Simulation and Control of a Methanol-To-Olefins (MTO) Laboratory Fixed-Bed Reactor

Document Type : Research Article

Authors

1 Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, I.R. Iran

2 Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, I.R. IRAN

Abstract

In this research, modeling, simulation, and control of a methanol-to-olefins laboratory fixed-bed reactor with electrical resistance furnace have been investigated in both steady-state and dynamic conditions. The reactor was modeled as a one-dimensional pseudo-homogeneous system. Then, the reactor was simulated at steady-state conditions and the effect of different parameters including inlet flow rate, inlet temperature and electrical resistance temperature on reactor performance was studied. Results showed that the most effective parameter is electrical resistance temperature. Thus, it was selected as manipulating variable for controlling product quality. In the next step, dynamic simulation of the process was performed and the effect of different disturbances on the dynamic behavior of the reactor was assessed. Finally, PID and Neural Network Model Predictive (NNMP) controllers were utilized for process control, and their performances were compared to each other. The response of the control system to different disturbances and set point changes showed that both PID and NNMP control systems can maintain the process at the desired conditions. PID controller had smaller rise time and no offset compared to NNMP controller while NNMP controller had smaller overshoot.

Keywords

Main Subjects

References

[1] Van V., "Methanol To Olefins", SRI, Process Economics Report Number 261 (2007).
[2] Xiang D., Qian Y., Man Y., Yang S., Techno-Economic Analysis of the Coal-to-Olefins Process  in Comparison with the Oil-to-Olefins Process, Appl. Energy, 113: 639-647 (2014).
[3] Stöcker M., Methanol-to-Hydrocarbons: Catalytic Materials and Their Behavior, Microporous Mesoporous Mater., 29 (1–2): 3-48 (1999).
[4] Al Wahabi S.M., "Conversion of Methanol to Light Olefins on SAPO-34: Kinetic Modeling and Reactor Design", PhD. Thesis, Texas A&M University (2003).
[5] Froment G.F., Dehertog W.J.H., Marchi A.J., Zeolite Catalysis in the Conversion of Methanol into Olefins, In: "Catalysis", J.J. Spivey (Ed.), The Royal Society of Chemistry, 1-64 (1992).
[6] Freiding J., Kraushaar-Czarnetzki B., Novel Extruded Fixed-Bed MTO Catalysts with High Olefin Selectivity and High Resistance Against Coke Deactivation, Appl. Catal., A, 391 (1–2): 254-260 (2011).
[7] Yang H., Liu Z., Gao H., Xie Z., Synthesis and Catalytic Performances of Hierarchical SAPO-34 Monolith, J. Mater. Chem., 20(16): 3227-3231 (2010).
[8] Emrani P., Fatemi S., Ashraf-Talesh S., Effect of Synthesis Parameters on Phase Purity, Crystallinity and Particle Size of SAPO-34, Iran. J. Chem. Chem. Eng. (IJCCE), 30 (4): 29-36 (2011).
[9] Chang C.D., Hydrocarbons from Methanol, Catal. Rev., 25 (1): 1-118 (1983).
[10] Chen D., Rebo H.P., Moljord K., Holmen A., Methanol Conversion to Light Olefins over SAPO-34. Sorption, Diffusion, and Catalytic Reactions, Ind. Eng. Chem. Res., 38 (11): 4241-4249 (1999).
[11] Haw J.F., Song W., Marcus D.M., Nicholas J.B., The Mechanism of Methanol to Hydrocarbon Catalysis, Acc. Chem. Res., 36 (5): 317-326 (2003).
[12] Wu W., Guo W., Xiao W., Luo M., Methanol Conversion to Olefins (MTO) Over H-ZSM-5: Evidence of Product Distribution Governed by Methanol Conversion, Fuel Process. Technol., 108: 19-24 (2013).
[13] Bos A.N.R., Tromp P.J.J., Akse H.N., Conversion of Methanol to Lower Olefins. Kinetic Modeling, Reactor Simulation, and Selection, Ind. Eng. Chem. Res., 34 (11): 3808-3816 (1995).
[14] Gayubo A.G., Aguayo A.T., Olazar M., Vivanco R., Bilbao J., Kinetics of the Irreversible Deactivation of the HZSM-5 Catalyst in the MTO Process, Chem. Eng. Sci., 58 (23–24): 5239-5249 (2003).
