Prediction of sludge volume index bulking using image analysis and neural network at a full-scale activated sludge plant

References

• R. Govoreanu, Activated Sludge Flocculation Dynamics: On-Line Measurement Methodology and Modelling, PhD thesis, Biological Sciences, 2004.
   Google Scholar
• D.P. Mesquita, A.L. Amaral, E.C. Ferreira, Characterization of activated sludge abnormalities by image analysis and chemometric techniques, Anal. Chim. Acta 705 (2011) 235–242.10.1016/j.aca.2011.05.050
   PubMed Web of Science ®Google Scholar
• B. Ernst, S. Neser, E. O’Brien, S.J. Hoeger, D.R. Dietrich, Determination of the filamentous cyanobacteria Planktothrix rubescens in environmental water samples using an image processing system, Harmful Algae 5 (2006) 281–289.10.1016/j.hal.2005.08.003
   Web of Science ®Google Scholar
• E. Liwarska-Bizukojc, Application of image analysis techniques in activated sludge wastewater treatment processes, Biotechnol. Lett. 27 (2005) 1427–1433.10.1007/s10529-005-1303-2
   PubMed Web of Science ®Google Scholar
• M. Da Motta, L.P. Amaral, M. Casellas, M.N. Pons, C. Dagot, N. Roche, E.C. Ferreira, H. Vivier, Characterisation of activated sludge by automated image analysis: Validation on full-scale plants, IFAC Comp. Appl. Biotechnol. (2001) 427–431.
   Google Scholar
• D.P. Mesquita, O. Dias, A.L. Amaral, E.C. Ferreira, Monitoring of activated sludge settling ability through image analysis: Validation on full-scale wastewater treatment plants, Bioprocess Biosyst. Eng. 32 (2009) 361–367.
   PubMed Web of Science ®Google Scholar
• R. Jenne, E.N. Banadda, I. Smets, J. Deurinck, J. Van Impe, Detection of filamentous bulking problems: Developing an image analysis system for sludge composition monitoring, Microsc. Microanal. 13 (2007) 36–41.
   PubMed Web of Science ®Google Scholar
• Y.G. Perez, S.G.F. Leite, M.A.Z. Coelho, Activated sludge morphology characterization through an image analysis procedure, Braz. J. Chem. Eng. 23 (2006) 319–330.
   Web of Science ®Google Scholar
• D.P. Mesquita, O. Dias, A.M. Dias, A.L. Amaral, E.C. Ferreira, Correlation between sludge settling ability and image analysis information using partial least squares, Anal. Chim. Acta 642 (2009) 94–101.10.1016/j.aca.2009.03.023
   PubMed Web of Science ®Google Scholar
• D.P. Mesquita, A.L. Amaral, E.C. Ferreira, Identifying different types of bulking in an activated sludge system through quantitative image analysis, Chemosphere 85 (2011) 643–652.10.1016/j.chemosphere.2011.07.012
   PubMed Web of Science ®Google Scholar
• N. Humaira, X.Y. Lee, K.H. Yeap, V.V. Yap, C.S. Soh, Application of Imaging Techniques for Monitoring Flocs in Activated Sludge, ICoBE, 27–28 February 2012, Penang, pp. 6–9.
   Google Scholar
• A.L. Amaral, D.P. Mesquita, E.C. Ferreira, Automatic identification of activated sludge disturbances and assessment of operational parameters, Chemosphere 91 (2013) 705–710.10.1016/j.chemosphere.2012.12.066
   PubMed Web of Science ®Google Scholar
• H. Nisar, L.X. Yong, Y. K. Ho, Y.V. Voon, S.C. Siang, Application of Imaging Techniques for Monitoring Flocs in Activated Sludge, ICoBE, 27–28 February, Penang, Malaysia, 2012.
