Design and Implementation of an Intelligent PI Controller for a Real Time Non Linear pH Neutralization Process
DOI:
https://doi.org/10.24297/jac.v12i22.4918Keywords:
pH, PI Controller, Relay Feedback, Particle Swarm Optimization, Fire Fly Algorithm.Abstract
In many chemical processes, pH is one of the most important parameter and control of the pH is highly non linear due to the complex nature of processes. PID controllers are widely used in process industries to control linear, non-linear and stable, unstable systems. Selection of the suitable controller tuning procedure is important to improve the performance of the PID controller and hence the process variable can be controlled in better manner. In this work, Firefly Algorithm (FA) based intelligent PI controller is attempted for a Non Linear pH control process in real time. The effectiveness of the FA controller is studied in the selected operating regions and the results are validated with Relay Feedback (RFB) method and Particle Swarm Optimization (PSO) method based controllers in the simulation environment. The simulation results indicated that the steady state performance and error performance indices of the FA controller are better than the RFB and PSO controller in the selected operating regions. The FA controller is also implemented in the real time laboratory pH control system, the results confirm that the servo response and regulatory response of the proposed intelligent controller provides better performance with the FA based PI Controllers.
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References
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[3] Shebel A. AlSabbah, Mohammad A. Al-Khedher, Mohammad K. Abu Zalata & Tariq M. Younes, Evaluation of Multiregional Fuzzy Cascade Control for pH Neutralization Process, International Journal of Research and Reviews in Applied Sciences, 10 (2), 2012.
[4] Luyben W.L., Process Modeling, Simulation and Control for Chemical Engineers, McGraw-Hill, Ohio, 1989.
[5] Norazzizi Nordin, Siti Fathrita Mohd Amir, Riyanto and Mohamed Rozali Othman, Textile Industries Wastewater Treatment by Electrochemical Oxidation Technique Using Metal Plate, International Journal of Electrochemical Science, 8 (2013) , pp. 11403 – 11415.
[6] Rajinikanth V. and K. Latha, Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm, Applied Computational Intelligence and Soft Computing, (2012) doi:10.1155/2012/214264.
[7] Jain T. and M. J. Nigam, Optimization of PD-PI controller using swarm intelligence, International Journal of Computational Cognition, 6 (4) (2008) , pp. 55–59.
[8] Ali A. and S. Majhi, Design of optimum PID controller by bacterial foraging strategy, Proceedings of the IEEE International Conference on Industrial Technology, (2006) , pp. 601– 605.
[9] Kim D. H., Hybrid GA-BF based intelligent PID controller tuning for AVR system, Applied Soft Computing Journal, 11(1) (2011),pp. 11–22.
[10] Kim D. H., A. Abraham, and J. H. Cho, A hybrid genetic algorithm and bacterial foraging approach for global optimization, Information Sciences, 177 (18) (2007) , pp. 3918–3937.
[11] Biswas, A., S. Dasgupta, S. Das, and A. Abraham, Synergy of PSO and bacterial foraging optimization - a comparative study on numerical benchmarks, Advances in Soft Computing, 44 (2007) , pp. 255–263.
[12] Korani, W. M., H. T. Dorrah, and H. M. Emara, Bacterial foraging oriented by particle swarm optimization strategy for PID Tuning, Proceedings of the 8th IEEE International Conference on Computational Intelligence in Robotics and Automation, Korea, (2009) pp. 445–450.
[13] Kennedy, J and R.C. Eberhart, Particle swarm optimization, In Proceedings of IEEE international conference on neural networks, (1995) , pp. 1942-1948.
[14] Jim Pugh, Alcherio Martinoli, and Yizhen Zhang, Particle Swarm Optimization for Unsupervised Robotic Learning, Proceedings of IEEE swarm intelligence symposium (SIS), (2005), pp. 92-99
[15] Yang X. S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, UK, 2008.
[16] Amir Hossein Gandomia, Xin-She Yang, Amir Hossein Alavi, Mixed variable structural optimization using Firefly Algorithm, Computers & Structures, 89 (2011) , pp. 2325–2336.
[17] Senthilnath J., S.N. Omkar, V. Mani, Clustering using firefly algorithm: Performance study, Swarm and Evolutionary Computation, 1 (3), (2011) , pp. 164–171.
[18] Raja N.S.M., Manic, K.S. and Rajinikanth,V., Firefly Algorithm with Various Randomization Parameters: An Analysis, In B.K. Panigrahi et al. (Eds.): SEMCCO 2013, Part.1, Lecture notes in computer science (LNCS 8297), (2013) , pp. 110–121.
[19] Gopal M, Control Systems Principles and Design, 2nd Edition, TATA McGraw Hill, New Delhi, (2002)
[20] Meenakshipriya, B., Saravanan, K., Krishnamurthy, K. and Bhaba, P. K., Design and implementation of CDM-PI control strategy in a pH neutralization system, Asian Journal of Scientific Research, 5 (3) (2012) , pp. 72-92.
[21] Swati, D, V.S.R. Rao, R.Pickhardi and M.Chidambaram, Non-Linear PI controller for time delayed pH System, Indian Chem. Eng., 50 (2) (2008) , pp. 93-105
[22] Astrom, K.J. and Hagglund. T, Automatic tuning of simple regulators with specifications on phase and gain margins, Automatica, 20 (5) (1984) , pp. 645-651.
[23] Banu U. S. and G. Uma, Fuzzy gain scheduled continuous stirred tank reactor with particle swarm optimization based PID control minimizing integral square error, Instrumentation Science and Technology, 36 (4) (2008) , pp. 394-409.
[24] Arumugam M. S. and M. V. C. Rao, On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems, Discrete Dynamics in Nature and Society, (2006) Article ID 79295.
[25] Rajinikanth V. and K. Latha, Set point weighted PID controller tuning for unstable system using heuristic algorithm, Archives of Control Science, 22(4) (2012) , pp. 481-505.
[26] Latha, K., V. Rajinikanth, and P. M. Surekha, PSO-based PID controller design for a class of stable and unstable systems, ISRN Artificial Intelligence, (2013) vol. 2013, Article ID 543607.
[27] Pal S.K., C.S. Rai, A.P. Singh, Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non-Linear Optimization Problems, International Journal Intelligent Systems and Applications, (2012) , pp.50-57.
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Published
2016-12-15
How to Cite
Kandasamy, M., & Vijayachitra, S. (2016). Design and Implementation of an Intelligent PI Controller for a Real Time Non Linear pH Neutralization Process. JOURNAL OF ADVANCES IN CHEMISTRY, 12(22), 5415–5423. https://doi.org/10.24297/jac.v12i22.4918
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