[15] Park T.-Y., Froment G.F., Kinetic Modeling of the Methanol to Olefins Process. 1. Model Formulation, Ind. Eng. Chem. Res., 40 (20): 4172-4186 (2001).
[16] Park T.-Y., Froment G.F., Kinetic Modeling of the Methanol to Olefins Process. 2. Experimental Results, Model Discrimination, and Parameter Estimation, Ind. Eng. Chem. Res., 40 (20): 4187-4196 (2001).
[17] Alwahabi S.M., Froment G.F., Single Event Kinetic Modeling of the Methanol-to-Olefins Process on SAPO-34, Ind. Eng. Chem. Res., 43 (17): 5098-5111 (2004).
[18] Chen D., Grønvold A., Moljord K., Holmen A., Methanol Conversion to Light Olefins over SAPO-34: Reaction Network and Deactivation Kinetics, Ind. Eng. Chem. Res., 46 (12): 4116-4123 (2007).
[19] Mihail R., Straja S., Maria G., Musca G., Pop G., A Kinetic Model for Methanol Conversion to Hydrocarbons, Chem. Eng. Sci., 38 (9): 1581-1591 (1983).
[20] Rostami R.B., Lemraski A.S., Ghavipour M., Behbahani R.M., Shahraki B.H., Hamule T., Kinetic Modelling of Methanol Conversion to Light Olefins Process Over Slicoaluminophosphate (SAPO-34) Catalyst, Chem. Eng. Res. Des., 106: 347-355 (2016).
[21] Kaarsholm M., Rafii B., Joensen F., Cenni R., Chaouki J., Patience G.S., Kinetic Modeling of Methanol-to-Olefin Reaction over ZSM-5 in Fluid Bed, Ind. Eng. Chem. Res., 49: 29-38 (2010).
[22] Gayubo A.G., Arandes J.M., Aguayo A.T., Olazar M., Bilbao J., Calculation of the Kinetics of Deactivation by Coke in an Integral Reactor for a Triangular Scheme Reaction, Chem. Eng. Sci., 48 (6): 1077-1087 (1993).
[23] Chen D., Rebo H.P., Grønvold A., Moljord K., Holmen A., Methanol Conversion to Light Olefins Over SAPO-34: Kinetic Modeling of Coke Formation, Microporous Mesoporous Mater., 35–36 (0): 121-135 (2000).
[24] Kaarsholm M., Joensen F., Nerlov J., Cenni R., Chaouki J., Patience G.S., Phosphorous Modified ZSM-5: Deactivation and Product Distribution for MTO, Chem. Eng. Sci., 62 (18–20): 5527-5532 (2007).
[25] Chae H.-J., Song Y.-H., Jeong K.-E., Kim C.-U., Jeong S.-Y., Physicochemical Characteristics of ZSM-5/SAPO-34 Composite Catalyst for MTO Reaction, J. Phys. Chem. Solids, 71 (4): 600-603 (2010).
[26] Lee Y.J., Lee J.S., Park Y.S., Yoon K.B., Synthesis of Large Monolithic Zeolite Foams with Variable Macropore Architectures, Adv. Mater., 13 (16): 1259-1263 (2001).
[27] Müller S., Liu Y., Vishnuvarthan M., Sun X., van Veen A.C., Haller G.L., Sanchez-Sanchez M., Lercher J.A., Coke Formation and Deactivation Pathways on H-ZSM-5 in the Conversion of Methanol to Olefins, J. Catal., 325: 48-59 (2015).
[28] Chen J.Q., Bozzano A., Glover B., Fuglerud T., Kvisle, S., Recent Advancements in Ethylene and Propylene Production Using the UOP/Hydro MTO Process, Catal. Today, 106 (1–4): 103-107 (2005).
[29] Chang C.D., Kuo J.C.W., Lang W.H., Jacob S.M., Wise J.J., Silvestri A.J., Process Studies on the Conversion of Methanol to Gasoline, Ind. Eng. Chem. Process Des. Dev., 17 (3): 255-260 (1978).
[30] Anderson J.R., Mole T., Christov V., Mechanism of Some Conversions Over ZSM-5 Catalyst, J. Catal., 61 (2): 477-484 (1980).
[31] Voltz S.E., Wise J.J., "Development Studies on Conversion of Methanol and Related Oxygenates to Gasoline", Final Report, US ERDA Contract No. E (49-18)-1773 (1976).
[32] Gayubo A.G., Aguayo A.T., Sánchez del Campo A.E., Tarrío A.M., Bilbao J., Kinetic Modeling of Methanol Transformation into Olefins on a SAPO-34 Catalyst, Ind. Eng. Chem. Res., 39 (2): 292-300 (2000).
[33] Chen N.Y., Reagan W.J., Evidence of Autocatalysis in Methanol to Hydrocarbon Reactions over Zeolite Catalysts, J. Catal., 59 (1): 123-129 (1979).
[34] Chang C.D., A Kinetic Model for Methanol Conversion to Hydrocarbons, Chem. Eng. Sci., 35(3): 619-622 (1980).
[35] Alwahabi S.M., Froment G.F., Conceptual Reactor Design for the Methanol-to-Olefins Process on SAPO-34, Ind. Eng. Chem. Res., 43 (17): 5112-5122 (2004).
