INTERNAL MODEL CONTROL OF CUMENE PROCESS USING ANALYTICAL RULES AND EVOLUTIONARY COMPUTATION Original scientific paper
Article Sidebar
Main Article Content
Abstract
Cumene is a precursor for producing many organic chemicals and is thinner in paints and lacquers. Its production process involves one of the large-scale manufacturing processes with complex kinetics. Different classical control strategies have been implemented and compared in this process for the cumene reactor. As a system with large degrees of freedom, a novel approach for extracting the state space model from the COMSOL Multiphysics implementation of the system is adopted here. Internal Modern Control (IMC) based PI and PID controllers are derived for the system. The system is reduced to the FOPDT and SOPDT model structure to derive the controller setting using Skogestad half rules. The integral time is modified for excellent set point tracking and faster disturbance rejection. From the analysis, it can be stated that the PI controller suits more for this specific process. The particle swarm optimization (PSO) algorithm, an evolutionary computation technique, is also used to tune the PI settings. The PI controllers with IMC, Zeigler Nichols, and PSO tuning are compared, and it can be concluded that the PSO PI controller settles at 45 s without any oscillations and settles down faster for the disturbance of magnitude 0.5 applied at t = 800 s. However, it is computationally intensive compared to other controller strategies.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors grant to the Publisher the following rights to the manuscript, including any supplemental material, and any parts, extracts or elements thereof:
- the right to reproduce and distribute the Manuscript in printed form, including print-on-demand;
- the right to produce prepublications, reprints, and special editions of the Manuscript;
- the right to translate the Manuscript into other languages;
- the right to reproduce the Manuscript using photomechanical or similar means including, but not limited to photocopy, and the right to distribute these reproductions;
- the right to reproduce and distribute the Manuscript electronically or optically on any and all data carriers or storage media – especially in machine readable/digitalized form on data carriers such as hard drive, CD-Rom, DVD, Blu-ray Disc (BD), Mini-Disk, data tape – and the right to reproduce and distribute the Article via these data carriers;
- the right to store the Manuscript in databases, including online databases, and the right of transmission of the Manuscript in all technical systems and modes;
- the right to make the Manuscript available to the public or to closed user groups on individual demand, for use on monitors or other readers (including e-books), and in printable form for the user, either via the internet, other online services, or via internal or external networks.
References
S. Skogestad, J. Process Control. 13 (2003) 291—309. https://doi.org/10.1016/S0959-1524(02)00062-8.
J.G. Ziegler, N.B. Nichols, Trans. ASME. 64 (1942) 759—765. https://doi.org/10.1115/1.4019264.
D.E. Rivera, M. Morari, S. Skogestad, Ind. Eng. Chem. Process Des. Dev. 25 (1986) 252—265. https://doi.org/10.1021/i200032a041.
B.D. Tyreus, W.L. Luyben, Ind. Eng. Chem. Res. 31 (1992) 2625—2628. https://doi.org/10.1021/ie00011a029.
K.J. Astrom, P.I.D. Controllers, Theory, Design, and Tuning, Instrument Society of America, Research Triangle Park, North Carolina (1995).
K.J. Astrom, T. Hägglund, J. Process Control. 14 (2004) 635—650. https://doi.org/10.1016/j.jprocont.2004.01.002.
R. Eberhart, J. Kennedy, Particle Swarm Optimization, in Proceedings of the IEEE International Conference on Neural Networks, Citeseer, (1995) 1942—1948.
W. Zeng, W. Zhu, T. Hui, L. Chen, J. Xie, T. Yu, Nucl. Eng. Des. 360 (2020) 110513. https://doi.org/10.1016/j.nucengdes.2020.110513.
F. Marini, B. Walczak, Chemom. Intell. Lab. Syst. 149 (2015) 153—165. https://doi.org/10.1016/j.chemolab.2015.08.020.
D. Wang, D. Tan, L. Liu, Soft Comput. 22 (2018) 387—408. https://doi.org/10.1007/s00500-016-2474-6.
F. Mahmoudian, A.H. Moghaddam, S.M. Davachi, Can. J. Chem. Eng. 100 (2022). 90—102.
https://doi.org/10.1002/cjce.24072.
A.H. Moghaddam, J. Shayegan, J. Sargolzaei, J. Taiwan Inst. Chem. Eng. 62 (2016) 150—157. https://doi.org/10.1016/j.jtice.2016.01.024.
A. HedayatiMoghaddam, H. Hazrati, J. Sargolzaei, J. Shayegan, A, Appl. Water Sci. 7 (2017) 2753—2765. doi:10.1007/s13201- 016-0503-3.
H. Vaziri, A. HedayatiMoghaddam, S.A. Mirmohammadi,Chem. Pap. 74 (2020) 3311—3324. doi:10.1007/s11696-020-01162-w.
S. Bennett, Annu. Rev. Control 25 (2001) 43—53. https://doi.org/10.1016/S1367-5788(01)00005-0.
V.M. Lakshmanan, A. Kallingal, S. Sreekumar,, J. Indian Chem. Soc. 99 (2022) 100730. https://doi.org/10.1016/j.jics.2022.100730.
V.M. Lakshmanan, A. Kallingal, S. Sreekumar,, J. Control Decis. (2022) 1—11. https://doi.org/10.1080/23307706.2022.2146009
V. Gera, M. Panahi, S. Skogestad, N. Kaistha, Ind. Eng. Chem. Res. 52 (2013) 830—846. https://doi.org/10.1021/ie301386h.
A. Chudinova, A. Salischeva, E. Ivashkina, O. Moizes, A. Gavrikov, Procedia Chem. 15 (2015) 326—334. https://doi.org/10.1016/j.proche.2015.10.052.
V.M. Lakshmanan, A. Kallingal, S. Sreekumar, Int. J. Chem. React. Eng. (2021) 1—17. https://doi.org/10.1080/23307706.2022.2146009.
X. Yang, S. Wang, B. Li, Y. He, H. Liu, Fuel 274 (2020) 117829. https://doi.org/10.1016/j.fuel.2020.117829.
H.M. Park, Int. J. Heat Mass Transfer 116 (2018) 520—531. https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.035.
U.M. Nath, C. Dey, R.K. Mudi, IETE J. Res. (2021) 1—21. https://doi.org/10.1080/03772063.2021.1874839.
S.K. Pradhan, D. Acharya, D.K. Das, Ann. Nucl. Energy 165 (2022) 108675. https://doi.org/10.1016/j.anucene.2021.108675.
Z. Nie, Z. Li, Q. Wang, Z. Gao, J. Luo,Int. J. Robust Nonlinear Control. (2021). https://doi.org/10.1002/rnc.5848.
S. Sreekumar, A. Kallingal, L.V. Mundakkal, Chem. Ind. Chem. Eng. Q. 28(2) (2022) 127—134. https://doi.org/10.2298/CICEQ200911031S.
P.G. Junqueira, P.V Mangili, R.O. Santos, L.S. Santos, D.M. Prata, Chem. Eng. Process. 130 (2018) 309—325. https://doi.org/10.1016/j.cep.2018.06.010.
M.L. Vinila, K. Aparna, S. Sreepriya, in Int. Conf.Intell.Comput.Inf.Control Syst., Springer, 2019: pp. 201—208. https://doi.org/10.1007/978-3-030-30465-2_23.