   Google Scholar
• A.L. Amaral, E.C. Ferreira, Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression, Anal. Chim. Acta 544(1–2) (2005) 246–253.10.1016/j.aca.2004.12.061
   Web of Science ®Google Scholar
• J. Kilander, S. Blomström, A. Rasmuson, Spatial and temporal evolution of floc size distribution in a stirred square tank investigated using PIV and image analysis, Chem. Eng. Sci. 61(23) (2006) 7651–7667.10.1016/j.ces.2006.09.001
   Web of Science ®Google Scholar
• W. Heine, I. Sekoulov, H. Burkhardt, L. Bergen, J. Behrendt, Early warning-system for operation-failures in biological stages of WWTPs by on-line image analysis, Water Sci. Technol. 46 (2002) 117–124.
   PubMed Web of Science ®Google Scholar
• H.G. Han, J.F. Qiao, Prediction of activated sludge bulking based on a self-organizing RBF neural network, J. Process Control 22 (2012) 1103–1112.10.1016/j.jprocont.2012.04.002
   Web of Science ®Google Scholar
• A.M.P. Martins, K. Pagilla, J.J. Heijnen, M. van Loosdrecht, Filamentous bulking sludge—A critical review, Water Res. 38(4) (2004) 793–817.10.1016/j.watres.2003.11.005
   PubMed Web of Science ®Google Scholar
• J. Nan, W. He, Characteristic analysis on morphological evolution of suspended particles in water during dynamic flocculation process, Desalin. Water Treat. 41 (2012) 35–44.10.1080/19443994.2012.664676
   Web of Science ®Google Scholar
• J. Van Dierdonck, R. Van den Broeck, A. Vansant, J. Van Impe, I. Smets, Microscopic image analysis versus sludge volume index to monitor activated sludge bioflocculation: A case study, Sep. Sci. Technol. 48 (2013) 1433–1441.
   Web of Science ®Google Scholar
• E. Koivuranta, J. Keskitalo, T. Stoor, J. Hattuniemi, M. Sarén, J. Niinimäki, A comparison between floc morphology and the effluent clarity at a full-scale activated sludge plant using optical monitoring, Environ. Technol. 35(13) (2014) 1605–1610.10.1080/09593330.2013.875065
   PubMed Web of Science ®Google Scholar
• E. Beşdok, P. Çivicioğlu, M. Alçı, Impulsive noise suppression from highly corrupted images by using resilient neural networks, ICAISC (2004) 670–675.
   Google Scholar
• P. Çivicioğlu, M. Alçı, E. Beṣdok, Using an exact radial basis function artificial neural network for impulsive noise suppression from highly distorted image databases, in: Advances in Information Systems, Springer, Berlin, Heidelberg, 2005, pp. 383–391 ( Chapter 39).
   Web of Science ®Google Scholar
• M.E. Yüksel, A. Bastürk, E. Besdok, Detail-preserving restoration of impulse noise corrupted images by a switching median filter guided by a simple neuro-fuzzy network, EURASIP 16 (2004) 2451–2461.
   Google Scholar
• G. Civelekoğlu, The Modeling of Treatment Processes with Artificial Intelligence and Multistatistical Methods, PhD Thesis, Suleyman Demirel University, Institute of Science, Environmental Engineering, Isparta, 2006.
   Google Scholar
• F.S. Mjalli, S. Al-Asheh, H.E. Alfadala, Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance, J. Environ. Manage. 83 (2007) 329–338.10.1016/j.jenvman.2006.03.004
   PubMed Web of Science ®Google Scholar
• K.P. Oliveira-Esquerre, M. Mori, R.E. Bruns, Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis Braz. J. Chem. Eng. 19 (2002) 365–370.10.1590/S0104-66322002000400002
   Web of Science ®Google Scholar
• P. Civicioglu, Artificial cooperative search algorithm for numerical optimization problems, Inf. Sci. 229 (2013) 58–76.10.1016/j.ins.2012.11.013
   Web of Science ®Google Scholar
• P. Civicioglu, Backtracking search optimization algorithm for numerical optimization problems, Appl. Math. Comput. 219(15) (2013) 8121–8144.
   Web of Science ®Google Scholar
• D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, A comprehensive survey: Artificial bee colony (ABC) algorithm and applications, Art. Intell. Rev. 42(1) (2014) 21–57.
   Web of Science ®Google Scholar
• D. Karaboga, An idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Engineering Department, Tech. Rep. TR06, 2005, pp. 1–10.
