Authors
1 Department of Chemistry, Faculty of Science, Al Azhar University, Gaza Strip, Palestine
2 Environment Quality Authority, Gaza Strip, Palestine
3 Institute of Water and Environment, Al Azhar University, Gaza Strip, Palestine
Abstract
This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment plant during monthly records. Treatment efficiency of the plant was determined by taking into account of influent input values of pH, temperature (T), biological oxygen demand (BOD), chemical oxygen demand (COD) and total dissolved solids (TSS) with effluent output values of BOD, COD and TSS. Performance of the model was compared via the parameters of root mean squared error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r). The suitable architecture of the neural network model is determined after several trial and error steps. Results showed that the artificial neural network (ANN) performance model was better than the MLR model. It was found that the ANN model could be employed successfully in estimating the BOD, COD and TSS in the outlet of Gaza wastewater treatment plant. Moreover, sensitive examination results showed that influent TSS and T parameters have more effect on BOD, COD and TSS predicting to other parameters.
Keywords
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Gholikandi G.B., Jamshidi S., Hazrati H., Optimization of anaerobic baffled reactor (ABR) using artificial neural network in municipal wastewater treatment, Environmental Engineering and Management Journal 13 (2014) 95-104.
Glorot, X., Bengio Y., Understanding the difficulty of training deep feedforward neural networks. Appearing in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: W&CP 9.
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Guo H., Jeong K., Lim J., Jo J., Kim Y.M., Park J., Kim J.H., Cho K.H., Prediction of effluent concentration in a wastewater treatment plant using machine learning models, Journal of Environmental Sciences 32 (2015) 90-101.
Hack M., Köhne M., Estimation of wastewater process parameters using neural networks, Water Science and Technology 33 (1996) 101–115.
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Nasr M.S., Moustafa M.A.E., Seif H.A.E., El Kobrosy J., Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance- EGYPT, Alexandria Engineering Journal 51 (2012) 37–43.
Neelakantan T.R. Brion T.R., Lingireddy S., Neural network modeling of cryptosporidium and giardia concentrations in Delware River, USA, Water Science and Technology 43 (2001) 125–132.
Oliveira-Esquerre K.M., Seborg D.E., Mori M., Bruns R.E., Application of steady state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill Part II. Nonlinear approaches, Chemical Engineering Journal 105 (2004) 61–69.
Pai T.Y., Chuang S.H., Ho H.H., Predicting performance of grey and neural network in industrial effluent using online monitoring parameters, Process Biochemistry 43 (2008) 199-205.
Pakrou S., Mehrdadi N., Baghvand A., ANN modeling to predict the COD and efficiency of waste pollutant removal from municipal wastewater treatment plants. Current World Environment. 10 (2015) 873-881.
Razi M.A., Athappilly, K., A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models, Expert Systems with Applications 29 (2005) 65–74.
Rounds S.A., Development of a neural network model for dissolved oxygen in the Tualatin River, Oregon, Presented at the Proceedings of the Second Federal Interagency Hydrologic Modeling Conference (Sub-Committee on Hydrology of the Interagency Advisory Committee on Water Information), Las Vegas, Nevada, July 29 to August 1, 2002.
Saen F.R., The use of artificial neural networks for technology selection in the presence of both continuous and categorical data. World Applied Sciences Journal 6 (2009) 1177-1189.
Shomar B., The Gaza Strip: Politics and environment, Water Policy 13 (2011) 28-37.
Talib A., Amat M.I., Prediction of Chemical Oxygen Demand in Dondang River Using Artificial Neural Network, International Journal of Information and Education Technology 2 (2012) 259-261.
Talib A., Abu Hasan Y., Abdul Rahman N.N., Predicting Biochemical Oxygen Demand as Indicator Of River Pollution Using Artificial Neural Networks, present at the World IMACS / MODSIM Congress, Cairns, Australia, July 13-17, 2009.
Tarke P.D., Sarda P.R., Sadgir P.A., Performance of ANNs for Prediction of TDS of Godavari River, India, International Journal of Engineering Research, 5 (2016) 115-118.
Vyas M., Modhera B., Vyas V., Performance forecasting of common effluent treatment plant parameters by artificial neural network, ARPN, Journal of Engineering and Applied Sciences 6 (2011) 38-42.
Yordanova S., Petrova R., Noykova N., Tzvetkov P., Neuro-fuzzy modeling in anaerobic wastewater treatment for prediction and control, International Journal of Computing 5 (2014) 51-56.
Zare A.H., Bayat V.M., Daneshkare A.P., Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models, International Agrophysics 25 (2011) 187-192.
Zhang R., Hu, X., Effluent quality prediction of wastewater treatment system based on small-world ANN, Journal of Computers 7 (2012) 2136-2143.
Zhua J., Zurcher J., Raoc M., An on-line wastewater quality predication system based on a time-delay neural network, Engineering Applications of Artificial Intelligence 11 (1998) 747-758.
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