Fuzzy model prediction of Co (III)/Al2O3 catalytic behavior in Fischer-Tropsch synthesis

doi:10.1016/S1003-9953(10)60240-X

能源化学(英文) ›› 2011, Vol. 20 ›› Issue (6): 603-610.DOI: 10.1016/S1003-9953(10)60240-X

Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis

Mohammad Ali Takassi¹, Mahdi Koolivand Salooki²*, Morteza Esfandyari³

1. Department of Science, Petroleum College, Petroleum University of Technology, Ahvaz 6198144471, Iran; 2. Oil & Gas Processing Center of Excellence,
Chemical Engineering Department, University of Tehran, 11155-4563, Tehran, Iran; 3. Department of Chemical Engineering, Faculty of Engineering, Ferdowsi
University of Mashad, P.O.Box 1111 Mashad, Iran

收稿日期:2010-12-24 修回日期:2011-03-12 出版日期:2011-11-20 发布日期:2011-11-18
通讯作者: M. Koolivand Salooki

Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis

Mohammad Ali Takassi¹, Mahdi Koolivand Salooki²*, Morteza Esfandyari³

1. Department of Science, Petroleum College, Petroleum University of Technology, Ahvaz 6198144471, Iran; 2. Oil & Gas Processing Center of Excellence,
Chemical Engineering Department, University of Tehran, 11155-4563, Tehran, Iran; 3. Department of Chemical Engineering, Faculty of Engineering, Ferdowsi
University of Mashad, P.O.Box 1111 Mashad, Iran

Received:2010-12-24 Revised:2011-03-12 Online:2011-11-20 Published:2011-11-18
Contact: Mahdi

摘要/Abstract

摘要： The application of Co (III)/Al₂O₃ catalyst in Fischer-Tropsch synthesis (FTS) was studied in a wide range of synthesis gas conversions and compared with Fuzzy Simulation results. Present study applies fuzzy model to predicting the product composition of CH₄, CO₂ and CO in Fischer-Tropsch process for natural gas synthesis, in which the input vector was 4-dimension including four variables (operating pressure, operating temperature, time and CO/H₂ ratio) of 70 different experiments and the output product is a composition of CO₂, CO and CH₄. The Mamdani algorithm has been applied to the training of the fuzzy system and the test set was used to evaluate the performance of the system includingR², ARE, AARE and SD. The results demonstrated that the predicted values from the model were in good consistency with the experimental data. The work indicates how fuzzy inference system (FIS), as a promising predicting technique, would be effectively used in FTS.

关键词: fuzzy inference system, Fischer-Tropsch, natural gas, catalyst, CO (III), Al2O3

Abstract: The application of Co (III)/Al₂O₃ catalyst in Fischer-Tropsch synthesis (FTS) was studied in a wide range of synthesis gas conversions and compared with Fuzzy Simulation results. Present study applies fuzzy model to predicting the product composition of CH₄, CO₂ and CO in Fischer-Tropsch process for natural gas synthesis, in which the input vector was 4-dimension including four variables (operating pressure, operating temperature, time and CO/H₂ ratio) of 70 different experiments and the output product is a composition of CO₂, CO and CH₄. The Mamdani algorithm has been applied to the training of the fuzzy system and the test set was used to evaluate the performance of the system includingR², ARE, AARE and SD. The results demonstrated that the predicted values from the model were in good consistency with the experimental data. The work indicates how fuzzy inference system (FIS), as a promising predicting technique, would be effectively used in FTS.

Key words: fuzzy inference system, Fischer-Tropsch, natural gas, catalyst, CO (III), Al2O3

Mohammad Ali Takassi, Mahdi Koolivand Salooki*, Morteza Esfandyari. Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis[J]. 能源化学(英文), 2011, 20(6): 603-610.

Mohammad Ali Takassi, Mahdi Koolivand Salooki*, Morteza Esfandyari. Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis[J]. Journal of Energy Chemistry, 2011, 20(6): 603-610.

参考文献

[1] Pour A N, Housaindokht M R, Tayyari S F, Zarkesh J. J Nat Gas Chem, 2010, 19(4): 441
[2] Anderson R B. The Fischer-Tropsch Synthesis. Orlando, FL: Academic Press, 1984
[3] Bartholomew C H. Stud Surf Sci Catal, 1991, 64: 158
[4] Dry M E, Chem J. Technol Biotechnol, 2001, 77: 43
[5] Iglesia E. Appl Catal A, 1997, 161(1-2): 59
[6] Dalai A K, Davis B H. Appl Catal A, 2008, 348(1): 1
[7] Karimi A, Pour A N, Torabi F, Hatami B, Tavasoli A, Alaei M R, Irani M. J Nat Gas Chem, 2010, 19(5): 503
[8] Xiong H F, Zhang Y H, Liew K Y, Li J L. J Mol Catal A, 2008, 295(1-2): 68
[9] Song D C, Li J L. J Mol Catal A, 2006, 247(1): 206
[10] Li H L, Li J L, Ni H K, Song D C. Catal Lett, 2006, 110(1-2): 71
[11] Khodakov A Y, Bechara R, Griboval-Constant A. Appl Catal A, 2003, 254(2): 273
[12] Satish S, Setty Y P. Int Commun Heat Mass Transfer, 2005, 32(3-4): 539
[13] Mastorocostas P A, Theocharis J B. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2002, 32(2): 176
[14] Seghatoleslami N, Salooki M K, Mohamadi N. J Petrol Technol, 2011, 29: 1437
[15] Yao H M, Vuthaluru H B, Tade M O, Djukanovic D. Fuel, 2005, 84(12-13): 1535
[16] Sadrzadeh M, Ghadimi A, Mohammadi T. Chem Eng J, 2009, 151(1): 262
[17] Erguo L. Comput Chem Eng, 2002, 26(9): 1253
[18] Entchev E, Yang L B. J Power Sources, 2007, 170(1): 122
[19] Varol Y, Koca A, Oztop H F, Avci E. Expert Systems with Applications, 2008, 35(4): 1989
[20] Singh T N, Singh V K, Sinha S. Mine Water Env, 2006, 25(4): 214
[21] Lofti Zadeh A. Fuzzy Sets Information Control, 1965, 3: 338
[22] Wang L X. A Course in Fuzzy Systems and Control, Englewood Cliffs, USA, 1994
[23] Hiirsalmi M, Kotsakis E, Pesonen A, Wolski A. Discovery of Fuzzy Models from Observation Data, VTT Information Technology, Finland. 2000
[24] Takagi T, Sugeno M. IEEE Transactions on Systems Man and Cybernetics, 1985, 15(1): 116
[25] Sargolzaei J, Khoshnoodi M, Saghatoleslami N, Mousavi M. Appl Soft Comput, 2008, 8(1): 456
[26] Sivanandam S. N, et al. Introduction to Fuzzy Logic using MATLAB, New York: Springer Berlin Heidelberg, 2007
[27] Ross T. Fuzzy logic with engineering applications. New York: McGraw-Hill, Inc., 1995. 600
[28] Gokceoglu C, Zorlu K. Eng Appl Artif Intell, 2004, 17(1): 61
[29] Sivanandam S N, Sumathi S, Deepa S N. Introduction to Fuzzy Logic using MATLAB. New York: Springer Berlin Heidelberg, 2007

Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis

Fuzzy model prediction of Co (III)/Al₂O₃ catalytic behavior in Fischer-Tropsch synthesis

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