Erdal KARADURMUŞ, Eda GÖZ, Nur TAŞKIN, Mehmet YÜCEER

BROMATE REMOVAL PREDICTION IN DRINKING WATER BY USING THE LEAST SQUARES SUPPORT VECTOR MACHINE (LS-SVM)

The main objective of this study was to develop Least Squares Support Vector Machine (LS-SVM) algorithm for prediction of bromate removal in drinking water. Adsorption method known as environmental-friendly and economical was used in the experimental part of this study to remove this harmful compound from drinking water. Technically (pure), HCl-, NaOH- and NH3-modified activated carbons were prepared as adsorbent. Experimental studies were carried out with synthetic samples in three different concentrations. To forecast bromate removal percentage particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables Radial basis kernel function was selected as activation function in algorithm. Algorithm parameters that γ and σ2 values set as 415 and 3.956 respectively. To evaluate model performance some performance indices were calculated. Correlation coefficient (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE) value for the training and testing phase R:0.996, MAPE%: 2.59 RMSE: 2.14 and R:0.994, MAPE%: 3.21 RMSE: 2.51 respectively. These results obtained from this study were compared with the ANN model previously developed with the same input data. As a result, LS-SVM has better performance than ANN.

Keywords:

Drinking water treatment, bromate removal, artificial intelligence, LS-SVM,

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[1] West D.M., Mu R., Gamagedara S., Ma Y., Adams C., Eicholz T., Burken J.G., Shi H., (2015). Simultaneous Detection of Perchlorate and Bromate Using Rapid High-Performance Ion Exchange Chromatography-Tandem Mass Spectrometry and Perchlorate Removal in Drinking Water, Environmental Science and Pollution Research 22, 8594–8602.
[2] Xiao Q., Yu S., Li L., Wang T., Liao X., Ye, Y., (2017). An Overview of Advanced Reduction Process for Bromate Removal from Drinking Water: Reducing Agents, Activation Methods, Application and Mechanisms, Journal of Hazardous Materials 324, 230–240.
[3] Naushad M., Khan M.R., Alothman Z.A., AlSohaimi I., Rodriguez-Rienoso F., Turki T.M., Ali, R., (2015). Removal of Bro3 −from Drinking Water Samples using Newly Developed Agricultural Waste-Based Activated Carbon and its Determination by Ultra-Performance Liquid Chromatography-Mass Spectrometry, Environmental Science and Pollution Research, 22, 15853–15865.
[4] Bao M.L., Griffini O., Santianni D., Barbieri K., Burrini D., Pantani, F., (1999). Removal of Bromate Ion from Water using Granular Activated Carbon, Water Research 33, 2959–2970.
[5] Wang L., Zhang J., Liu, J., He H., Yang M., Yu J., Ma Z., Jiang F., (2010). Removal of Bromate Ion using Powdered Activated Carbon, Journal of Environmental Sciences 22, 1846–1853.
[6] Chen F., Zhang Z., Li Q. Wang H., (2012). Adsorption of Bromate and Competition from Oxyanions on Cationic Surfactant-Modified Granular Activated Carbon (GAC), Chemical Engineering Journal 203, 319–325.
[7] Zhang Y., Wu Q., Zhang J., Yang X. (2015). Removal of Bromide and Bromate from Drinking Water using Granular Activated Carbon, Journal of Water Health, 13, 73–78.
[8] Hong S., Deng S., Yao X., Wang B., Wang Y., Huang J., Yu G., (2016). Bromate Removal from Water by Polypyrrole Tailored Activated Carbon, Journal of Colloid and Interface Science 467, 10-16.
[9] Civelekoğlu G., Yigit N.O., Diadopolous E. Kitis M., (2007). Prediction of Bromate Formation Using Multi-Linear Regression and Artificial Neural Networks, Ozone: Science&Engineering 29, 353–362.
[10] Kulkarni P., Chellam, S., (2010). Disinfection by-product Formation Following Chlorination of Drinking Water: Artificial Neural Network Models and Changes in Speciation With Treatment, Science of the Total Environment 408, 4202–4210.
[11] Karadurmuş E., Taşkın N., Göz E., Yüceer M., (2019). Prediction of Bromate Removal in Drinking Water using Artificial Neural Networks, Ozone: Science & Engineering 41, 118-127.
[12] Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J., (2002). Least Squares Support Vector Machines. World Scientific, Singapore, Australia.
[13] Güneşoğlu S., Yüceer M., (2018). A Modeling Study of Micro-Cracking Processes of Polyurethane Coated Cotton Fabrics, Textile Research Journal 88, 2766-2781.