Artificial Intelligence Based State Observer in Polymerization Process

  • Jarinah Mohd Ali Department of Chemical Engineering, Faculty of Engineering University of Malaya, 50603Kuala Lumpur
  • M. A. Hussain Department of Chemical Engineering, Faculty of Engineering University of Malaya, 50603Kuala Lumpur
Keywords: Artificial Intelligence, Fuzzy logic, State estimation, Polymerization, Reactor

Abstract

Observers or state estimators are devices used to estimate immeasurable key parameters that are due to noise, disturbances and mismatch. It is important to identify those variables prior to construct a control system and avoid fault or process disruption. In certain chemical processes, such observer usage produced unsatisfactory results therefore hybrid approached is the appropriate solution. Hybrid observers are combination of two or more conventional observers mainly to enhance the estimator’s performance and overcoming their limitations. In advanced cases, Artificial Intelligence algorithm is applied. This paper develops two hybrid observers namely sliding mode and extended Luenberger observers with fuzzy logic for approximating the monomer concentration in a polymerization reactor. It was found that the sliding mode observer- fuzzy combination is better based on noise handling with less oscillation.

References

1. Aguilar-López, R., & Martinez-Guerra, R. (2005). State estimation for nonlinear systems under model unobservable uncertainties: application to continuous reactor. Chemical Engineering Journal, 108(1–2), 139-144.
2. BenAmor, S., Doyle Iii, F. J., & McFarlane, R. (2004). Polymer grade transition control using advanced real- time optimization software. Journal of Process Control, 14(4), 349-364.
3. Gentric, C., Pla, F., Latifi, M. A., & Corriou, J. P. (1999). Optimization and non-linear control of a batch emulsion polymerization reactor. Chemical Engineering Journal, 75(1), 31-46.
4. McAuley, K., MacGregor, J., & Hamielec, A. E. (1990). A kinetic model for industrial gas‐phase ethylene copolymerization. AIChE Journal, 36(6), 837-850.
5. McAuley, K., Talbot, J., & Harris, T. (1994). A comparison of two-phase and well-mixed models for fluidized-bed polyethylene reactors. Chemical Engineering Science, 49(13), 2035-2045.
6. Ng, C. W., & Hussain, M. A. (2004). Hybrid neural network—prior knowledge model in temperature control of a semi-batch polymerization process. Chemical Engineering and
Processing: Process Intensification, 43(4), 559-570.
7. Vicente, M., BenAmor, S., Gugliotta, L. M., Leiza, J. R., & Asua, J. M. (2000). Control of Molecular Weight Distribution in Emulsion Polymerization a) Without Noise b) With noise Fig. 5: Monomer concentration using SMO based on co-monomer concentration Using On-Line Reaction Calorimetry. Industrial & Engineering Chemistry Research, 40(1) , 218-227.
8. Wei, N. C., Hussain, M. A., & Wahab, A. K. A. (2007). Control of a Batch Polymerization System Using Hybrid Neural Network - First Principle Model. The Canadian Journal of Chemical Engineering, 85(6) , 936-945.
9. Zambare, N., Soroush, M., & Grady, M. C. (2002). Real-time multirate state estimation in a pilot-scale polymerization reactor. AIChE Journal, 48(5), 1022-1033.
Published
2013-12-31
How to Cite
Mohd Ali, J., & Hussain, M. A. (2013). Artificial Intelligence Based State Observer in Polymerization Process. ASEAN Journal of Chemical Engineering, 13(2), 50-56. Retrieved from https://dev.journal.ugm.ac.id/v3/AJChE/article/view/8159
Section
Articles