DOI QR코드

DOI QR Code

Design of optimal PID controller for the reverse osmosis using teacher-learner-based-optimization

  • Rathore, Natwar S. (Department of Electrical Engineering, National Institute of Technology) ;
  • Singh, V.P. (Department of Electrical Engineering, National Institute of Technology)
  • Received : 2017.07.03
  • Accepted : 2017.12.06
  • Published : 2018.03.25

Abstract

In this contribution, the control of multivariable reverse osmosis (RO) desalination plant using proportional-integral-derivative (PID) controllers is presented. First, feed-forward compensators are designed using simplified decoupling method and then the PID controllers are tuned for flux (flow-rate) and conductivity (salinity). The tuning of PID controllers is accomplished by minimization of the integral of squared error (ISE). The ISEs are minimized using a recently proposed algorithm named as teacher-learner-based-optimization (TLBO). TLBO algorithm is used due to being simple and being free from algorithm-specific parameters. A comparative analysis is carried out to prove the supremacy of TLBO algorithm over other state-of-art algorithms like particle swarm optimization (PSO), artificial bee colony (ABC) and differential evolution (DE). The simulation results and comparisons show that the purposed method performs better in terms of performance and can successfully be applied for tuning of PID controllers for RO desalination plants.

Keywords

References

  1. Alatiqi, I., Ghabris, A. and Ebrahim, S. (1989), "System identification and control of reverse osmosis desalination", Desalination, 75, 119-140. https://doi.org/10.1016/0011-9164(89)85009-X
  2. A strom, K.J. (2012), Introduction to Stochastic Control Theory, Courier Corporation
  3. Bartels, C.R., Wilf, M., Andes, K. and Iong, J. (2005), "Design considerations for wastewater treatment by reverse osmosis", Water Sci. Technol., 51(6-7), 473-482.
  4. Bartman, A.R., Zhu, A., Christofides, P.D. and Cohen, Y. (2010), "Minimizing energy consumption in reverse osmosis membrane desalination using optimization-based control", J. Proc. Control, 20(10), 1261-1269. https://doi.org/10.1016/j.jprocont.2010.09.004
  5. Basu, M. (2014), "Teaching-learning-based optimization algorithm for multi-area economic dispatch", Energy, 68, 21-28. https://doi.org/10.1016/j.energy.2014.02.064
  6. Baykasoglu, A., Hamzadayi, A. and Kose, S.Y. (2014), "Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases", Inform. Sci., 276, 204-218. https://doi.org/10.1016/j.ins.2014.02.056
  7. Chaaben, A.B., Andoulsi, R., Sellami, A. and Mhiri, R. (2011), "MIMO Modeling approach for a small photovoltaic reverse osmosis desalination system", J. Appl. Fluid Mech., 4(1), 35-41.
  8. Corne, D., Dorigo, M., Glover, F., Dasgupta, D., Moscato, P., Poli, R. and Price, K.V. (1999), New Ideas in Optimization, McGraw-Hill Ltd., U.K.
  9. Gagnon, E., Pomerleau, A. and Desbiens, A. (1998), "Simplified, ideal or inverted decoupling?", ISA Trans., 37(4), 265-276. https://doi.org/10.1016/S0019-0578(98)00023-8
  10. Gambier, A., Wellenreuther, A. and Badreddin, E. (2009), "Optimal operation of reverse osmosis plants based on advanced control", Desalin. Water Treat., 10, 200-209. https://doi.org/10.5004/dwt.2009.922
  11. Karaboga, D. and Akay, B. (2009), "A comparative study of artificial bee colony algorithm", Appl. Math. Comput., 214(1), 108-132. https://doi.org/10.1016/j.amc.2009.03.090
  12. Karuppiah, R., Bury, S.J., Vazquez, A. and Poppe, G. (2012), "Optimal design of reverse osmosis-based water treatment systems", AIChE J., 58(9), 2758-2769. https://doi.org/10.1002/aic.13880
  13. Kim, G., Park, J., Kim, J., Lee, H. and Heo, H. (2009), "PID control of reverse osmosis desalination plant using immunegenetic algorithm", Proceedings of the 2009 ICROS-SICE International Joint Conference (ICCAS-SICE 2009), Fukuoka, Japan, August.
  14. Kim, J.S., Kim, J.H., Park, J.M., Park, S.M., Choe, W.Y. and Heo, H. (2008), "Auto tuning PID controller based on improved genetic algorithm for reverse osmosis plant", World Acad. Sci. Eng. Technol., 47(2), 384-389.
  15. Luyben, W.L. (1970), "Distillation decoupling", AIChE J., 16(2), 198-203. https://doi.org/10.1002/aic.690160209
  16. Malwatkar, G., Sonawane, S. and Waghmare, L. (2009), "Tuning PID controllers for higher-order oscillatory systems with improved performance", ISA Trans., 48(3), 347-353. https://doi.org/10.1016/j.isatra.2009.04.005
  17. Park, J., Kim, G., Kim, J., Na, S. and Heo, H. (2009), "Simulation of reverse osmosis plant using RCGA based PID controller", Proceedings of the 2009 ICROS-SICE International Joint Conference (ICCAS-SICE 2009), Fukuoka, Japan, August.
  18. Rao, R., Savsani, V. and Vakharia, D. (2012), "Teaching-learningbased optimization: An optimization method for continuous non-linear large scale problems", Inform. Sci., 183(1), 1-15. https://doi.org/10.1016/j.ins.2011.08.006
  19. Rao, R.V. and Patel, V. (2013), "Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm", Appl. Math. Modell., 37(3), 1147-1162. https://doi.org/10.1016/j.apm.2012.03.043
  20. Rathore, N.S., Kundariya, N. and Narain, A. (2013), "PID controller tuning in reverse osmosis system based on particle swarm optimization", J. Sci. Res. Pub., 3(6), 1-5.
  21. Riverol, C. and Pilipovik, V. (2005), "Mathematical modeling of perfect decoupled control system and its application: A reverse osmosis desalination industrial-scale unit", J. Anal. Meth. Chem., 2005(2), 50-54. https://doi.org/10.1155/JAMMC.2005.50
  22. Robertson, M., Watters, J., Desphande, P., Assef, J. and Alatiqi, I. (1996), "Model based control for reverse osmosis desalination processes", Desalination, 104(1), 59-68. https://doi.org/10.1016/0011-9164(96)00026-4
  23. Trelea, I.C. (2003), "The particle swarm optimization algorithm: Convergence analysis and parameter selection", Inform. Proc. Lett., 85(6), 317-325. https://doi.org/10.1016/S0020-0190(02)00447-7

Cited by

  1. A modified controller design based on symbiotic organisms search optimization for desalination system vol.68, pp.5, 2018, https://doi.org/10.2166/aqua.2019.162