کنترل برج تقطیر با مدل غیرخطی به‌روش فازی نوع-2 بهینه‌سازی شده با الگوریتم ژنتیک

نویسندگان

دانشگاه شهرکرد - دانشکده فنی و مهندسی

چکیده

فرآیند تقطیر از فرآیندهای مهم در صنایع شیمیایی به‌شمار می‌رود و کاربرد وسیعی در این صنایع دارد. برج تقطیر به‌عنوان یک ابزار محبوب نزد مهندسان شیمی به‌منظور جداسازی مواد مورد استفاده قرار می‌گیرد و متداول‌ترین روش در جداسازی مواد می‌باشد. ثابت نگه‌داشتن غلظت محصولات در برج تقطیر از دیدگاه کنترلی بسیار مهم است. کنترل این فرآیندهای پیچیده نیاز به روش‌های هوشمند دارد تا بتواند بر اساس رفتار سیستم، تصمیم مناسبی را برای کنترل آن اتخاذ کند. از میان روش‌های هوشمند، سیستم فازی به‌دلیل کارآیی این روش در کنترل سیستم‌های پیچیده در این تحقیق مورد استفاده قرار گرفته است. در این مقاله، یک کنترل‌کننده فازی نوع-1 برای یک مدل غیرخطی برج تقطیر طراحی شده است. در طراحی این کنترل‌کننده فازی، الگوریتم ژنتیک (GA) مورد استفاده قرار گرفته است. نشان داده شده است که کنترل‌کننده فازی عملکرد بهتری نسبت به کنترل‌کننده‌های PI متداول دارد. سپس کنترل‌کننده فازی نوع-2 جایگزین فازی نوع-1 شده و برای بهینه‌سازی عملکرد کنترل فازی-2، گین‌های خروجی آن با روش GA تعیین گردیده است. نشان داده شده است که عملکرد فازی-2 از لحاظ مختلف بهتر از فازی-1 می‌باشد و همچنین در برابر تغییرات تغذیه، مقاوم‌تر است. در این تحقیق از نرم‌افزار MATLAB/ SIMULINK برای مدل‌سازی و پیاده‌سازی روش‌های پیشنهادی استفاده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Control of a Non-Linear Distillation Column Using Type-2 Fuzzy Method Optimized by Genetic Algorithm

نویسندگان [English]

  • A. Asgari
  • G. R. Arab Markadeh
Faculty of technical and engineering, Shahrekord University, Shahrekord, Iran
چکیده [English]

The distillation process is important process in the chemical industry and has wide application in industry. Distillation tower is used by chemical engineers as a popular tool to separate materials and is the most common method for separating materials. Keeping constant the product composition in the distillation column is very important from control perspective. Control of these complicated processes need intelligent methods to adopt the appropriate decision for control based on the behavior of the system. Between intelligent methods, fuzzy technique has superior response in complex systems control and so is used in this study. At first, a type-1fuzzy controller is designed for non-linear model of distillation tower. In design of this Fuzzy controller, genetic algorithm (GA) is used for optimization of fuzzy. It has been shown that the fuzzy controller is better than conventional PI one. Then the type-1 fuzzy controller has been replaced with type-2 fuzzy controller and to optimize the performance of fuzzy-2 control, its output gains is determined by GA and has been shown that the performance of type-2 is better than type-1 in various points of view. In this study, the MATLAB/SIMULINK software has been used for modeling and implementing the proposed methods.

کلیدواژه‌ها [English]

