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Fuzzy controller of model reduction distillation column with minimal rules

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the control of a binary distillation column is described. This control is done with fuzzy logic, one with PI- like fuzzy controller and the other with modified PI fuzzy controller, using the minimal rules for fuzzy processing. This work is focused on model reduction of Wood and Berry binary distillation column to get the best performance. It is desired to minimize the rules in order to reduce the computation time to make a faster decision. Comparisons will be made between two versions of fuzzy controllers utilizing reduced rules to verify the outputs. The controlled variables are top composition with high concentration and bottom composition with low. To demonstrate the performance of the fuzzy PI control schemes, results are compared with a classical PI controller and optimal methods, like Differential Evolution (DE), Invasive Weed Optimization (IWO). The proposed structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes. Then all the processes of the distillation column with itۥ s fuzzy controllers are simulated in MATLAB software as the results are shown.
Rocznik
Strony
80--94
Opis fizyczny
Bibliogr. 17 poz., fig., tab.
Twórcy
autor
  • Al-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad, Iraq
autor
  • Al-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad, Iraq
Bibliografia
  • [1] Aaron, S., Antony, A., & Kumaravel, G. (2018). Distillation Column Control in Labview Using Fuzzy Interference System. World Scientific News, 98, 214–220.
  • [2] Alawad, N., & Jebar, A. (2020). Decoupling and Model Reduction for the Binary Distillation Column Linear System. International Journal of Scientific Engineering and Science, 4(3), 25–31.
  • [3] Alwadie, A., Ying, H., & Shah, H. (2003). A practical Two-Input Two-Output Takagi-Sugeno Fuzzy Controller. International Journal of fuzzy systems, 5(2), 123–130.
  • [4] Avatefipour, O., Piltan, F., Reza, M., & Nasrabad, S. (2014). Design New Robust Self Tuning Fuzzy Backstopping Methodology. I.J. Information Engineering and Electronic Business, 1, 49–61. http://dx.doi.org/10.5815/ijieeb.2014.01.06
  • [5] Cutler, C.R., & Ramaker, B.L. (1979). Dynamic matrix control—a computer control algorithm. Houston, TX: AICHE national meeting.
  • [6] Drgona, J., Takác, Z., Hornák, M., Valo, R., & Kvasnica, M. (2017). Fuzzy Control of a Laboratory Binary Distillation Column. 2017 21st International Conference on Process Control (PC) (pp. 120–125). Štrbské Pleso, Slovakia. http://dx.doi.org/10.1109/PC.2017.7976200
  • [7] Farzin, M., & Mirshekari, M. (2014). Design Minimum Rule-Base Fuzzy Inference Nonlinear Controller for Second Order Nonlinear System. I.J. Intelligent Systems and Applications, 7, http://dx.doi.org/10.5815/ijisa.2014.07.10
  • [8] Fileti, A., Antunes, A., Silva, F., Jr .V. S., & Pereira, J. (2007). Experimental investigations on fuzzy logic for process control. Control Engineering Practice, 15(9), 1149–1160. http://dx.doi.org/10.1016/j.conengprac.2007.01.009
  • [9] Glankwamdee, S., Tarathammatikorn, K., & Chattanaanan, T. (1999). Fuzzy supervisory control system of a binary distillation column. In TENCON 99. Proceedings of the IEEE Region 10 Conference, 2, 1055– 1058. http://dx.doi.org/10.1109/TENCON.1999.818604
  • [10] Gorak, A., & Schoenmakers, H. (2014). Distillation: Operation and Applications.1st Edition. Elsevier.
  • [11] Hamdy, M., Ramadan, A., & Abozalam, B. (2018). Comparative Study of Different Decoupling Schemes for TITO Binary Distillation Column via PI Controller. In IEEE/CAA Journal of Automatica Sinica, 5(4), 869–877. http://dx.doi.org/10.1109/JAS.2016.7510040
  • [12] Hung, C., & Benito Ferndndee, R. (1993). Minimizing Rules of Fuzzy Logic System by Using a Systematic Approach. In Second IEEE International Conference on Fuzzy Systems, (vol. 1, pp. 38–44). San Francisco, CA, USA. http://dx.doi.org/10.1109/FUZZY.1993.327466
  • [13] Jacobsen, E.W., & Skogestad, S. (1991). Control of unstable distillation columns. American Control Conference, 1991, 773–778.
  • [14] Javadi, S., & Hosseini, J. (2009). Control of Binary Distillation Column Using Fuzzy PI Controllers. In Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision (pp. 145–152). http://dx.doi.org/10.5555/1627535.1627559
  • [15] Jin, Q., Wang, Q., & Liu, L. (2016). Design of decentralized proportional– integral–derivative controller based on decoupler matrix for two-input/two output process with active disturbance rejection structure. Advances in Mechanical Engineering, 8(6), 1–18. http://dx.doi.org/10.1177/1687814016652563
  • [16] Kalpana, R., Harikumar, K., Senthilkumar, J., Balasubramanian, G., & Abhay, S. (2017). Multivariable Static Output Feedback Control of a Binary Distillation Column using Linear Matrix Inequalities and Genetic Algorithm. Control and System Engineering. http://dx.doi.org/10.20944/preprints202003.0079.v1.
  • [17] Liu, G., Wang, Z., Mei, C., & Ding, Y. (2013). A review of decoupling control based on multiple models. In 2012 24th Chinese control and decision conference (pp. 1077–1081). Taiyuan. http://dx.doi.org/10.1109/CCDC.2012.6244171
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bf0b7e40-1b64-4229-9b6c-8e338237f185
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