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http://dx.doi.org/10.12989/mwt.2018.9.5.363

Modeling of Co(II) adsorption by artificial bee colony and genetic algorithm  

Ozturk, Nurcan (Department of Civil Engineering, Faculty of Technology, Karadeniz Technical University)
Senturk, Hasan Basri (Department of Chemistry, Faculty of Science, Karadeniz Technical University)
Gundogdu, Ali (Department of Food Engineering, Faculty of Engineering and Natural Science, Gumushane University)
Duran, Celal (Department of Chemistry, Faculty of Science, Karadeniz Technical University)
Publication Information
Membrane and Water Treatment / v.9, no.5, 2018 , pp. 363-371 More about this Journal
Abstract
In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.
Keywords
adsorption; artificial bee colony algorithm; modeling; genetic algorithm;
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Times Cited By KSCI : 4  (Citation Analysis)
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