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Optimization of saponification process in multi-response framework by using desirability function approach

Year 2015, Volume: 19 Issue: 2, 141 - 149, 01.08.2015
https://doi.org/10.16984/saufenbilder.44436

Abstract

In chemical engineering field, there are many processes which need to optimize more than one responses, called multiresponse, simultaneously. In this study, it is aimed to analyse the effects of operating parameters (modeling) and to obtain the compromise process factor values (optimization) for a continuous saponification process. The novelty of this study is considering the saponification process as a multi-response problem. It is important both engineering and statistical aspects. For the continuous saponification process, sodium hydroxide (X1), ethyl acetate concentrations (X2), and their volumetric flow rates (X3, X4) were regarded as the process factors in order to maximize the conversion of sodium hydroxide (Y1) and to minimize the space time (Y2) which is calculated analytically by using X3 and X4Response Surface Methodology (RSM) and Desirability Function Approach (DFA) were used for modeling and optimization of the process, respectively. Therefore, it is clear that compromise factor conditions which are obtained by the optimization of conversion and space time simultaneously will satisfy the product quality and process economy.

References

  • Simandi, B., Sawinsky, J. And Molnar, K. (1996) ‘Analysis at a mixing model and its application to a multistate column reactor’, Chemical and Biochemical Engineering Quarterly, vol. 10, no. 3, pp. 129-136.
  • Heny, C., Simanca, D. and Delgado, M. (2000) ‘Pseudo-bond graph model and simulation of a continuous stirred tank reactor’, Journal of the Franklin Institute, vol. 337, no. 1, pp. 21-42.
  • Krupska, A., Konarski, J., Fiedorow, R. and Adamiec, J. (2002) ‘Determination of the rate constants from phase delay effect in chemical reactions’, Kinetics and Catalysis, vol. 43, no. 3, pp. 295-302.
  • Mendes, A.M., Madeira, L.M., Magalhaes, F.D. and Sousa, J.M. (2004) ‘An integrated chemical engineering Lab Experiment’, Chemical Engineering Education, vol. 38, no. 3, pp. 228-235.
  • Bezerra, M.A., Santelli, R.E., Oliveiraa, E.P., Villara, L.S. and Escaleiraa, L.A. (2008) ‘Response surface methodology (RSM) as a tool for optimization in analytical chemistry’, Talanta, vol. 76, pp. 965-977.
  • Chi, G., Hu, S., Yang, Y. and Chen, T. (2012) ‘Response surface methodology with prediction uncertainty: A multi-objective optimization approach’, Chemical Engineering Research and Design, vol. 90, pp. 1235-1244.
  • Istadi, A.N. (2005) ‘A hybrid numerical approach for multi-responses optimization of process parameters and catalyst compositions in CO2 OCM process over CaO-MnO/CeO2 catalyst’, Chemical Engineering Journal, vol. 106, pp. 213-227.
  • Seritan, M.P., Gutt, S., Gutt, G., Cretescu, I., Cojocaru, C. and Severin, T. (2011) ‘Design of experiments for statistical modeling and multi-response optimization of nickel electroplating process’, Chemical Engineering Research and Design, vol. 89, pp. 136-147.
  • Salimon, J., Abdullah, B.M. and Salih, N. (2012) ‘Saponification of Jatropha curcas Seed Oil: Optimization by D-Optimal Design’, Hindawi Publishing Corporation International Journal of Chemical Engineering, doi:10.1155/2012/574780.
  • Bursali, N., Ertunc, S. and Akay, B. (2006) ‘Process improvement approach to the saponification reaction by using statistical experimental design’, Chemical Engineering and Processing, vol. 45, pp. 980–989.
  • Khuri, A. and Mukhopadhyay, S. (2010) ‘Response surface methodology’, WIREs Computational Statistics, vol. 2, pp. 128-149.
  • Khuri, A.I. and Cornell, M. (1996) Response Surfaces, Marcel Dekker, New-York.
  • Myers, R.H. and Montgomery, D.C. (2002) Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd Ed., John Wiley and Sons, New York.
  • Box, G.E.P. and Draper, N.R. (2007) Response Surface Mixtures and Ridge Analysis, John Wiley and Sons, New Jersey.
  • Zellner, A. (1962) ‘An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias’, American Statistical Association Journal, vol. 57, pp. 348–368.
  • Lind, E.E., Goldin, J. and Hickman, J.B. (1960) ‘Fitting yield and cost response surface’, Chemical Engineering Progress, vol. 56, pp. 62-68.
  • Harrington, E.C. (1965) ‘The desirability function’, Industrial Quality Control, vol. 21, no. 10, pp. 494-498.
  • Derringer, G. and Suich, R. (1980) ‘Simultaneous optimization of several response variables’, Journal of Quality Technology, vol. 12, pp. 214-219.
  • Del Castillo, E., Montgomery, D.C. and McCarville, D.R. (1996) ‘Modified Desirability functions for Multiple Response Optimization’, Journal of Quality Technology, vol. 28, no. 3, pp. 337-345.
  • Kim, K.J. and Lin, D.K.J. (2000) ‘Simultaneous optimization mechanical properties of steel by maximizing exponential desirability functions’, Applied Statistics, vol. 49, no. 3, pp. 311-325.
  • Köksoy, O. (2005) ‘Dual response optimization: The desirability approach’, International Journal of Industrial Engineering, vol. 12, no. 4, pp.335-342.
  • Khuri, A.I. and Conlon, M. (1981) ‘Simultaneous Optimization of Multiple Responses Represented by Polinomial Regression Functions’, Technometrics, vol. 23, pp. 363-375.
  • Pignatiello, J.J. (1993) ‘Strategies for Robust Multiresponse Quality Engineering’, IIE Transactions, vol. 25, pp. 5-15.
  • Vining, G. (1998) ‘A compromise approach to multiresponse optimization’, Journal of Quality Technology, vol. 30, no. 4, pp. 309-314.
  • Ames, A.E., Mattucci, N., Macdonald, S., Szonyi, G. and Hawkins, D.M. (1997) ‘Quality Loss Function for Optimization Across Multiple Response Surface’, Journal of Quality Technology, vol. 29, pp. 339-346.
  • Ko, Y.H., Kim, K.J. and Jun, C.H. (2005) ‘A New Loss Function-Based Method for Multiresponse Optimization’, Journal of Quality Technology, vol. 37, no. 1, pp.50-59.
  • Derringer, G. (1994) ‘A balancing act: optimizing a product’s properties’, Qual. Prog., pp. 51-58.
  • http://www.che.boun.edu.tr/courses/che302/Chapter%2010.pdf.
  • Minitab Release 14 for Windows, Minitab Inc., USA.
  • Matlab, The Language of Technical Computing, http://www.mathworks.com.