  • Distillation column
  • non-liner model
  • composition control
  • fuzzy system
  • type-2 fuzzy controller
  • genetic algorithm
[1] S. Skogestad and M.Morari, “Shortcut model for distillation column,” California Institute of Technology, Chemical Engineering, PhD Thesis, 1997.
[2] S. Skogestad and M. Morari, “Understanding the dynamic behavior of distillation columns,” Industrial & Engineering Chemistry Research, vol. 27, no. 10, pp. 1848-1862, 1988.
[3] S. Skogestad, “Dynamics and Control of Distillation Columns - A Critical Survey,” Modeling, Identification and Control, vol. 18, no. 3, pp. 177--217, 1997.
[4] S. Skogestad and I. Postlethwaite, “Multivariable feedback control: analysis and design,” Wiley, 2007.
[5] R. Irani, R. Nasimi and M. Shahbazian, “Approximate predictive control of a distillation column using an evolving artificial neural network coupled with a genetic algorithm,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, vol. 37, no. 5, pp. 518-535, Feb. 2015.
[6] A. George, P. Riya and M. Francis, “Model reference adaptive control of binary distillation column composition using MIT adaptive mechanism,” International Journal of Engineering Research and Technology, vol. 4, no. 6, pp. 555–558, Jun. 2015.
[7] N. Sharma and K. Singh, “Model predictive control and neural network predictive control of TAME reactive distillation column,” Chemical Engineering and Processing: Process Intensification, vol. 59, pp. 9–21, Sep. 2012.
[8] P. Mishra, V. Kumar and K. P. S. Rana, “A fractional order fuzzy PID controller for binary distillation column control,” Expert Systems with Applications, vol. 42, no. 22, pp. 8533-8549, Dec. 2015.
[9] U. Kapoor, A. Rani, V. Singh and J. R. P. Gupta, “Simulation and control of reactivedistillation column,” India International Conference on Power Electronics (IICPE), New Delhi, pp. 1-7, Jan. 2011.
[10] J. Mo-yi and M. Lei, “The application of fuzzy control in extractive distillation column,” 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), Chengdu, vol. 6, pp. 322-324, Aug. 2010.
[11] N.N. Mohammad, N. Kasuan, M. H. F. Rahiman and M. N. Taib, “Steam temperature control using fuzzy logic for steam distillation essential oil extraction process,” IEEE Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, pp. 53-58, Jun. 2011.
[12] A. K. Singh, B. Tyagi and V. Kumar, “Comparative performance analysis of fuzzy logic controller for the composition control of binary distillation column,” IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, pp. 515-519, Sep. 2011.
[13] S. Javahernia and F. Masoudinia. "A new approach based on Fuzzy Inference System to control the product distillation column," Journal of Current Research in Science, vol. S (1), pp. 963-972, 2016.
[14] A. Vasičkaninová, M. Bakošová and A. Mészáros, "Fuzzy Control of a Distillation Column," Elsevier B.V, vol. 26, pp. 1299–1304, 2016.
[15] M. Miccio and B. Cosenza, "Control Of A Distillation Column By Type-2 And Type-1 Fuzzy Logic PID Controllers," Journal of Process Control,  vol. 24, no. 5, pp. 475-484, 2014.
[16] L.X.Wang, “A Course in Fuzzy Systems and Control,” Prentice-Hall international Inc., 1997.
[17] J. M. Mendel, “Advances in type-2 fuzzy sets and system,” Information Sciences, vol.177, no. 1, pp. 84-110, Jan. 2007.
[18] M. Nabipour, M. Razzaz, S.G. Seifossadat and S.S. Mortazavi, “injected voltage control of the dvr using a new hybrid adaptive controller in compensating network faults,” Tabriz Journal of Electrical Eng., vol. 46, no. 2, pp. 307-321, Summer 2016.
[19] H. Jafari and H. Kharrati, “3D Path planning system design and development for unmanned aerial vehicle,” Tabriz Journal of Electrical Eng., vol. 46, no. 3, pp. 83-94, Autumn 2016.
[20] M. Behnam and H. Pourghassem, “Epilepsy detection based on optimization of fused hartley transform feature with hybrid model of mlp and ga using memetic learning strategy,” Tabriz Journal of Electrical Eng., vol. 45, no. 4, pp. 51-67,  Winter 2015.
[21] S. Skogestad, “Dynamics and control of distillation columns - A critical survey,” Modeling, Identification and Control, vol. 18, no. 3, pp. 177--217, 1997.
[22] S. Skogestad and M. Morari, “A systematic approach to distillation column control,” Symposium Distillation, Brighton, 1987.
[23] S. Skogestad and I. Postlethwaite, “Multivariable feedback control: analysis and design,” Wiley, 2007.
[24]  J. H. Lilly, “Fuzzy control and identification,” New Jersey: John Wiley and Sons Inc., 2010.