- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 237693, 17 pages
doi:10.1155/2012/237693
Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network
1Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Avenue Padre Tomás Pereira, Taipa 999078, Macau
2State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
Received 5 August 2012; Revised 22 October 2012; Accepted 25 October 2012
Academic Editor: Siamak Talatahari
Copyright © 2012 Inchio Lou and Yuchao Zhao. 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
- D. Jenkins, M. G. Richard, and G. T. Digger, Manual on the Caused and Control of Activated Sludge Bulking, Foaming and other Solids Separation Problems, Lewis Publishers, New York, NY, USA, 2003. View at Zentralblatt MATH
- J. Chudoba, J. Blaha, and V. Madera, “Control of activated sludge filamentous bulking. III. Effect of sludge loading,” Water Research, vol. 8, no. 4, pp. 231–237, 1974. View at Publisher · View at Google Scholar · View at Scopus
- J. Chudoba, P. Grau, and V. Ottova, “Control of activated sludge filamentous bulking. II. Selection of microorganisms by means of a selector,” Water Research, vol. 7, no. 10, pp. 1389–1406, 1973. View at Publisher · View at Google Scholar · View at Scopus
- J. Chudoba, V. Ottova, and V. Madera, “Control of activated sludge filamentous bulking: I. Effect of the hydraulic regime or degree of mixing in an aeration tank,” Water Research, vol. 7, no. 8, pp. 1163–1182, 1973. View at Publisher · View at Google Scholar · View at Scopus
- M. Sezgin, D. Jenkins, and D. S. Parker, “A unified theory of filamentous activated sludge bulking,” Journal of the Water Pollution Control Federation, vol. 50, no. 2, pp. 362–381, 1978. View at Scopus
- A. M. P. Martins, J. J. Heijnen, and M. C. M. Van Loosdrecht, “Effect of feeding pattern and storage on the sludge settleability under aerobic conditions,” Water Research, vol. 37, no. 11, pp. 2555–2570, 2003. View at Publisher · View at Google Scholar · View at Scopus
- R. Goel, T. Mino, H. Satoh, and T. Matsuo, “Intracellular storage compounds, oxygen uptake rates and biomass yield with readily and slowly degradable substrates,” Water Science and Technology, vol. 38, no. 8-9, pp. 85–93, 1998. View at Publisher · View at Google Scholar · View at Scopus
- M. C. M. Van Loosdrecht, M. A. Pot, and J. J. Heijnen, “Importance of bacterial storage polymers in bioprocesses,” Water Science and Technology, vol. 35, no. 1, pp. 41–47, 1997. View at Publisher · View at Google Scholar · View at Scopus
- C. L. In and F. L. De Los Reyes, “Integrating decay, storage, kinetic selection, and filamentous backbone factors in a bacterial competition model,” Water Environment Research, vol. 77, no. 3, pp. 287–296, 2005. View at Publisher · View at Google Scholar · View at Scopus
- I. Lou and F. L. De Los Reyes III, “Substrate uptake tests and quantitative FISH show differences in kinetic growth of bulking and non-bulking activated sludge,” Biotechnology and Bioengineering, vol. 92, no. 6, pp. 729–739, 2005. View at Publisher · View at Google Scholar · View at Scopus
- A. G. Capodaglio, H. V. Jones, V. Novotny, and X. Feng, “Sludge bulking analysis and forecasting: application of system identification and artificial neural computing technologies,” Water Research, vol. 25, no. 10, pp. 1217–1224, 1991. View at Publisher · View at Google Scholar · View at Scopus
- H. R. Maier, A. Jain, G. C. Dandy, and K. P. Sudheer, “Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions,” Environmental Modelling and Software, vol. 25, no. 8, pp. 891–909, 2010. View at Publisher · View at Google Scholar · View at Scopus
