damn lies and response surface methodology." European Journal of Lipid Science and Technology 112, no. 12 (December 2010): 1290–93. http://dx.doi.org/10.1002/ejlt.201000422.

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47

ÇİÇEK, Ekrem. "The Response Surface Methodology for Depth-Dependent Chondrocyte Surface Area." Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 14, no. 2 (August 31, 2021): 714–23. http://dx.doi.org/10.18185/erzifbed.827163.

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48

Nirosha, D., C. Sashidhar, and K. Narasimhulu. "Prediction of Compressive Strength of Silica Fume Blended High Strength Concrete Using Response Surface Methodology Approach." Indian Journal Of Science And Technology 17, no. 9 (February 27, 2024): 804–10. http://dx.doi.org/10.17485/ijst/v17i9.45.

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Objectives: In this study, a model was developed to predict the compressive strength of High Strength Concrete (HSC) mixed with silica fume using Response Surface Methodology (RSM). This study investigated the effects of cement, water, Silica Fume (SF), Coarse Aggregate (CA), and silica fume-cement ratio (SF/C) on the 28-day compressive strength of HSC. Silica fume is added with varying amounts of SF (5% to 25%) to cement content. Methods: Response surface methodology (RSM) was performed to investigate the influence of independent variables on the compressive strength of HSC. Findings: Analysis of the response surface plot reveals a remarkably low error percentage of less than 5%. This reveals a high degree of confidence (95%) in the model's accuracy. This study yielded a coefficient of determination (R2) of 0. 9968. It is observed negligible deviation between predicted and actual 28-day compressive strength values, indicating high model accuracy. Novelty: The predicted equation is reasonably predicting the compressive strength of high strength concrete. Keywords: High strength concrete, Response surface methodology, Silica fume, Compressive strength, Prediction model

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49

Storm, Scott M., Raymond R. Hill, and Joseph J. Pignatiello. "A Response Surface Methodology for Modeling Time Series Response Data." Quality and Reliability Engineering International 29, no. 5 (August 13, 2012): 771–78. http://dx.doi.org/10.1002/qre.1427.

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50

Khotchapong, Wiraya, and Niramol Punbusayakul. "Optimization of the infeed formula for spent coffee ground extract encapsulation using the response surface methodology." Journal on Processing and Energy in Agriculture 23, no. 3 (2019): 108–12. http://dx.doi.org/10.5937/jpea1903108k.

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