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

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.

İstenebilirlik fonksiyonu yaklaşımı kullanılarak çok yanıtlı çerçevede sabunlaşma sürecinin optimizasyonu

In chemical engineering field, there are many processes which need to optimize more than one responses, called multi- response, 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 X4. Response 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

PDF

___

[1] B. Simandi, J. Sawinsky ve K. Molnar 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, 1996 [2] C. Heny, D. Simanca ve M. Delgado, Pseudo- bond graph model and simulation of a continuous stirred tank reactor, Journal of the Franklin Institute, vol. 337, no. 1, pp. 21-42, 2000 [3] A. Krupska, J. Konarski, R. Fiedorow and J. Adamiec Determination of the rate constants from phase delay effect in chemical reactions, Kinetics and Catalysis, vol. 43, no. 3, pp. 295- 302, 2002 [4] A.M. Mendes, L.M. Madeira, F.D. Magalhaes, and J.M. Sousa An integrated chemical engineering Lab Experiment, Chemical Engineering Education, vol. 38, no. 3, pp. 228- 235, 2004 [5] M.A. Bezerra, R.E. Santelli, E.P. Oliveiraa, L.S. Villara and L.A. Escaleiraa, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta, vol. 76, pp. 965- 977, 2009 [6] G. Chi, S. Hu, Y. Yang and T. Chen Response surface methodology with prediction uncertainty: A multi-objective optimization approach, Chemical Engineering Research and Design, vol. 90, pp. 1235-1244, 2012 [7] A.N. Istadi 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, 2005 [8] M.P. Seritan, S. Gutt, G. Gutt, I. Cretescu, C. Cojocaru and T. Severin Design of experiments for statistical modeling and multi-response optimization of nickel electroplating process, Chemical Engineering Research and Design, vol. 89, pp. 136-147, 2011 [9] J. Salimon, B.M. Abdullah and N. Salih Saponification of Jatropha curcas Seed Oil: Optimization by D-Optimal Design, Hindawi Publishing Corporation International Journal of Chemical Engineering, doi: 10.1155/2012/574780, 2012 [10] N. Bursali, S. Ertunc and B. Akay Process improvement approach to the saponification reaction by using statistical experimental design, Chemical Engineering and Processing, vol. 45, pp. 980989, 2006 [11] A. Khuri and S. Mukhopadhyay, Response surface methodology, WIREs Computational Statistics, vol. 2, pp. 128-149, 2010 [12] A.I. Khuri and M. Cornell Response Surfaces, Marcel Dekker, New-York, 1996 [13] R.H. Myers and D.C. Montgomery, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 2nd Ed., John Wiley and Sons, New York, 2002 [14] G.E.P. Box and N.R. Draper, Response Surface Mixtures and Ridge Analysis, John Wiley and Sons, New Jersey, 2007 [15] A. Zellner, An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias, American Statistical Association Journal, vol. 57, pp. 348368, 1962 [16] E.E. Lind, J. Goldin and J.B. Hickman, Fitting yield and cost response surface, Chemical Engineering Progress, vol. 56, pp. 62-68, 1960 [17] E.C. Harrington, The desirability function, Industrial Quality Control, vol. 21, no. 10, pp. 494-498, 1965 [18] G. Derringer and R. Suich, Simultaneous optimization of several response variables, Journal of Quality Technology, vol. 12, pp. 214- 219, 1980 [19] E. Del Castillo, D.C. Montgomery and D.R. McCarville, Modified Desirability functions for Multiple Response Optimization, Journal of Quality Technology, vol. 28, no. 3, pp. 337-345, 1996 [20] K. J. Kim and D.K.J Lin, Simultaneous optimization mechanical properties of steel by maximizing exponential desirability functions, Applied Statistics, vol. 49, no. 3, pp. 311-325, 2000 [21] O. Köksoy,Dual response optimization: The desirability approach, International Journal of Industrial Engineering, vol. 12, no. 4, pp.335- 342, 2005 [22] A.I. Khuri and M. Conlon, Simultaneous Optimization of Multiple Responses Represented by Polinomial Regression Functions, Technometrics, vol. 23, pp. 363-375, 1981 [23] J.J. Pignatiello, Strategies for Robust Multiresponse Quality Engineering, IIE Transactions, vol. 25, pp. 5-15, 1993 [24] G. Vining, A compromise approach to multiresponse optimization, Journal of Quality Technology, vol. 30, no. 4, pp. 309-314, 1998 [25] A.E. Ames, N. Mattucci, S. Macdonald, G. Szonyi and D.M. Hawkins, Quality Loss Function for Optimization Across Multiple Response Surface, Journal of Quality Technology, vol. 29, pp. 339-346, 1997 [26] Y.H. Ko, K.J. Kim, and C.H. Jun, A New Loss Function-Based Method for Multiresponse Optimization, Journal of Quality Technology, vol. 37, no. 1, pp.50-59, 2005 [27] G. Derringer, A balancing act: optimizing a products properties, Qual. Prog., pp. 51-58, 1994 [28] http://www.che.boun.edu.tr/courses/che302/Cha pter%2010.pdf. [29] Minitab Release 14 for Windows, Minitab Inc., USA. [30] Matlab, The Language of Technical Computing, http://www.mathworks.com.