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

Karadurnius E., GÖZ E., Taskin N., YÜCEER A. M.

SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, cilt.38, sa.4, ss.2145-2153, 2020 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 4
  • Basım Tarihi: 2020
  • Dergi Adı: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Academic Search Premier, Directory of Open Access Journals
  • Sayfa Sayıları: ss.2145-2153
  • Anahtar Kelimeler: Drinking water treatment, bromate removal, artificial intelligence, LS-SVM, ACTIVATED CARBON, ION
  • Ankara Üniversitesi Adresli: Evet

Özet

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 gamma and sigma(2) 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.