- H. Çamdevýren, N. Demýr, A. Kanik, and S. Keskýn, “Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs,” Ecological Modelling, vol. 181, no. 4, pp. 581–589, 2005. View at Publisher · View at Google Scholar · View at Scopus
- S. H. Te and K. Y. H. Gin, “The dynamics of cyanobacteria and microcystin production in a tropical reservoir of Singapore,” Harmful Algae, vol. 10, no. 3, pp. 319–329, 2011. View at Publisher · View at Google Scholar · View at Scopus
- J. T. Kuo, Y. Y. Wang, and W. S. Lung, “A hybrid neural-genetic algorithm for reservoir water quality management,” Water Research, vol. 40, no. 7, pp. 1367–1376, 2006. View at Publisher · View at Google Scholar · View at Scopus
- F. Recknagel, M. French, P. Harkonen, and K. I. Yabunaka, “Artificial neural network approach for modelling and prediction of algal blooms,” Ecological Modelling, vol. 96, no. 1–3, pp. 11–28, 1997. View at Publisher · View at Google Scholar · View at Scopus
- K. I. Yabunaka, M. Hosomi, and A. Murakami, “Novel application of a back-propagation artificial neural network model formulated to predict algal bloom,” Water Science and Technology, vol. 36, no. 5, pp. 89–97, 1997. View at Publisher · View at Google Scholar · View at Scopus
- P. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
- L. Belanche, J. J. Valdés, J. Comas, I. R. Roda, and M. Poch, “Prediction of the bulking phenomenon in wastewater treatment plants,” Artificial Intelligence in Engineering, vol. 14, no. 4, pp. 307–317, 2000. View at Publisher · View at Google Scholar · View at Scopus
- M. Côté, B. P. A. Grandjean, P. Lessard, and J. Thibault, “Dynamic modelling of the activated sludge process: improving prediction using neural networks,” Water Research, vol. 29, no. 4, pp. 995–1004, 1995. View at Publisher · View at Google Scholar · View at Scopus
- H. G. Han and J. F. Qiao, “Prediction of activated sludge bulking based on a self-organizing RBF neural network,” Journal of Process Control, vol. 22, no. 6, pp. 1103–1112, 2012. View at Publisher · View at Google Scholar
- APHA, AWWA, and WEF, Standard Methods for the Examination of Water and Wastewater, American Public Health Association, Washington, DC, USA, 2002.
- P. H. Nielsen, C. Kragelund, R. J. Seviour, and J. L. Nielsen, “Identity and ecophysiology of filamentous bacteria in activated sludge,” FEMS Microbiology Reviews, vol. 33, no. 6, pp. 969–998, 2009. View at Publisher · View at Google Scholar · View at Scopus
- A. M. P. Martins, K. Pagilla, J. J. Heijnen, and M. C. M. Van Loosdrecht, “Filamentous bulking sludge—a critical review,” Water Research, vol. 38, no. 4, pp. 793–817, 2004. View at Publisher · View at Google Scholar · View at Scopus
- J. . Pallant, I. Chorus, and J. Bartram, “Toxic cyanobacteria in water,” in SPSS Survival Manual, McGraw Hill, 2007.
- W. . Zhang, I. Lou, W. K. Ung, Y. Kong, and K. M. Mok, “Eutrophication in Macau main storage reservoir,” in Proceedings of the 12th International Conference on Environmental Science and Technology,, pp. 1114–1121, Rhodes Island, Greece, September 2011.
- R. . Hecht-Nielsen, “Kolmogorov's mapping neural network existence theorem,” in Proceedings of the 1st IEEE International Joint Conference of Neural Networks, vol. 3, pp. 11–14, New York, NY, USA, 1987.
- L. L. Rogers and F. U. Dowla, “Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling,” Water Resources Research, vol. 30, no. 2, pp. 457–481, 1994. View at Publisher · View at Google Scholar · View at Scopus
- S. I. V. Sousa, F. G. Martins, M. C. M. Alvim-Ferraz, and M. C. Pereira, “Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations,” Environmental Modelling and Software, vol. 22, no. 1, pp. 97–103, 2007. View at Publisher · View at Google Scholar · View at Scopus