İstenebilirlik Fonksiyonu Yaklaşımı Kullanılarak Çok Yanıtlı Çerçevede Sabunlaşma Sürecinin Optimizasyonu

Year 2015, Volume: 19 Issue: 2, 141 - 149, 01.08.2015
https://doi.org/10.16984/saufenbilder.44436

Abstract

Kimya mühendisliği alanında, çok yanıtlı problem olarak adlandırılan, birden fazla yanıtın eşanlı optimizasyonunu gerektiren pek çok süreç mevcuttur. Bu çalışmada, bir sürekli sabunlaşma süreci için süreç parametrelerinin etkilerinin analizi (modelleme) ve uzlaşık süreç parametre değerlerinin elde edilmesi (optimizasyon) amaçlanmıştır. Bu çalışmanın özgünlüğü, sabunlaşma sürecinin çok yanıtlı bir problem olarak ele alınmasıdır. Bu, mühendislik ve istatistiksel yönden önemlidir. Sürekli sabunlaşma süreci için, sodyum hidroksit (X1), etil asetat derişimleri (X2) ve onların hacimsel akış hızları (X3, X4), sodyum hidroksit dönüşümünü (Y1) maksimum ve işletme süresini (Y2) minimum yapmak amacıyla süreç faktörleri olarak ele alınmıştır. Burada, Y2 değişkeni, X3 ve X4 değişkenlerini kullanarak analitik olarak hesaplanmıştır. Yanıt Yüzey Yöntemi (YYY) ve İstenebilirlik Fonksiyonu Yaklaşımı (İFY), sırasıyla sürecin modellenmesi ve optimizasyonu için kullanılmıştır. Böylece, dönüşüm ve işletme süresi yanıtlarının eşanlı optimizasyonu ile elde edilen uzlaşık faktör koşullarının, üretim kalitesini ve süreç ekonomisini sağlayacağı açıktır.

