Neural Wiener Based Model Predictive Control (NWMPC) for MTBE Catalytic Distillation Using Reduced Sequential Quadratic Programming (RSQP)

Abstract:

Reactive distillation is a process that combines both reactor and distillation column in one unit process. The reactive distillation is normally applied in MTBE production in order to achieve high reaction conversion and purity of the MTBE. Controlling such reactive distillation is a challenging task due to its highly nonlinear behavior and the existence of strong interactions among control variables. In this work, a Neural Wiener based model predictive control (NWMPC) is designed and implemented to control the tray temperature of MTBE reactive distillation. The Reduced SQP (RSQP) has been embedded as an optimizer in the NWMPC proposed. The MTBE reactive distillation has been modeled using aspen dynamic and the control study has been simulated using Simulink (Matlab) which is integrated with Aspen dynamic model. The results achieved show that the NWMPC is able to maintain tray temperatures at desired set points, able to reject the disturbance and robust toward robustness test conducted.

• Periodical: Advanced Materials Research (Volume 1113)
• Pages: 733-738
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1113.733
• Online since: July 2015
• Authors: Sudibyo Sudibyo, Muhamad Nazri Murat, Norashid Aziz*
• Keywords: MTBE, Neural Network, Neural Wiener, NMPC, Reduced SQP

References

[1] R. Taylor. And R. Krishna. Review: Modelling reactive distillation, Chem. Eng. Science, 55(2000) 5183-5229.

DOI: 10.1016/s0009-2509(00)00120-2

[2] M. M. Arefi, A. Montazeri, J. Poshtan and M. R. Jahed-Motlagh, Wiener-neural identification and predictive control of a more realistic plug-flow tubular reactor, Chemical Eng. Journal, vol. 138 (2008) 274-282.

DOI: 10.1016/j.cej.2007.05.044

[3] M. Lawry´nczuk, Precise and Computationally Efficient Nonlinear Predictive Control Based on Neural Wiener Models, ISMIS 2011, LNAI 6804 (2011) 663–672.

DOI: 10.1007/978-3-642-21916-0_70

[4] R.L. Tousain and O.H. Bosgra, Efficient dynamic optimization for Nonlinear Model Predictive Control - application to a High-Density Poly-Ethylene grade change problem, Mechanical Engineering Systems and Control Group, Delft University of Technology(2000).

DOI: 10.1109/cdc.2000.912860

[5] M. Diehl, H. G. Bock, J. P. Schloder, R. Findeisen, Z. Nagy, F. Allgower, Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations, Journal of Process Control 12 (2002) 577–585.

DOI: 10.1016/s0959-1524(01)00023-3

[6] L.T. Biegler, J. Nocedal, & C. Schmid& D. Ternet, Numerical experience with a reduced Hessian method for large scale constrained optimization,. Computational Optimization and Applications, 15(2000) 45-67.

DOI: 10.1023/a:1008723031056

[7] A. P. Higler, R. Taylor and R. Krishna. The influence of mass transfer and mixing on the performance of a tray column for reactive distillation, Chem. Eng. Science, 54 (1999) 2873-2881.

DOI: 10.1016/s0009-2509(98)00317-0