Kinetic Modeling of the Fischer-Tropsch Reactions and Modeling Steady State Heterogeneous Reactor
The rate of production of main products of the Fischer-Tropsch reactions over Fe/HZSM5 bifunctional catalyst in a fixed bed reactor is investigated at a broad range of temperature, pressure, space velocity, H2/CO feed molar ratio and CO2, CH4 and water flow rates. Model discrimination and parameter estimation were performed according to the integral method of kinetic analysis. Due to lack of mechanism development for Fisher – Tropsch Synthesis on bifunctional catalysts, 26 different models were tested and the best model is selected. Comprehensive one and two dimensional heterogeneous reactor models are developed to simulate the performance of fixed-bed Fischer – Tropsch reactors. To reduce computational time for optimization purposes, an Artificial Feed Forward Neural Network (AFFNN) has been used to describe intra particle mass and heat transfer diffusion in the catalyst pellet. It is seen that products' reaction rates have direct relation with H2 partial pressure and reverse relation with CO partial pressure. The results show that the hybrid model has good agreement with rigorous mechanistic model, favoring that the hybrid model is about 25-30 times faster.
[1] Ketta J. J. Mc (1993). Encyclopedia of Chemical Processing and Design;
Marcel Dekker; 3rd edition,
[2] Raymond E. Kirk and Donald F. (1978).Othmer; Encyclopedia of
chemical technology; John Wiley & Sons; 3rd edition
[3] Parkinson G.( 1997). Fischer - Tropsch Comes Back, Chemical
Engineering, 39 - 40, 170
[4] Hedden, K., Jess, A., Kuntze, T(1994). From Natural gas to liquid
hydrocarbons (1). A new concept for the production of liquid
hydrocarbons from natural gas in remote areas; OIL GAS - European
Magazine, 3, 42 - 44
[5] Hedden, K., Jess, A., Kuntze, T(1997). From Natural gas to liquid
hydrocarbons (4). Production of diesel oil and wax by Fischer - Tropsch
synthesis using a nitrogen rich synthesis gas investigation on a semi
technical scale; Edröl Erdgas Kohle, 113, 12, 531-540,
[6] Shiri Garakani A. R., Yeganeh Mehr J., Jafari Jozani Kh.(1998). Fischer
- Tropsch reaction for production of gasoline on a coprecipitated iron
catalyst modified with HZSM-5; 15th Canadian Symposium on Catalysis;
17 - 20 May; Quebec, Canada
[7] Yegane Mehr J., jafari Jozani Kh. (1998). FT synthesis catalyst
production for producing liquid fuel from synthesis gas; Tahghigh,
Quarterly Bulletin of RIPI, 28, 106 - 124
[8] Shen W. J., Zhou J. L., Zhang B. J.( 1994). Kinetics of Fischer -
Tropsch synthesis over precipitated iron catalyst; Journal of natural gas
chemistry; 4, 385 - 400
[9] Zimmerman W.H. and Bukur D.B. (1990). Reaction kinetics over iron
catalysts used for the fischer-tropsch synthesis; Canadian Journal of
Chemical Engineering; 68, pp. 292-301.
[10] Van der Laan G.P. and Beenackers A.A.C.M. (1999). Kinetics and
Selectivity of the Fischer-Tropsch Synthesis: A Literature Review;
Catalysis Reviews, 41, pp. 255-318
[11] Nakhaei Pour A., Housaindokht M.R., Tayyari S.F., Zarkesh J., Alaei
M.R. (2010); Kinetic studies of the Fischer-Tropsch synthesis over La,
Mg and Ca promoted nano-structured iron catalyst; Journal of Natural
Gas Science and Engineering, 2 (2-3), pp. 61-68.
[12] TENG B., CHANG J,, WAN H, LU J,, ZHENG S., LIU Y., LIU Y.,
GUO X. (2007); A Corrected Comprehensive Kinetic Model of Fischer-
Tropsch Synthesis; Chines Journal of Catalysis; 28 (8), PP. 687-695.
