Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks

Karadurmus E., Taskin N., Goz E., Yuceer M.

OZONE-SCIENCE & ENGINEERING, vol.41, no.2, pp.118-127, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 2
  • Publication Date: 2019
  • Doi Number: 10.1080/01919512.2018.1510763
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.118-127
  • Keywords: Adsorption, artificial neural network (ANN), bromate removal, disinfection of drinking water, ozone, DISINFECTION BY-PRODUCTS, ECOLOGICAL STATUS, ACTIVATED CARBON, SURFACE-TENSION, RIVER, ION, PERFORMANCE, REDUCTION, OZONATION, QUALITY
  • Inonu University Affiliated: Yes


In treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH3-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer-Emmett-Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. 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. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase.