Development of Genetically tuned Fuzzy dynamic model for nonlinear dynamical systems: Application on reaction section of Tennessee Eastman process

Document Type : Article

Authors

1 Petroleum Refining Technology Development Division, Research Institute of Petroleum Industry (RIPI)-West side of Azadi Complex-Tehran-Iran.

2 Petroleum Refining Technology Development Division, Research Institute of Petroleum Industry (RIPI), West side of Azadi Complex, Tehran, Iran.

3 Dept. of Chemical and Petroleum Eng. Sharif University of Technology Tehran, Iran

Abstract

This work presents a new GA-Fuzzy method to model dynamic behavior of a process, based on Recurrent Fuzzy modeling through Mamdani approach whose inference system is optimized by Genetic Algorithms. By using the Mamdani approach, the proposed method surmounts the need to solve various types of mathematical equations governing the dynamic behavior of the process.
The proposed method consists of two steps; i) constructing a startup version of the model, ii) optimizing the shape of membership functions of the fuzzy sets corresponding to the variables exist in the fuzzy model, along with the production rules constituting the inference such that the obtained fuzzy model can predict the dynamic behavior of the process fairly accurately.
The proposed method is used to predict the dynamic behavior of the reaction section of the Tennessee Eastman (TE) benchmark. The overall accuracy of the obtained results compared to their corresponding counterparts in TE benchmark. The mean absolute percentage error (MAPE) of the key process variables which are temperature, pressure, and level of the reactor, and the reactor cooling water outlet temperature were calculated as 1.17%, 0.38%, 1.5%, and 1.57%, respectively, showing high prediction capability of the proposed method.

Keywords

Main Subjects


References
1. Mamdani, E.H. and Assilian, S. An experiment in
linguistic synthesis with a fuzzy logic controller", Int.
J. Man. Mach. Stud., 7(1), pp. 1-13 (1975).
2. Takagi, T. and Sugeno, M. Fuzzy identi cation of
systems and its applications to modeling and control",
IEEE Trans. Syst. Man Cybern., 15(1), pp. 116-132
(1985).
3. Kandi-D, M., Soleymani, M., and Ghadimi, A.A.
Designing an optimal fuzzy controller for a fuel cell
vehicle considering driving patterns", Sci. Iran., 23(1),
pp. 218-227 (2016).
4. Lima, N.M.N., Li~nan, L.Z., Manenti, F., Maciel Filho,
R., Maciel, M.R.W., Embirucu, M., and Medina, L.C.
Fuzzy cognitive approach of a molecular distillation
process", Chem. Eng. Res. Des., 89(4), pp. 471-479
(2011).
5. Lam, H.K. A review on stability analysis
of continuous-time fuzzy-model-based control
systems: From membership-function-independent to
membership-function-dependent analysis", Eng. Appl.
Artif Intell., 67, pp. 390-408 (2018).
6. Krzywanski, J. and Nowak, W. Modeling of bed-towall
heat transfer coecient in a large-scale CFBC by
fuzzy logic approach", Int. J. Heat Mass Transf., 94,
pp. 327-334 (2016).
7. Boulkaibet, I., Belarbi, K., Bououden, S., Marwala,
T., and Chadli, M. A new TS fuzzy model predictive
control for nonlinear processes", Expert. Syst. Appl.,
88, pp. 132-151 (2017).
8. Kamesh, R. and Rani, K.Y. Parameterized datadriven
fuzzy model based optimal control of a semibatch
reactor", ISA Trans., 64, pp. 418-430 (2016).
9. Esfandyari, M., Fanaei, M.A., and Zohreie, H. Adaptive
fuzzy tuning of PID controllers", Neural Comput.
Appl., 23(1), pp. S19-S28 (2013).
10. Adoko, A.C., Gokceoglu, C., Wu, L., and Zuo, Q.J.
Knowledge-based and data-driven fuzzy modeling for
rockburst prediction", Int. J. Rock Mech. Min. Sci.,
61, pp. 86-95 (2013).
11. Habbi, H., Zelmat, M., and Bouamama, B.O. A dynamic
fuzzy model for a drum-boiler-turbine system",
Automatica, 39(7), pp. 1213-1219 (2003).
3390 M.H. Eghbal Ahmadi et al./Scientia Iranica, Transactions C: Chemistry and ... 25 (2018) 3381{3390
12. Sala, A., Guerra, T.M., and Babuska, R. Perspectives
of fuzzy systems and control", Fuzzy Sets Syst., 156(3),
pp. 432-444 (2005).
13. Ahmadi, M.A. and Ebadi, M. Fuzzy modeling and
experimental investigation of minimum miscible pressure
in gas injection process", Fluid Phase Equilib.,
378, pp. 1-12 (2014).
14. Madaeni, S.S. and Kurdian, A.R. Fuzzy modeling and
hybrid genetic algorithm optimization of virus removal
from water using micro ltration membrane", Chem.
Eng. Res. Des., 89(4), pp. 456-470 (2011).
15. Amiryouse , M.R., Mohebbi, M.,
Golmohammadzadeh, S., Koocheki, A. and
Baghbani, F. Fuzzy logic application to model
ca eine release from hydrogel colloidosomes", J. Food
Eng., 212, pp. 181-189 (2017).
16. Wang, G., Luo, Z., Zhu, L., Chen, H., and Zhang,
L. Fuzzy estimation for temperature distribution of
furnace inner surface", Int. J. Therm. Sci., 51(1), pp.
84-90 (2012).
17. Cordon, O., Gomide, F., Herrera, F., Ho mann, F.,
and Magdalena, L. Ten years of genetic fuzzy systems:
Current framework and new trends", Fuzzy Sets Syst.,
141(1), pp. 5-31 (2004).
18. Herrera, F. Genetic fuzzy systems: Taxonomy, current
research trends and prospects", Evol. Intell., 1(1),
pp. 27-46 (2008).
19. Gudwin, R., Gomide, F., and Pedrycz, W. Context
adaptation in fuzzy processing and genetic algorithms",
Int. J. Intell. Syst., 13(10-11), pp. 929-948
(1998).
20. Cordon, O., Herrera, F., Del Jesus, M.J., Magdalena,
L., Sanchez, A.M., and Villar, P. A multiobjective
genetic algorithm for feature selection and granularity
learning in fuzzy-rule based classi cation systems",
20th NAFIPS Int. Conf., 3, Vancouver, Canada, pp.
1253-1258 (2001).
21. Cordon, O., Jose del Jesus, M., and Herrera, F.
Genetic learning of fuzzy rule-based classi cation
systems cooperating with fuzzy reasoning methods",
Int. J. Intell. Syst., 13(10-11), pp. 1025-1053 (1998).
22. Pulkkinen, P. and Koivisto, H. A dynamically constrained
multiobjective genetic fuzzy system for regression
problems", IEEE Trans. Fuzzy Syst., 18(1), pp.
161-177 (2010).
23. Harmsen, G.J. Industrial best practices of conceptual
process design", Chem. Eng. Process. Process Intensif.,
43(5), pp. 677-681 (2004).
24. Zimmermann, H.J. Fuzzy set theory", Wiley Interdisciplinary
Reviews: Comput. Stat, 2(3). pp. 317-332
(2010).
25. Gorrini, V. and Bersini, H. Recurrent fuzzy systems",
3rd IEEE World Conf. on Comput Intell., New
Rochelle, NY, USA, pp. 193-198 (1994).
26. Pedrycz, W. Why triangular membership functions?",
Fuzzy Sets Syst., 64(1), pp. 21-30 (1994).
27. Sharifzadeh, M. Integration of process design and
control: A review", Chem. Eng. Res. Des., 91(12). pp.
2515-2549 (2013).
28. Wasanapradit, T., Mukdasanit, N., Chaiyaratana, N.,
and Srinophakun, T. Solving mixed-integer nonlinear
programming problems using improved genetic algorithms",
Korean J. Chem. Eng., 28(1), pp. 32-40
(2010).
29. Costa, L. and Oliveira, P. Evolutionary algorithms
approach to the solution of mixed integer non-linear
programming problems", Comput. Chem. Eng., 25(2-
3), pp. 257-266 (2001).
30. Downs, J.J. and Vogel, E.F. A plant-wide industrial
process control problem", Comput. Chem. Eng., 17(3),
pp. 245-255 (1993).