[13] Ahmadi Marvast M., Sohrabi M., Zarrinpashne S., Baghmisheh Gh.;
Chem. Eng. Technol.; 28(1), 78-86; 2005
[14] José M. Serra, Avelino Corma, Antonio Chica, Estefania Argente,
Vicente Botti; Can artificial neural networks help the experimentation in
catalysis?; Catalysis Today; 81, 393-403; 2003
[15] J. A. Conesa, J. A. Caballero, J. A. Reyes-Labarta; Artificial neural
network for modeling thermal decompositions; J. Anal. Appl. Pyrolysis;
71, 343-352; 2004
[16] E.J. Molga, B.A.A. van Woezik, K.R. Westerterp; Neural networks for
modelling of chemical reaction systems with complex kinetics: oxidation
of 2-octanol with nitric acid; Chemical Engineering and Processing; 39,
323-334; 2000
[17] Daniel R. Parisi, Miguel A. Laborde; Modeling steady-state
heterogeneous gas-solid reactors using feedforward neural networks;
Computers and Chemical Engineering; 25, 1241-1250; 2001
[18] Bub, G.; Baerns, M. (1980). Prediction of the performance of catalytic
fixed bed reactors for Fischer - Tropsch synthesis; Chemical
Engineering Science; 35, 348-355
[1] Ketta J. J. Mc (1993). Encyclopedia of Chemical Processing and Design;
Marcel Dekker; 3rd edition,
[2] Raymond E. Kirk and Donald F. (1978).Othmer; Encyclopedia of
chemical technology; John Wiley & Sons; 3rd edition
[3] Parkinson G.( 1997). Fischer - Tropsch Comes Back, Chemical
Engineering, 39 - 40, 170
[4] Hedden, K., Jess, A., Kuntze, T(1994). From Natural gas to liquid
hydrocarbons (1). A new concept for the production of liquid
hydrocarbons from natural gas in remote areas; OIL GAS - European
Magazine, 3, 42 - 44
[5] Hedden, K., Jess, A., Kuntze, T(1997). From Natural gas to liquid
hydrocarbons (4). Production of diesel oil and wax by Fischer - Tropsch
synthesis using a nitrogen rich synthesis gas investigation on a semi
technical scale; Edröl Erdgas Kohle, 113, 12, 531-540,
[6] Shiri Garakani A. R., Yeganeh Mehr J., Jafari Jozani Kh.(1998). Fischer
- Tropsch reaction for production of gasoline on a coprecipitated iron
catalyst modified with HZSM-5; 15th Canadian Symposium on Catalysis;
17 - 20 May; Quebec, Canada
[7] Yegane Mehr J., jafari Jozani Kh. (1998). FT synthesis catalyst
production for producing liquid fuel from synthesis gas; Tahghigh,
Quarterly Bulletin of RIPI, 28, 106 - 124
[8] Shen W. J., Zhou J. L., Zhang B. J.( 1994). Kinetics of Fischer -
Tropsch synthesis over precipitated iron catalyst; Journal of natural gas
chemistry; 4, 385 - 400
[9] Zimmerman W.H. and Bukur D.B. (1990). Reaction kinetics over iron
catalysts used for the fischer-tropsch synthesis; Canadian Journal of
Chemical Engineering; 68, pp. 292-301.
[10] Van der Laan G.P. and Beenackers A.A.C.M. (1999). Kinetics and
Selectivity of the Fischer-Tropsch Synthesis: A Literature Review;
Catalysis Reviews, 41, pp. 255-318
[11] Nakhaei Pour A., Housaindokht M.R., Tayyari S.F., Zarkesh J., Alaei
M.R. (2010); Kinetic studies of the Fischer-Tropsch synthesis over La,
Mg and Ca promoted nano-structured iron catalyst; Journal of Natural
Gas Science and Engineering, 2 (2-3), pp. 61-68.
[12] TENG B., CHANG J,, WAN H, LU J,, ZHENG S., LIU Y., LIU Y.,
GUO X. (2007); A Corrected Comprehensive Kinetic Model of Fischer-
Tropsch Synthesis; Chines Journal of Catalysis; 28 (8), PP. 687-695.
[13] Ahmadi Marvast M., Sohrabi M., Zarrinpashne S., Baghmisheh Gh.;
Chem. Eng. Technol.; 28(1), 78-86; 2005
[14] José M. Serra, Avelino Corma, Antonio Chica, Estefania Argente,
Vicente Botti; Can artificial neural networks help the experimentation in
catalysis?; Catalysis Today; 81, 393-403; 2003
[15] J. A. Conesa, J. A. Caballero, J. A. Reyes-Labarta; Artificial neural
network for modeling thermal decompositions; J. Anal. Appl. Pyrolysis;
71, 343-352; 2004
[16] E.J. Molga, B.A.A. van Woezik, K.R. Westerterp; Neural networks for
modelling of chemical reaction systems with complex kinetics: oxidation
of 2-octanol with nitric acid; Chemical Engineering and Processing; 39,
323-334; 2000
[17] Daniel R. Parisi, Miguel A. Laborde; Modeling steady-state
heterogeneous gas-solid reactors using feedforward neural networks;
Computers and Chemical Engineering; 25, 1241-1250; 2001
[18] Bub, G.; Baerns, M. (1980). Prediction of the performance of catalytic
fixed bed reactors for Fischer - Tropsch synthesis; Chemical
Engineering Science; 35, 348-355
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:50529", author = "M. Ahmadi Marvast and M. Sohrabi and H. Ganji", title = "Kinetic Modeling of the Fischer-Tropsch Reactions and Modeling Steady State Heterogeneous Reactor", abstract = "The rate of production of main products of the Fischer-Tropsch reactions over Fe/HZSM5 bifunctional catalyst in a fixed bed reactor is investigated at a broad range of temperature, pressure, space velocity, H2/CO feed molar ratio and CO2, CH4 and water flow rates. Model discrimination and parameter estimation were performed according to the integral method of kinetic analysis. Due to lack of mechanism development for Fisher – Tropsch Synthesis on bifunctional catalysts, 26 different models were tested and the best model is selected. Comprehensive one and two dimensional heterogeneous reactor models are developed to simulate the performance of fixed-bed Fischer – Tropsch reactors. To reduce computational time for optimization purposes, an Artificial Feed Forward Neural Network (AFFNN) has been used to describe intra particle mass and heat transfer diffusion in the catalyst pellet. It is seen that products' reaction rates have direct relation with H2 partial pressure and reverse relation with CO partial pressure. The results show that the hybrid model has good agreement with rigorous mechanistic model, favoring that the hybrid model is about 25-30 times faster.
", keywords = "Fischer-Tropsch, heterogeneous modeling, kinetic study.", volume = "5", number = "2", pages = "114-8", }
