A decision support tool for technical processes optimization in drinking water treatment

References

• W.J. Masschelein, H. Chevolet, Processus unitaires du traitement de l’eau potable [Unit processes in drinking water treatment]. Cebedoc, Paris, 1997.
   Google Scholar
• Degrémont. Mémento technique de l’eau [Water treatment handbook] (Tomes 1 et 2, 10 Ed.), Tec & Doc (Ed), Paris, 2007.
   Google Scholar
• W.F. Gerdes, A new instrument-the streaming current detector, in: Proceedings of the 12th National ISA analysis Instrument Symposium, Houston, TX, USA, May 1966, pp. 181–198.
   Google Scholar
• C.A. Walker, J.T. Kirby, S.K. Dentel, The streaming current detector: A quantitative model. J. Colloid Interface Sci. 182 (1996) 71–81.
   Web of Science ®Google Scholar
• C. Gagnon, B.P.A. Grandjean, J. Thibault, Modelling of coagulant dosage in a water treatment plant. Eng. Appl. Artif. Intell. 11 (1997) 401–404.
   Web of Science ®Google Scholar
• A. Adgar, C.S. Cox, P.R. Daniel, A. Lowdon, Chemical dosing philosophies for a water treatment plant: Results of some pilot plant experimentation. In: Proceedings of the IEE Conference Publication 427 (1996) 1052–1057.
   Google Scholar
• N. Valentin, T. Denoeux, A neural network-based software sensor for coagulation control in a water treatment plant. Intell. Data Anal. 5 (2001) 23–39.
   Google Scholar
• B. Lamrini, M.V. Le Lann, E.K. Lakhal, Control, fault detection, and supervision in technical processes–Application to surface water treatment, in: 1st Water and Environment International Conference, October 26–29, Marrakech, 2011.
   Google Scholar
• J. Aguilar-Martin, M. Balssa, R. Lopez De Mantras, Recursive estimation of partitions: Examples of learning and self teaching in RN and IN, Questiio (Quaderns d’Etadisticia, Siestems, Informatica i Investigacio Operativa) 5 (1981) 150–172.
   Google Scholar
• T. Kohonen, Self-organizing Maps, vol. 30, Springer Series in Information Sciences, Berlin, 1995.
   Google Scholar
• G. Dreyfus, J.M. Martinez, M. Samuelides, M.B. Gordon, F. Badron, S. Thiria, L. Hérault, Réseaux de neurones: Méthodologie et applications [Neural networks: Methodology and applications]. Eyrolles, Paris, 2005.
   Google Scholar
• B. Lamrini, A. Benhammou, M.V. Le Lann, A. Karama, A neural software sensor for on-line prediction of coagulant dosage: Application to a drinking water treatment Plant. Trans. Inst. Meas. Control (TIMC) 27 (2005) 95–213.
   Web of Science ®Google Scholar
• B. Lamrini, M.V. Le Lann, A. Benhammou, E.K. Lakhal, Gestion supervisée d’une unité de coagulation pour la potabilisation des eaux à partir d’une méthodologie d’apprentissage et d’expertise [Coagulation monitoring through a learning and expertise methodology for drinking water]. J. Water Sci. 20 (2007) 325–338.
   Google Scholar
• B. Lamrini, E.K. Lakhal, M.V. Le Lann, L. Wehenkel, Data validation and missing data reconstruction using self organizing map for water treatment. Neural Comput. Appl. 20 (2011) 575–588.
   Web of Science ®Google Scholar
• J. Waissmann-Vilanova, Building a behavioural model for process supervision: Application to a wastewater treatment plant [PhD thesis], LAAS-CNRS, Institut National Polytechnique (INP), Toulouse, 2000.
   Google Scholar
• T. Kempowsky, Surveillance des procédés à base de méthodes de classification: conception d’un outil d’aide pour la détection et le diagnostic des défaillances Process monitoring based on classification methods: A design tool for fault detection and diagnosis [PhD thesis], LAAS-CNRS, Institut National des Sciences Appliquées (INSA), Toulouse, France, 2004.
   Google Scholar
• L. Hedjazi, J. Aguilar Martin, M.V. Le Lann, Similarity-margin based feature selection for symbolic interval data. Pattern Recognit. Lett. 32 (4) (2011) 578–585.
   Web of Science ®Google Scholar
• L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility. Fuzzy Set. Syst. 1 (1978) 3–28.
   Google Scholar
• V. Barnett, T. Lewis, Outliers in Statistical Data, Wiley Series in Probability & Mathematical Statistics. third ed ed. Wiley, New York, 1994.
   Google Scholar
• D. Cousineau, S. Chartier, Outliers detection and treatment: A review. Int. J. Psychol. Res. 3 (1) (2010) 58–67.
   Google Scholar
• C. Biernacki, G. Celeux, J.F. Si Abdallah, G. Govaert, F. Langrognet, MIXMOD User’s Guide (MIXture MODelling software: High Performance Model-based Cluster and Discriminant Analysis, Univ. Franche-Comté, France. 2009. http://www-math.univ-fcomte.fr/mixmod/index.php.
   Google Scholar
• D.J.C. MacKay, A practical Bayesian framework for backpropagation networks. Neural Comput. 4 (1992) 448–472.
   Web of Science ®Google Scholar
• F.D. Foresee, M.T. Hagan, Gauss-Newton approximation to Bayesian regularisation, in: Proceedings of the Int. Joint Conf. on, Neural Networks, Houston, TX, 1997, pp. 1930–1935.
   Google Scholar
• H.R. Tashaouie, G.B. Gholikandi, H. Hazrati, Artificial neural network modeling for predict performance of pressure filters in a water treatment plant. Desalin. Water Treat. 39 (2012) 192–198.
   Web of Science ®Google Scholar
• J.M. Grosso, C. Ocampo-Martínez, V. Puig, Adaptive multilevel neuro-fuzzy model predictive control for drinking water networks, in: 20th Mediterranean Conference on Control and Automation. IEEE, Barcelona, 2012 . pp. 1548–1553.
   Google Scholar
• J.A. Hernández, D. Colorado, Uncertainty analysis of COP prediction in a water purification system integrated into a heat transformer using several artificial neural networks. Desalin. Water Treat. 52 (7–9) (2012) 1443–1456.
   Google Scholar