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Farzi, A., Jomea, M. (2017). Simulation and Control of a Methanol-To-Olefins (MTO) Laboratory Fixed-Bed Reactor. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 36(2), 175-190.
Ali Farzi; Mohammad Javad Jomea. "Simulation and Control of a Methanol-To-Olefins (MTO) Laboratory Fixed-Bed Reactor". Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 36, 2, 2017, 175-190.
Farzi, A., Jomea, M. (2017). 'Simulation and Control of a Methanol-To-Olefins (MTO) Laboratory Fixed-Bed Reactor', Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 36(2), pp. 175-190.
Farzi, A., Jomea, M. Simulation and Control of a Methanol-To-Olefins (MTO) Laboratory Fixed-Bed Reactor. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 2017; 36(2): 175-190.

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

Article 15, Volume 36, Issue 2 - Issue Serial Number 82, Spring 2017, Page 175-190  XML PDF (847 K)
Document Type: Research Article
Authors
Ali Farzi 1; Mohammad Javad Jomea2
1Faculty of Chemical and Petroleum Engineering, University of Tabriz, Tabriz, I.R. Iran
2Faculty 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
Methanol-To-Olefins (MTO); dynamic simulation; Electrical resistance furnace; Artificial Neural Network (ANN); Neural Network Model Predictive Control (NNMPC)
Main Subjects
Simulation, Optimization, Control; Unit Operations, Separation Processes
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