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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 3476851, 9 pages
https://doi.org/10.1155/2018/3476851
Research Article

Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process

Industrial Systems Study and Renewable Energy Unit, National Engineering School of Monastir, University of Monastir, Ibn El Jazzar Street, Skanes, 5019 Monastir, Tunisia

Correspondence should be addressed to Intissar Khoja; moc.liamg@ajohkrassitni

Received 14 July 2017; Revised 5 December 2017; Accepted 20 December 2017; Published 28 January 2018

Academic Editor: Amparo Alonso-Betanzos

Copyright © 2018 Intissar Khoja et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Snyder and D. Wyant, “Activated sludge process control, training manual for wastewater treatment plant operators,” Department of Environmental Quality, State of Michigan.
  2. S. Daira, M. Bensoltane, Y. Djebbar, and H. Abida, “Management of control production for activated mud of the municipal wastewater treatment plant at souk-ahras using stoat,” Revue LJEE, 2014. View at Google Scholar
  3. A. A. Boukerroucha, “Modélisation des stations d'épuration a boues activées-cas de la station de Baraki(Alger),” Master memory, 2010. View at Google Scholar
  4. S. Julien, Modelisation et estimation pour le controle d'un procédé boues activées eliminan l'azote des eaux residuaires urbaines [Ph.D. thesis], Toulouse university, France, 1997.
  5. C. Gomez-Quintero, Modélisation et estimation robuste pour un procédé boues activées en alternance de phases [Ph.D. thesis], Toulouse university, France, 2002.
  6. J. C. Kabouris and A. P. Georgakakos, “Parameter and state estimation of the Activated Sludge Process - II. applications,” Water Research, vol. 30, no. 12, pp. 2867–2882, 1996. View at Publisher · View at Google Scholar · View at Scopus
  7. U. Jeppsson and G. Olsson, “Reduced order models for on-line parameter identification of the activated sludge process,” Water Science and Technology, vol. 28, no. 11-12, pp. 173–183, 1993. View at Google Scholar · View at Scopus
  8. S. Caraman, M. Barbu, and G. Dumitrascu, “Wastewater Treatment Process Identification Based on the Calculus of State Variables Sensibilities with respect to the Process Coefficients,” in Proceedings of the 2006 IEEE International Conference on Automation, Quality and Testing, Robotics, pp. 199–204, Cluj-Napoca, Romania, May 2006. View at Publisher · View at Google Scholar
  9. O. A. Z. Sotomayor, S. W. Park, and C. Garcia, “Multivariable identification of an activated sludge process with subspace-based algorithms,” Control Engineering Practice, vol. 11, no. 8, pp. 961–969, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Ekman, “Bilinear black-box identification and MPC of the activated sludge process,” Journal of Process Control, vol. 18, no. 7-8, pp. 643–653, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Caraman and M. Barbu, “The identification and robust control of a biological wastewater treatment process,” in Proceedings of the 52008 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2008 - THETA 16th Edition, pp. 37–42, rom, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. P. Holck, A. Sorsa, and K. Leiviskä, “Parameter identification in the activated sludge process,” Chemical Engineering Transctions, vol. 17, pp. 1293–1298, 2009. View at Google Scholar
  13. D. Sendrescu, “Parameter identification of anaerobic wastewater treatment bioprocesses using particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 103748, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. A. A. Juan, J. Faulin, S. E. Grasman, M. Rabe, and G. Figueira, “A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems,” Operations Research Perspectives, vol. 2, pp. 62–72, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. H. J. Xu, J. K. Liu, and Z. R. Lu, “Structural damage identification based on cuckoo search algorithm,” Advances in Structural Engineering, vol. 19, no. 5, pp. 849–859, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Ahmed and Z. Salam, “A Maximum power point tracking (MPPT) for PV system using Cuckoo Search with partial shading capability,” Applied Energy, vol. 119, pp. 118–130, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. A. A. El-Fergany and A. Y. Abdelaziz, “Capacitor allocations in radial distribution networks using cuckoo search algorithm,” IET Generation, Transmission & Distribution, vol. 8, no. 2, pp. 223–232, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Basu and A. Chowdhury, “Cuckoo search algorithm for economic dispatch,” Energy, vol. 60, pp. 99–108, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. A. K. Bhandari, V. Soni, A. Kumar, and G. K. Singh, “Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD,” ISA Transactions®, vol. 53, no. 4, pp. 1286–1296, 2014. View at Publisher · View at Google Scholar
  20. O. Roeva and V. Atanassova, “Cuckoo search algorithm for model parameter identification,” International Journal Bioautomation, vol. 20, no. 4, pp. 483–492, 2016. View at Google Scholar · View at Scopus
  21. J. Ma, T. O. Ting, K. L. Man, N. Zhang, S.-U. Guan, and P. W. H. Wong, “Parameter estimation of photovoltaic models via cuckoo search,” Journal of Applied Mathematics, vol. 2013, Article ID 362619, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. X.-T. Li and M.-H. Yin, “Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method,” Chinese Physics B, vol. 21, no. 5, Article ID 050507, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. “Evaluation of Cuckoo Search usage for model parameters estimation,” International Journal of Computer Applications, vol. 78, no. 11, 2013.
  24. J. S. Anderson, H. Kim, T. J. McAvoy, and O. J. Hao, “Control of an alternating aerobic-anoxic activated sludge system - Part 1: Development of a linearization-based modeling approach,” Control Engineering Practice, vol. 8, no. 3, pp. 271–278, 2000. View at Publisher · View at Google Scholar · View at Scopus
  25. I. Y. Smets, J. V. Haegebaert, R. Carrette, and J. F. Van Impe, “Linearization of the activated sludge model ASM1 for fast and reliable predictions,” Water Research, vol. 37, no. 8, pp. 1831–1851, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. I. Queinnec and G.-Q. Claudia-Sophya, “Reduced modeling and state observation of an activated sludge process,” Biotechnology Progress, vol. 25, no. 3, pp. 654–666, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Ouaarab, Résolution de Problémes d'Optimisation Combinatoire par des Métaheuristiques Inspirées de la Nature: Recherche du Coucou via les Vols de Lévy [Ph.D. thesis], Mohammed V Universty, Rabat Sciences Faculty, Souissi, Morocco, 2015.
  28. S.-K. S. Fan, Y.-C. Liang, and E. Zahara, “A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search,” Computers & Industrial Engineering, vol. 50, no. 4, pp. 401–425, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Levasseur, Y. Malécot, M. Boulon, and E. Flavigny, “Soil parameter identification using a genetic algorithm,” International Journal for Numerical and Analytical Methods in Geomechanics, vol. 32, no. 2, pp. 189–213, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Messai, A. Chammam, and A. Sellami, “Conductivity polynomial model parameters identification based on particle swarm optimization,” Control Engineering and Applied Informatics, vol. 15, no. 4, pp. 58–65, 2013. View at Google Scholar · View at Scopus