FUZZY CONTROLLER OF MODEL REDUCTION DISTILLATION COLUMN WITH MINIMAL RULES
Nasir ALAWAD
nasir.awad@uomustansiriyah.edu.iqAl-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad (Iraq)
Afaf ALSEADY
Al-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad (Iraq)
Abstract
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 it? s fuzzy controllers are simulated in MATLAB software as the results are shown.
Keywords:
Distillation column, Model reduction, PI controller, Fuzzy Inference System, MATLAB toolReferences
Aaron, S., Antony, A., & Kumaravel, G. (2018). Distillation Column Control in Labview Using Fuzzy Interference System. World Scientific News, 98, 214–220.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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
DOI: https://doi.org/10.5815/ijieeb.2014.01.06
Google Scholar
Cutler, C.R., & Ramaker, B.L. (1979). Dynamic matrix control—a computer control algorithm. Houston, TX: AICHE national meeting.
Google Scholar
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
DOI: https://doi.org/10.1109/PC.2017.7976200
Google Scholar
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
DOI: https://doi.org/10.5815/ijisa.2014.07.10
Google Scholar
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
DOI: https://doi.org/10.1016/j.conengprac.2007.01.009
Google Scholar
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
DOI: https://doi.org/10.1109/TENCON.1999.818604
Google Scholar
Gorak, A., & Schoenmakers, H. (2014). Distillation: Operation and Applications.1st Edition. Elsevier.
DOI: https://doi.org/10.1016/B978-0-12-386876-3.05002-X
Google Scholar
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
DOI: https://doi.org/10.1109/JAS.2016.7510040
Google Scholar
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
DOI: https://doi.org/10.1109/FUZZY.1993.327466
Google Scholar
Jacobsen, E.W., & Skogestad, S. (1991). Control of unstable distillation columns. American Control Conference, 1991, 773–778.
DOI: https://doi.org/10.23919/ACC.1991.4791478
Google Scholar
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
Google Scholar
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
DOI: https://doi.org/10.1177/1687814016652563
Google Scholar
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.
DOI: https://doi.org/10.20944/preprints202003.0079.v1
Google Scholar
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
DOI: https://doi.org/10.1109/CCDC.2012.6244171
Google Scholar
Lundstrom, P., Skogestad, S., & Doyle, J. (1999). Two-degree-of-freedom controller design for an ill-conditioned distillation process using mu-synthesis. In IEEE Transactions on Control Systems Technology, 7(1), 12–21. http://dx.doi.org/10.1109/87.736744
DOI: https://doi.org/10.1109/87.736744
Google Scholar
Luyben, W.L. (1970). Distillation decoupling. AIChE Journal, 16(2), 198–203. http://dx.doi.org/10.1002/aic.690160209
DOI: https://doi.org/10.1002/aic.690160209
Google Scholar
Margaglio, E., Lamanna, R., & Glorennec, P.-Y. (1997). Control of a Distillatio in Column Using Fuzzy Inference Systems. Proceedings of 6th International Fuzzy Systems Conference (vol. 2, pp. 995–999). Barcelona. http://dx.doi.org/10.1109/FUZZY.1997.622844
DOI: https://doi.org/10.1109/FUZZY.1997.622844
Google Scholar
Miccio, M., & Cosenza, B. (2014). Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers. Journal of Process Control, 24(5), 475–484. http://dx.doi.org/10.1016/j.jprocont.2013.12.007
DOI: https://doi.org/10.1016/j.jprocont.2013.12.007
Google Scholar
Nayef Ghasem, R.H. (2014). Principles of Chemical Engineering Processes: Material and Energy Balances. Second Edition. London: CRC Press, Taylor and Francis Group.
DOI: https://doi.org/10.1201/b17696
Google Scholar
Perry, R.H., & Green, D. (2008). Perry’s Chemical Engineers. Handbook, Eighth Edition. McGraw Hill professional, McGraw-Hill.
Google Scholar
Prodanović, L.S., Nedić, N., Filipović, Ž., & Dubonjić, M. (2017). Modified approach to distillation column control. Hemijska industrija, 71(3), 183–193. http://dx.doi.org/10.2298/HEMIND160326028P
DOI: https://doi.org/10.2298/HEMIND160326028P
Google Scholar
Reznik, L. (1997). Fuzzy Controllers. British Library Cataloguing. Skogestad, S. (1997). Dynamics and control of distillation columns – a tutorial introduction. Chemical Engineering Reserach and Design, 75(6), 539–562. http://dx.doi.org/10.1205/026387697524092
DOI: https://doi.org/10.1205/026387697524092
Google Scholar
Vasickaninová, A., Bakošová, M., & Mészáros, A. (2016). Fuzzy control of a distillation column. Computer Aided Chemical Engineering, 38, 1299–1304. http://dx.doi.org/10.1016/B978-0-444-63428-3.50221-6
DOI: https://doi.org/10.1016/B978-0-444-63428-3.50221-6
Google Scholar
Wood, R., & Berry, M. (1973). Terminal composition control of binary distillation column. Chem Eng Sci, 28(9), 1707–1717. http://dx.doi.org/10.1016/0009-2509(73)80025-9
DOI: https://doi.org/10.1016/0009-2509(73)80025-9
Google Scholar
Authors
Nasir ALAWADnasir.awad@uomustansiriyah.edu.iq
Al-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad Iraq
Authors
Afaf ALSEADYAl-Mustansiriyh University, Faculty of Engineering, Computer Department, Palestine Street, 14022, Baghdad Iraq
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