[36] Nijemeisland M., Dixon A.G., CFD Study of Fluid Flow and Wall Heat transfer in a Fixed Bed of Spheres, AIChE J., 50 (5): 906-921 (2004).
[37] Zhuang Y.-Q., Gao X., Zhu Y.-p., Luo Z.-h., CFD Modeling of Methanol to Olefins Process in a Fixed-Bed Reactor, Powder Technol., 221: 419-430 (2012).
[38] Schoenfelder H., Hinderer J., Werther J., Keil F.J., Methanol to Olefins—Prediction of the Performance of a Circulating Fluidized-Bed Reactor on the Basis of Kinetic Experiments in a Fixed-Bed Reactor, Chem. Eng. Sci., 49 (24, Part 2): 5377-5390 (1994).
[39] Soundararajan S., Dalai A.K., Berruti F., Modeling of Methanol to Olefins (MTO) Process in a Circulating Fluidized Bed Reactor, Fuel, 80 (8): 1187-1197 (2001).
[40] Chang J., Zhang K., Chen H., Yang Y., Zhang L., CFD Modelling of the Hydrodynamics and Kinetic Reactions in a Fluidised-Bed MTO Reactor, Chem. Eng. Res. Des., 91 (12): 2355-2368 (2013).
[41] Lu B., Luo H., Li H., Wang W., Ye M., Liu Z., Li J., Speeding up CFD Simulation of Fluidized Bed Reactor for MTO by Coupling CRE Model, Chem. Eng. Sci., 143: 341-350 (2016).
[42] Green D.W., Perry R.H., "Perry's Chemical Engineers' Handbook", McGraw-Hill Book Company, New York, USA (2007).
[43] Hottel A.F., Sarofim H.C., "Radiative Transfer", McGraw-Hill Book Company, New York, USA (1967).
[44] Froment G.F., Fixed Bed Catalytic Reactors—Current Design Status, Ind. Eng. Chem., 59 (2): 18-27 (1967).
[45] de Wasch A.P., Froment G.F., Heat Transfer in Packed Beds, Chem. Eng. Sci., 27 (3): 567-576 (1972).
[46] Dixon A.G., Cresswell D.L., Theoretical Prediction of Effective Heat Transfer Parameters in Packed Beds, AIChE J., 25 (4): 663-676 (1979).
[47] Froment G.F., Bischoff K.B., De Wilde J., "Chemical Reactor Analysis and Design", Wiley, New York, USA (2011).
[48] Schiesser W.E., "The Numerical Method of Lines: Integration of Partial Differential Equations", Academic Press, Inc., San Diego, California (1991).
[49] Samarasinghe, S., "Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition", Auerbach Publications, Taylor & Francis Group, New York (2006).
[50] Krose B., van der Smagt P., "An Introduction to Neural Networks ", The University of Amsterdam, The Netherlands (1996).
[51] Valadkhani A., Shahrokhi M., Simulation and Control of an Aromatic Distillation Column, Iran. J. Chem. Chem. Eng. (IJCCE), 26(2): 97-108 (2007).
[52] Åkesson B.M., Toivonen H.T., A Neural Network Model Predictive Controller, J. Process Control, 16(9): 937-946 (2006).
[53] Draeger A., Engell S., Ranke H., Model Predictive Control Using Neural Networks, IEEE Control Syst., 15(5): 61-66 (1995).
[54] Yeh T.-M., Huang M.-C., Huang C.-T., Estimate of Process Compositions and Plantwide Control from Multiple Secondary Measurements Using Artificial Neural Networks, Comput. Chem. Eng., 27 (1): 55-72 (2003).
[55] Akpan V.A., Hassapis G.D., Nonlinear Model Identification and Adaptive Model Predictive Control Using Neural Networks, ISA Trans., 50 (2): 177-194 (2011).
[56] Yu D.L., Gomm J.B., Implementation of Neural Network Predictive Control to a Multivariable Chemical Reactor, Control Eng. Pract., 11(11): 1315-1323 (2003).
[57] Kittisupakorn P., Thitiyasook P., Hussain M.A., Daosud W., Neural Network Based Model Predictive Control for a Steel Pickling Process, J. Process Control, 19 (4): 579-590 (2009).
[58] Shampine, L. F., and Reichelt, M. W., The MATLAB ODE Suite, SIAM J. Sci. Comput., 18: 1-22 (1997).
[59] Tan W., Liu J., Chen T., Marquez H.J., Comparison of Some Well-Known PID Tuning Formulas, Comput. Chem. Eng., 30 (9): 1416-1423 (2006).
Iranian Journal of Chemistry and Chemical Engineering (IJCCE)
Volume 36, Issue 2 - Serial Number 82
May and June 2017
Pages 175-190

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