Investigation of Artificial Intelligence Approaches Capability in Predicting the Wastewater Treatment Plant Performance (Case Study: Tabriz Wastewater Treatment Plant)

Document Type : Research Article

Authors

1 Department of Water Engineering , Faculty of civil Engineering, University of Tabriz

2 Department of Water Engineering, Faculty of Civil, University of Tabriz, Tabriz, Iran

3 Department of Civil Engineering, Ahar Branch, Islamic Azad University – Ahar - Iran

Abstract

Due to the excessive concern about environmental issues, researchers had to come up with a better solution to control the Wastewater treatment plants (WWTPs).In this research, two approaches, including Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used for modeling the effluent quality of the Tabriz Wastewater Treatment Plant. Input data of models consist ofBODinf, CODinf, TSSinf, and PHinf of influent sewage related to Tabriz Treatment Plant which has been used to predict the corresponding value of BODeff, CODeff, and TSSeff concerning the treatment plant effluent. The daily, weekly, and monthly average data have been studied. According to the results, the two approaches mentioned, have the best performance in the prediction of the monthly average dataset of effluent parameters of Tabriz Wastewater Treatment Plant.
 

Keywords

Main Subjects

References

[1] E. Yel, S. Yalpir, Prediction of primary treatment effluent parameters by Fuzzy Inference System (FIS) approach, Procedia Computer Science, 3 (2011) 659-665.
[2] Y.-S.T. Hong, M.R. Rosen, R. Bhamidimarri, Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis, Water research, 37(7) (2003) 1608-1618.
[3] F.S. Mjalli, S. Al-Asheh, H. Alfadala, Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance, Journal of Environmental Management, 83(3) (2007) 329-338.
[4] V. Nourani, G. Elkiran, S. Abba, Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach, Water Science and Technology, 78(10) (2018) 2064-2076.
[5] H. Türkmenler, M. Pala, Performance Assessment of Advanced Biological Wastewater Treatment Plants Using Artificial Neural Networks, (2017).
[6] C.W. Chan, G.H. Huang, Artificial intelligence for management and control of pollution minimization and mitigation processes, Engineering applications of artificial intelligence, 16(2) (2003) 75-90.
[7] B.V.M. Zare Abiane Hamid, Bayat Varkeshi Jaber., Evaluation of Ekbatan wastewater treatment plant using artificial neural network, Journal of Envirology (In Persian), 38(3) (2013) 85-98.
[8] F. Rafaat Motavalli, Danesh, S., Rajabi Mashhadi, H., Investigating and comparing the ability of two models of artificial neural network and neural network optimized with genetic algorithm in predicting the effluent quality of semi-mechanical Wastewater Treatment Plants, in:  The 10th national congress in Civil Engineering, Faculty of Civil Engineering, Tabriz. (In Persian), 2015.
[9] W. Chen, N.-B. Chang, W.K. Shieh, Advanced hybrid fuzzy-neural controller for industrial wastewater treatment, Journal of environmental engineering, 127(11) (2001) 1048-1059.
[10] K. Oliveira-Esquerre, M. Mori, R. Bruns, Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis, Brazilian Journal of Chemical Engineering, 19(4) (2002) 365-370.
[11] M.M. Hamed, M.G. Khalafallah, E.A. Hassanien, Prediction of wastewater treatment plant performance using artificial neural networks, Environmental Modelling & Software, 19(10) (2004) 919-928.
[12] J. Wan, M. Huang, Y. Ma, W. Guo, Y. Wang, H. Zhang, W. Li, X. Sun, Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system, Applied Soft Computing, 11(3) (2011) 3238-3246.
[13] H. Guo, K. Jeong, J. Lim, J. Jo, Y.M. Kim, J.-p. Park, J.H. Kim, K.H. Cho, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, Journal of Environmental Sciences, 32 (2015) 90-101.
[14] A.E. Tümer, S. Edebalı, Prediction of wastewater treatment plant performance using multilinear regression and artificial neural networks, in:  2015 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), IEEE, 2015, pp. 1-5.
[15] D. Manu, A.K. Thalla, Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater, Applied Water Science, 7(7) (2017) 3783-3791.
[16] F. Granata, S. Papirio, G. Esposito, R. Gargano, G. de Marinis, Machine learning algorithms for the forecasting of wastewater quality indicators, Water, 9(2) (2017) 105.
[17] A.A. Nadiri, S. Shokri, F.T.-C. Tsai, A.A. Moghaddam, Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model, Journal of cleaner production, 180 (2018) 539-549.
[18] M. Zeinolabedini, M. Najafzadeh, Comparative study of different wavelet-based neural network models to predict sewage sludge quantity in wastewater treatment plant, Environmental monitoring and assessment, 191(3) (2019) 163.
[19] R.S. Govindaraju, Artificial neural networks in hydrology. I: Preliminary concepts, Journal of Hydrologic Engineering, 5(2) (2000) 115-123.
[20] B. Raheli, M.T. Aalami, A. El-Shafie, M.A. Ghorbani, R.C. Deo, Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River, Environmental Earth Sciences, 76(14) (2017) 503.
[21] V. Vapnik, The Nature of Statistical Learning Theory. Data Mining and Knowledge Discovery, Springer Verlag, New York, 1995.
[22] V. Nourani, Basics of Hydroinformatics, Tabriz University Press. (In Persian), 2015.
[23] J.E. Nash, J.V. Sutcliffe, River flow forecasting through conceptual models part I—A discussion of principles, Journal of hydrology, 10(3) (1970) 282-290.
 
Amirkabir Journal of Civil Engineering
Volume 53, Issue 3
June 2021
Pages 1033-1048

Files

Share

How to cite

Statistics