Department of Environmental Engineering, Faculty of Environment, University of Tehran
Abstract
Intelligent model optimization is a key factor in water treatment improvement. In current study, we applied the artificial neural networks modelling for the optimization of coagulation and flocculation processes to get sufficient water quality control over the total organic carbon parameter. ANN network consisted of a multilayer feed forward structure with backpropagation learning algorithm with the output layer of ferric chloride and cationic polymer dosages. The results were simultaneously compared with the nonlinear multiple regression model. Model validation phase performed using 94 unknown samples for which the prediction result was in good agreement with the observed values. Analysis of the results showed a determination coefficient of 0.85 for cationic polymer and 0.97 for ferric chloride models. Mean absolute percentage error and root mean square errors were calculated consequently as 5.8% and 0.96 for polymer and 3.1% and 1.97 for ferric chloride models. According to the results, artificial neural networks showed to be very promising for the optimization of water treatment processes.
Beckett R, Ranville J. Natural organic matter. In: Newcombe G Dixon D (eds) Interface Science in Drinking Water Treatment, Elsevier Ltd.; 2006. p. 376.
Hou Y, Chu W, Ma M. Carbonaceous and nitrogenous disinfection by products formation in the surface and ground water treatment plants using Yellow River as water source. Journal of Environmental Sciences; 2012; 24(7):1204â1209.
Matilainen A, Vepsalainen M, Sillanpaa M. Naturak organic matter removal by coagulation during drinking water treatment; a review. Advances in Colloid and Interface Science; 2010; 159:189-197.
Khataee A R, Zarei M, Pourhassan M. Bioremediation of Malachite Green from Contaminated Water by Three Microalgae: Neural networks Modelling. CLEAN â Soil Air Water; 2010; 38(1):96â103.
Yetilmezsoy K, Ozkaya B, Cakmakci M. Artificial intelligenceâbased prediction models for environmental engineering. Neural networks World; 2011; 11(3):193â218.
Baxter C W, Stanley S J, Zhang Q, Smith D W. Developing artiïcial neural network models of water treatment processes: a guide for utilities, Journal of Environmental Engineering and Science; 2001; 1:201â211.
Mirsepassi A. Application of Intelligent System for Water Treatment Plant Operation, Journal of Environmental Health Science and Engineering; 2004; 1 (2): 51-57.
Sengul A B, Gormez Z. Prediction of optimal coagulant dosage in drinking water treatment by artificial neural network, 1st EWaS-MED International Conference, Thessaloniki, Greece; 2013.
Volk C, Kaplan L A, Robinson J, Johnson B, Wood L, Zhu H W, Le Chevallier M. Fluctuations of dissolved organic matter in river used for drinking water and impacts on conventional treatment plant performance. Environmental Science and Technology; 2005; 39(11):4258â4264.
OrtizâRodriguez J M, MartinezâBlanco M R, Viramontes JMC, VegaâCarrillo H R. Robust Design of Artificial Neural Networks Methodology in Neutron Spectrometry. In: Suzuki K (ed) Artificial Neural networksâArchitectures and Applications. InTech, Rijeka; 2013. pp. 83-111.
Najah A, Elshafie A, Karim O A, Jaffar O. Prediction of Johor River Water Quality Parameters Using Artificial Neural Networks. European Journal of Scientific Research; 2009; 28(3):422-435.
Palani S, Liong S, Tkalich P. An ANN application for water quality forecasting. Marine Pollution Bulletin; 2008; 56(9):1586â1597.
Gemperline P. Practical guide to chemometrics 2nd edn. CRC, Boca Raton; 2006. p. 552.
Ahmadzadeh Kokya T, Farhadi Kh, AliMohammad Kalhori A. Optimized Dispersive LiquidâLiquid Microextraction and Determination of Sorbic Acid and Benzoic Acid in Beverage Samples by Gas Chromatography, Food Anal. Methods; 2012; 5:351â358.
Nissen S, et al. FANN. Fast Artificial Neural Network Library, http://leenissen.dk, (assessed July 23, 2015).
Lin T Y, Tseng C H. Optimum design for artificial neural networks: an example in a bicycle derailleur system. Journal of engineering applications of artificial intelligence; 2000; 13:3â14.
Ahmadzadeh, T., Mehrdadi, N., Ardestani, M., & Baghvand, A. (2016). Intelligent optimization of common water treatment plant for the removal of organic carbon. Environmental Sciences, 14(1), 1-8.
MLA
Taher Ahmadzadeh; Naser Mehrdadi; Mojtaba Ardestani; Akbar Baghvand. "Intelligent optimization of common water treatment plant for the removal of organic carbon", Environmental Sciences, 14, 1, 2016, 1-8.
HARVARD
Ahmadzadeh, T., Mehrdadi, N., Ardestani, M., Baghvand, A. (2016). 'Intelligent optimization of common water treatment plant for the removal of organic carbon', Environmental Sciences, 14(1), pp. 1-8.
VANCOUVER
Ahmadzadeh, T., Mehrdadi, N., Ardestani, M., Baghvand, A. Intelligent optimization of common water treatment plant for the removal of organic carbon. Environmental Sciences, 2016; 14(1): 1-8.
)