   Google Scholar
• D. Karaboğa, B. Akay, Artificial bee colony algorithm on training artificial neural networks, IEEE 15th Signal Process. Commun. Appl. 2007, pp. 818–821.
   Google Scholar
• D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Appl. Soft Comput. 8 (2008) 687–697.10.1016/j.asoc.2007.05.007
   Web of Science ®Google Scholar
• B.A. Garro, H. Sossa, R.A. Vazquez, Artificial neural network synthesis by means of artificial bee colony algorithm, IEEE Cong. on Evol. Comp., 2011, pp. 331–338.
   Google Scholar
• D. Karaboga, A powerful and efficient algorithm for numerical function optimization artificial bee colony (ABC) algorithm, J. Global Optim. 39 (2007) 459–471.10.1007/s10898-007-9149-x
   Web of Science ®Google Scholar
• D. Karaboga, C. Ozturk, Neural networks training by articial bee colony algorithm on pattern classification, Neural Network World 19(3) (2009) 279–292.
   Web of Science ®Google Scholar
• Y. Özbay, R. Ceylan, B. Karlik, Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier, Expert Syst. Appl. 38 (2011) 1004–1010.
   Web of Science ®Google Scholar
• R. Ruiz, C. Maroto, A comprehensive review and evaluation of permutation flowshop heuristics, Eur. J. Oper. Res. 165(2) (2005) 479–494.10.1016/j.ejor.2004.04.017
   Web of Science ®Google Scholar
• M.F. Tasgetiren, Q.-K. Pan, P.N. Suganthan, A. Oner, A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion, Appl. Math. Modell. 37(10–11) (2013) 6758–6779.10.1016/j.apm.2013.02.011
   Web of Science ®Google Scholar
• D. Karaboga, C. Ozturk, A novel clustering approach: Artificial bee colony (ABC) algorithm, Appl. Soft Comp. 11 (2011) 652–657.10.1016/j.asoc.2009.12.025
   Web of Science ®Google Scholar
• H. Boztoprak, Y. Özbay, A new method for segmentation of microscopic images on activated sludge, Turk. J. Electr. Eng. Comput. Sci. ( in press) Available from: <http://journals.tubitak.gov.tr/elektrik/inpress.htm>.
   Web of Science ®Google Scholar
• T.E. Keskin, M. Düğenci, F. KaçaroğluKeskin, Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey), Environ. Earth Sci. 73(9) (2014) 5333–5347.
   Web of Science ®Google Scholar
• A. Rajasekhar, A. Abraham, M. Pant, Levy mutated artificial bee colony algorithm for global optimization, Syst. Man Cybern. IEEE International Conference on 9–12 October, 2011, pp. 655–662.
   Google Scholar
• T. Kurban, E. Beşdok, A comparison of RBF neural network training algorithms for inertial sensor based terrain classification, Sensors 9(8) (2009) 6312–6329.10.3390/s90806312
   PubMed Web of Science ®Google Scholar
• S.N. Omkar, J. Senthilnath, Artificial bee colony for classification of acoustic emission signal source, Int. J. Aerosp. Innovations 1(3) (2009) 129–143.10.1260/175722509789685865
   Google Scholar
• D.J. Mala, M. Kamalapriya, R. Shobana, V. Mohan, A non-pheromone based intelligent swarm optimization technique in software test suite optimization, Intell. Agent Multi-Agent Syst. IAMA 2009, International Conference on, IEEE, 2009.
   Google Scholar
• S. Çobanli, A. Öztürk, U. Güvenç, S. Tosun, Active power loss minimization in electric power systems through artificial bee colony algorithm, Int. Rev. Electr. Eng. 5(5) (2010) 2217–2223.
   Web of Science ®Google Scholar
• V. Ravi, K. Duraiswamy, A novel power system stabilization using artificial bee colony optimization, Eur. J. Sci. Res. 62(4) (2011) 506–517.
   Google Scholar
• H. Liu, G. Chen, G. Song, T. Han, Analog circuit fault diagnosis using bagging ensemble method with cross-validation, Mechatronics and Autom. ICMA International Conference on. IEEE 2009, pp. 4430–4434.
   Google Scholar