Abstract
The problem of parametric synthesis of a model predictive control (MPC) system by the chemical process of production of the kerosene fraction of an industrial fractionator under conditions of constraints and uncertainty is considered. The optimal parameters of the MPC algorithm are obtained as a result of solving the problem of multi-criteria optimization, taking into account the intervally specified parameters of the plant model.
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REFERENCES
Qian, X., Jia, S., Huang, K., Chen, H., Yuan, Y., and Zhang, L., Model predictive control of azeotropic dividing wall distillation column for separating furfural-water mixture, ISA Transactions, 2021, vol. 111, pp. 302–308.
Martin, P.A., Zanin, A.C., and Odloak, D., Integrating real time optimization and model predictive control of a crude distillation unit, Brazilian Journal of Chemical Engineering, 2019, vol. 36, pp. 1205–1222.
Mendis, P., Wickramasinghe, C., Narayana, M., and Bayer, C., Adaptive model predictive control with successive linearization for distillate composition control in batch distillation, 2019 Moratuwa Engineering Research Conference, 2019, pp. 366–369.
Schwenzer, M., Ay, M., Bergs, T., and Abel, D., Review on model predictive control: an engineering perspective, The International Journal of Advanced Manufacturing Technology, 2021, vol. 117, pp. 1327–1349.
Seborg, D.E., Edgar, T.E., Mellichamp, D.A., and Doyle, III F.J., Process Dynamics and Control, 4nd ed., Wiley, 2016, pp. 369–389.
Mayne, D.Q., Kerrigan, E.C., and Falugi, P., Robust model predictive control: advantages and disadvantages of tube-based methods, IFAC Proceedings Volumes, 2011, vol. 44, no. 1, pp. 191–196.
Hill, E., Biglarbegian, M., and Gadsden, S.A., Tube-based model predictive control of small satellite systems with uncertainty dynamics, Proceedings of CSME Congress, 2021.
Kayacan, E. and Peschel, J., Robust model predictive control of systems by modeling mismatched uncertainty, IFAC-PapersOnLine, 2016, vol. 49, no. 18, pp. 265–269.
Giraldo, S.A.C., Melo, P.A., and Secchi, A.R., Tuning of model predictive controllers based on hybrid optimization, Processes, 2022, vol. 10, 351.
Al-Jamimi, H.A., BinMakhashen, G.M., Deb, K., and Saleh, T.A., Multiobjective optimization and analysis of petroleum refinery catalytic processes: A review, Fuel, 2021, vol. 288.
Funding
The research was carried out within the state assignment of the Institute of Automation and Control Processes, Far Eastern Branch, Russian Academy of Sciences (topic no. FWFW-2021-0003).
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This paper was recommended for publication by A.A. Galyaev, a member of the Editorial Board
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Snegirev, O.Y., Torgashov, A.Y. Optimization of the Parameters of a Model Predictive Control System for an Industrial Fractionator. Autom Remote Control 85, 652–659 (2024). https://doi.org/10.1134/S0005117924700085
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DOI: https://doi.org/10.1134/S0005117924700085