Modeling the quality of water and wastewater treatment using neural networks and hybrid neural networks
Subject Areas : StatisticsAhmad Jafarian 1 , Fatemeh Ghanbary 2 , Rahim saneeifard 3
1 - Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
2 - Department of Chemitry, Mahabad Branch, Mahabad, Iran
3 - Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran
Keywords: تصفیه فاضلاب, مدل سازی ریاضی, شبکه عصبی, شبکه عصبی ترکیبی, رگرسیون لجستیک,
Abstract :
One of the most important and fundamental factors in the life of living things is water. Therefore, water pollution is a major environmental problem and prevent water pollution and providing smart methods for water treatment is so important. Equipping engineering sciences with intelligent tools and artificial intelligence in the diagnose quality of wastewater treatments can reduce the errors of the methods. This paper presents a simple and hybrid neural network with statistical logistic regression method for modelling of the output quality of wastewater treatment. The proposed intelligent method plays an important role in the quality of wastewater treatment and can be used by artificial intelligence researchers and environmental engineers. Comparison of the predicted results by simple neural network and hybrid one showed that the efficiency of the hybrid model and it is suitable for our purpose. results of research proved that the new method has the highest efficiency with minimum errors.
[1] Nasr, S. Mahmoud, et al. Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT. Alexandria engineering journal 37-43 (2012)
[2] Wan, Jinquan, et al. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Applied Soft Computing 3238-3246 (2011)
[3] Honggui, Han, Li Ying, and Qiao Junfei. A fuzzy neural network approach for online fault detection in waste water treatment process. Computers & Electrical Engineering 2216-2226 (2014)
[4] Tay, Joo-Hwa, and Xiyue Zhang. A fast-predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Research 2849-2860 (2000)
[5] Steyer, Jean-Philippe, et al. Hybrid fuzzy neural network for diagnosis-application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor. Water Science and Technology 209-217(1997)
[6] Chen, Jeng-Chung, N. B. Chang. Assessing wastewater reclamation potential by neural network model. Engineering applications of artificial intelligence 149-157 (2003)
[7] G. Srecnik, Z. Debeljak. Use of Artificial Nueral Networks for retention Modelling in Ion Chromatography, Journal of Croatica Chemica acta 713-725(2002)
[8] A. Jafarian, F. Ghanbary. Polyaniline/wheat Husk Ash Nanocomposite Preparation and Modeling Its Removal Activity with an Artificial Neural Network. Journal of Chiang Mai Science 533-543(2017)
[9] Govindaraju, S. Rao. Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering 124-137(2000)
[10] Maier, R. Holger, and C. Graeme Dandy. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software 101-124(2000)
[11] T. Neelakantan, G. Brion, and S. Lingireddy. Neural network modelling of Cryptosporidium and Giardia concentrations in the Delaware River, USA. Water Science & Technology 125-132(2001)
[12] D.S. Lee, C.O. Jeon, J. M. Park, Hybrid neural network modeling of a full‐scale industrial wastewater treatment process, Biotechnology & Applied Microbiology, 670-682(2012)
[13] Hamoda, F. Mohamed, A. Ibrahim. Integrated wastewater treatment plant performance evaluation using artificial neural networks. Water Science and Technology 55-65(1999)
[14] Y.Yu, Z. Zou, S. Wang. Statistical regression modeling for energy consumption in wastewater treatment. Journal of Environmental Sciences 201-208(2019)
[15] 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. Journal of Environmental Management. 329-338 (2007)
[16] S. Haykin. Neural networks: a comprehensive foundation, 2nd Edition.
Prentice Hall. (1999)
[17] Y.X. Zhang. Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 68-75(2007).
[18] Knobbe, Lauren. Franken warns against weakening law on health-care spending. Minn Post. Retrieved (2013).
[19] R. Muoio, L.Palli, I. Ducci, E. Coppini, E. Bettazzi, Optimization of a large industrial wastewater treatment plant using a modeling approach: A case study. Journal of Environmental Management, 109-136 (2019)
[20] I. Pasztor, P. Thury, J. Pulai. Chemical oxygen demand fractions of municipal wastewater for modeling of wastewater treatment. International Journal of Environmental Science & Technology, 6. 12(2019): 51–56.
[21] D. Jérôme, D. W. Gujer. Data-driven modeling approaches to support wastewater treatment plant operation. Environmental Modelling & Software. 47-56(2012)
[22] Fahlman, E. Scott, and Christian Lebiere. The cascade-correlation learning architecture. (1989)
[23] Parker, David B. "Learning logic. Invention report S81-64, File 1, and Office of Technology Licensing." October, Stanford University (1982).
[24] Rumelhart, E. David, E. Geoffrey Hinton, and Ronald J. Williams. Learning internal representations by error propagation. No. ICS-8506.
[25] Werbos, Paul. "Beyond regression: New tools for prediction and analysis in the behavioral sciences." (1974).