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- In relation to this article, we declare that there is no conflict of interest.
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Received November 18, 2013
Accepted April 29, 2014
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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A comparative study of hybrid models combining various kinetic and regression models for p-xylene oxidation
Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
Korean Journal of Chemical Engineering, October 2014, 31(10), 1746-1756(11), 10.1007/s11814-014-0126-z
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Abstract
The hybrid modeling approach, which combines kinetic and regression module parts to an overall process model, is an attractive process modeling approach. However, the selection of various kinetic and regression models affects the hybrid model performance. As an illustrating example, we investigated the p-xylene (PX) oxidation process and summarized the published results of the PX oxidation kinetic model. The kinetic parameters of three kinetic models_x000D_
(i.e., three frequently used kinetic models of PX oxidation) were estimated and the fitting results were evaluated. Six hybrid models were then developed based on these three kinetic models and two regression models (artificial neural network and support vector regression). Afterwards, a comparative study of the six hybrid models was carried out based on various kinetic and regression models. The performances of these kinetic and regression models on the hybrid models were also evaluated. Finally, the best suitable hybrid model was obtained for the PX oxidation process.
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Lashkarbolooki M, Hezave AZ, babapoor A, Korean J. Chem. Eng., 30(1), 213 (2013)
Park TC, Kim US, Kim LH, Jo BW, Yeo YK, Korean J. Chem. Eng., 27(4), 1063 (2010)
Gandhi AB, Gupta PP, Joshi JB, Jayaraman VK, Kulkarni BD, Ind. Eng. Chem. Res., 48(9), 4216 (2009)
Vapnik V, The nature of statistical learning theory, Springer (1999)
Lahiri SK, Ghanta KC, Korean J. Chem. Eng., 26(5), 1175 (2009)
Park TC, Kim TY, Yeo YK, Korean J. Chem. Eng., 27(6), 1662 (2010)
Lee CJ, Ko JW, Lee G, Korean J. Chem. Eng., 29(2), 145 (2012)
Fazlali A, Koranian P, Beigzadeh R, Rahimi M, Korean J. Chem. Eng., 30(9), 1681 (2013)
Lee DE, Song SO, Yoon ES, Korean J. Chem. Eng., 21(6), 1103 (2004)
Psichogios DC, Ungar LH, AIChE J., 38, 1499 (1992)
Qi HY, Zhou XG, Liu LH, Yuan WK, Chem. Eng. Sci., 54(13-14), 2521 (1999)
Zahedi G, Elkamel A, Lohi A, Jahanmiri A, Rahimpor MR, Chem. Eng. J., 115(1-2), 113 (2005)
Yan XF, Chem. Eng. Sci., 62(10), 2641 (2007)
Simon LL, Fischer U, Hungerbuhler K, Ind. Eng. Chem. Res., 45(21), 7336 (2006)
Ou S, Achenie LEK, J. Power Sources, 140(2), 319 (2005)
Kumar BS, Venkateswarlu C, Bioresour. Technol., 103(1), 300 (2012)
Zander HJ, Dittmeyer R, Wagenhuber J, Chem. Eng. Technol., 22(7), 571 (1999)
Zahedi G, Lohi A, Mahdi KA, Fuel Process. Technol., 92(9), 1725 (2011)
Sun Q, Pan C, Yan X, Korean J. Chem. Eng., 30(3), 518 (2013)
Luo N, Du WL, Ye ZC, Qian F, Ind. Eng. Chem. Res., 51(19), 6926 (2012)
Ince H, Trafalis TB, Decision Support Systems, 42, 1054 (2006)
Duarte BPM, Saraiva PM, Ind. Eng. Chem. Res., 42(1), 99 (2003)
Wang XF, Chen JD, Liu CB, Pan F, Chem. Eng. Res. Des., 88(4A), 415 (2010)
Jia R, Mao Z, Chang Y, Zhao L, Chem. Eng. Res. Des., 89(6), 772 (2011)
Cao G, Pisu M, Morbidelli M, Chem. Eng. Sci., 49, 5775 (1994)
Wang QB, Li X, Wang LJ, Cheng YW, Me G, Ind. Eng. Chem. Res., 44(2), 261 (2005)
Sun WZ, Pan Y, Zhao L, Zhou XG, Chem. Eng. Technol., 31(10), 1402 (2008)
Yan X, Zhao W, Intell. Autom. Soft. Co., 15, 41 (2009)
Xuefeng Y, Chemometr. Intell. Lab., 103(2), 152 (2010)
Cao GM, Servida A, Pisu M, Morbidelli M, AIChE J., 40(7), 1156 (1994)
Cincotti A, Orru R, Broi A, Cao G, Chem. Eng. Sci., 52(21-22), 4205 (1997)
Cincotti A, Orru R, Cao G, Catal. Today, 52(2-3), 331 (1999)
Yan XF, Du WL, Qian F, AIChE J., 50(6), 1169 (2004)
Wang QB, Cheng YW, Wang LJ, Li X, Ind. Eng. Chem. Res., 46(26), 8980 (2007)
Wang QB, Li X, Wang LJ, Cheng YW, Xie G, Ind. Eng. Chem. Res., 44(13), 4518 (2005)
Qian F, Tao LL, Sun WZ, Du WL, Ind. Eng. Chem. Res., 51(8), 3229 (2012)
Dong YM, Yan XF, Ind. Eng. Chem. Res., 52(7), 2537 (2013)
Raghavendrachar P, Ramachandran S, Ind. Eng. Chem. Res., 31(2), 453 (1992)
Bamford CH, Comprehensive chemical kinetics, Elsevier Science Ltd. (1972)
Chang CC, Lin CJ, ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27 (2011)