References

  • Simandi, B., Sawinsky, J. And Molnar, K. (1996) ‘Analysis at a mixing model and its application to a multistate column reactor’, Chemical and Biochemical Engineering Quarterly, vol. 10, no. 3, pp. 129-136.
  • Heny, C., Simanca, D. and Delgado, M. (2000) ‘Pseudo-bond graph model and simulation of a continuous stirred tank reactor’, Journal of the Franklin Institute, vol. 337, no. 1, pp. 21-42.
  • Krupska, A., Konarski, J., Fiedorow, R. and Adamiec, J. (2002) ‘Determination of the rate constants from phase delay effect in chemical reactions’, Kinetics and Catalysis, vol. 43, no. 3, pp. 295-302.
  • Mendes, A.M., Madeira, L.M., Magalhaes, F.D. and Sousa, J.M. (2004) ‘An integrated chemical engineering Lab Experiment’, Chemical Engineering Education, vol. 38, no. 3, pp. 228-235.
  • Bezerra, M.A., Santelli, R.E., Oliveiraa, E.P., Villara, L.S. and Escaleiraa, L.A. (2008) ‘Response surface methodology (RSM) as a tool for optimization in analytical chemistry’, Talanta, vol. 76, pp. 965-977.
  • Chi, G., Hu, S., Yang, Y. and Chen, T. (2012) ‘Response surface methodology with prediction uncertainty: A multi-objective optimization approach’, Chemical Engineering Research and Design, vol. 90, pp. 1235-1244.
  • Istadi, A.N. (2005) ‘A hybrid numerical approach for multi-responses optimization of process parameters and catalyst compositions in CO2 OCM process over CaO-MnO/CeO2 catalyst’, Chemical Engineering Journal, vol. 106, pp. 213-227.
  • Seritan, M.P., Gutt, S., Gutt, G., Cretescu, I., Cojocaru, C. and Severin, T. (2011) ‘Design of experiments for statistical modeling and multi-response optimization of nickel electroplating process’, Chemical Engineering Research and Design, vol. 89, pp. 136-147.
  • Salimon, J., Abdullah, B.M. and Salih, N. (2012) ‘Saponification of Jatropha curcas Seed Oil: Optimization by D-Optimal Design’, Hindawi Publishing Corporation International Journal of Chemical Engineering, doi:10.1155/2012/574780.
  • Bursali, N., Ertunc, S. and Akay, B. (2006) ‘Process improvement approach to the saponification reaction by using statistical experimental design’, Chemical Engineering and Processing, vol. 45, pp. 980–989.
  • Khuri, A. and Mukhopadhyay, S. (2010) ‘Response surface methodology’, WIREs Computational Statistics, vol. 2, pp. 128-149.
  • Khuri, A.I. and Cornell, M. (1996) Response Surfaces, Marcel Dekker, New-York.
  • Myers, R.H. and Montgomery, D.C. (2002) Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd Ed., John Wiley and Sons, New York.
  • Box, G.E.P. and Draper, N.R. (2007) Response Surface Mixtures and Ridge Analysis, John Wiley and Sons, New Jersey.
  • Zellner, A. (1962) ‘An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias’, American Statistical Association Journal, vol. 57, pp. 348–368.
  • Lind, E.E., Goldin, J. and Hickman, J.B. (1960) ‘Fitting yield and cost response surface’, Chemical Engineering Progress, vol. 56, pp. 62-68.
  • Harrington, E.C. (1965) ‘The desirability function’, Industrial Quality Control, vol. 21, no. 10, pp. 494-498.
  • Derringer, G. and Suich, R. (1980) ‘Simultaneous optimization of several response variables’, Journal of Quality Technology, vol. 12, pp. 214-219.
  • Del Castillo, E., Montgomery, D.C. and McCarville, D.R. (1996) ‘Modified Desirability functions for Multiple Response Optimization’, Journal of Quality Technology, vol. 28, no. 3, pp. 337-345.
  • Kim, K.J. and Lin, D.K.J. (2000) ‘Simultaneous optimization mechanical properties of steel by maximizing exponential desirability functions’, Applied Statistics, vol. 49, no. 3, pp. 311-325.
  • Köksoy, O. (2005) ‘Dual response optimization: The desirability approach’, International Journal of Industrial Engineering, vol. 12, no. 4, pp.335-342.
  • Khuri, A.I. and Conlon, M. (1981) ‘Simultaneous Optimization of Multiple Responses Represented by Polinomial Regression Functions’, Technometrics, vol. 23, pp. 363-375.
  • Pignatiello, J.J. (1993) ‘Strategies for Robust Multiresponse Quality Engineering’, IIE Transactions, vol. 25, pp. 5-15.
  • Vining, G. (1998) ‘A compromise approach to multiresponse optimization’, Journal of Quality Technology, vol. 30, no. 4, pp. 309-314.
  • Ames, A.E., Mattucci, N., Macdonald, S., Szonyi, G. and Hawkins, D.M. (1997) ‘Quality Loss Function for Optimization Across Multiple Response Surface’, Journal of Quality Technology, vol. 29, pp. 339-346.
  • Ko, Y.H., Kim, K.J. and Jun, C.H. (2005) ‘A New Loss Function-Based Method for Multiresponse Optimization’, Journal of Quality Technology, vol. 37, no. 1, pp.50-59.
  • Derringer, G. (1994) ‘A balancing act: optimizing a product’s properties’, Qual. Prog., pp. 51-58.
  • http://www.che.boun.edu.tr/courses/che302/Chapter%2010.pdf.
  • Minitab Release 14 for Windows, Minitab Inc., USA.
  • Matlab, The Language of Technical Computing, http://www.mathworks.com.
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Özlem Türkşen

