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Received March 8, 2015
Accepted July 20, 2015
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Simultaneous multi-objective optimization of a new promoted ethylene dimerization catalyst using grey relational analysis and entropy measurement
1School of Chemical Engineering, Iran University of Science and Technology, Narmak, 1684613114, Tehran, Iran 2Catalysis research group, Research and Development Center, Arak Petrochemical Company, P. O. Box 575, Arak, Iran 3, Iran
Korean Journal of Chemical Engineering, February 2016, 33(2), 423-437(15), 10.1007/s11814-015-0158-z
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Abstract
A hybrid approach between the Taguchi method and grey relational analysis (GRA) with entropy measurement was applied to determine a single optimum setting for reaction factors of the proposed ethylene dimerization catalyst having overall selectivity to 1-butene (S1-btn (%)) and turnover frequency (TOF (h.1)) as multiple quality characteristics. Titanium tetrabutoxide (Ti(OC4H9)4) catalyst precursor in combination with triethyl aluminum (TEA) activator, 1,4-dioxane as a suitable modifier, and ethylene dichloride (EDC) as a novel promoter were used in the catalysis. Control factors of temperature, pressure, Al/Ti, 1,4-dioxane/Ti, and EDC/Ti mol ratios were investigated on three levels and their main effects were discussed. The effect of the binary interaction between temperature, pressure, and Al/Ti mol ratio was also examined. Weight of the responses was determined using entropy. Analysis of variance (ANOVA) for data obtained from GRA indicated that EDC/Ti mol ratio with 27.64% contribution had the most profound effect on the multiple quality characteristics. Development of the weighted Grey-Taguchi method used the Taguchi method as its basic structure, adopted GRA to deal with multiple responses, and entropy to enhance the reasonability of the comprehensive index produced by GRA to make the results more objective and accurate. Overall, these combined mathematical techniques improved catalytic performance for 1-butene production.
Keywords
References
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Bre A, Chauvin Y, Commereuc D, Nouv. J. Chim., 10, 535 (1986)
Belov GP, Dzhabiev TS, Kolesnikov IM, J. Mol. Catal., 14, 105 (1982)
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Taguchi G, System of experimental design: Engineering methods to optimize quality and minimize costs, UNIPUB/Kraus International Publications, New York (1987).
Ross PJ, Taguchi techniques for quality engineering, 2nd Ed., McGraw-Hill, New York (1996).
Deng JL, J. Grey Syst., 1, 1 (1989)
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Phadke MS, Quality engineering using robust design, AT&T Bell Laboratories Report, Prentice-Hall International Editions, New Jersey (1989).
Roy RK, Design of experiments using the Taguchi approach: 16 steps to product and process improvement, John Wiley & Sons, Inc., New York (2001).
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Chiang YM, Hsieh HH, Comput. Ind. Eng., 56, 648 (2009)
Rao R, Yadava V, Opt. Laser Technol., 41, 922 (2009)
Lindman HR, Analysis of variance in experimental design, Springer-Verlag, Berlin (1992).
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Chou CS, Liu CL, Chaung WC, Mater. Des., 44, 172 (2013)
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Bardinet GMP, Keck REJ, US Patent, 3,752,834 (1973).
Herman DF, US Patent, 2,654,770 (1953).
Pandey AK, Dubey AK, Opt. Laser Eng., 50, 328 (2012)
Srivastava VC, Mall ID, Mishra IM, Ind. Eng. Chem. Res., 46(17), 5697 (2007)
Fisher RA, Statistical methods for research workers, Oliver and Boyd, London (1925).