Suna Ertunç

Publication Date August 1, 2015
Submission Date April 8, 2014
Acceptance Date July 23, 2015
Published in Issue Year 2015 Volume: 19 Issue: 2

Cite

APA Türkşen, Ö., & Ertunç, S. (2015). Optimization of saponification process in multi-response framework by using desirability function approach. Sakarya University Journal of Science, 19(2), 141-149. https://doi.org/10.16984/saufenbilder.44436
AMA Türkşen Ö, Ertunç S. Optimization of saponification process in multi-response framework by using desirability function approach. SAUJS. July 2015;19(2):141-149. doi:10.16984/saufenbilder.44436
Chicago Türkşen, Özlem, and Suna Ertunç. “Optimization of Saponification Process in Multi-Response Framework by Using Desirability Function Approach”. Sakarya University Journal of Science 19, no. 2 (July 2015): 141-49. https://doi.org/10.16984/saufenbilder.44436.
EndNote Türkşen Ö, Ertunç S (July 1, 2015) Optimization of saponification process in multi-response framework by using desirability function approach. Sakarya University Journal of Science 19 2 141–149.
IEEE Ö. Türkşen and S. Ertunç, “Optimization of saponification process in multi-response framework by using desirability function approach”, SAUJS, vol. 19, no. 2, pp. 141–149, 2015, doi: 10.16984/saufenbilder.44436.
ISNAD Türkşen, Özlem - Ertunç, Suna. “Optimization of Saponification Process in Multi-Response Framework by Using Desirability Function Approach”. Sakarya University Journal of Science 19/2 (July 2015), 141-149. https://doi.org/10.16984/saufenbilder.44436.
JAMA Türkşen Ö, Ertunç S. Optimization of saponification process in multi-response framework by using desirability function approach. SAUJS. 2015;19:141–149.
MLA Türkşen, Özlem and Suna Ertunç. “Optimization of Saponification Process in Multi-Response Framework by Using Desirability Function Approach”. Sakarya University Journal of Science, vol. 19, no. 2, 2015, pp. 141-9, doi:10.16984/saufenbilder.44436.
Vancouver Türkşen Ö, Ertunç S. Optimization of saponification process in multi-response framework by using desirability function approach. SAUJS. 2015;19(2):141-9.

Sakarya University Journal of Science (SAUJS)