Artificial neural networks approach for zeta potential of Montmorillonite in the presence of different cations


YÜKSELEN AKSOY Y., ERZİN Y.

ENVIRONMENTAL GEOLOGY, vol.54, no.5, pp.1059-1066, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 54 Issue: 5
  • Publication Date: 2008
  • Doi Number: 10.1007/s00254-007-0872-x
  • Journal Name: ENVIRONMENTAL GEOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1059-1066
  • Keywords: artificial neural networks, contamination, heavy metals, montmorillonite, zeta potential, COMPLEX PERMITTIVITY, SOIL, CONTAMINATION, PREDICTION, REMOVAL
  • Dokuz Eylül University Affiliated: No

Abstract

In this study, the zeta potential of montmorillonite in the presence of different chemical solutions was modeled by means of artificial neural networks (ANNs). Zeta potential of the montmorillonite was measured in the presence of salt cations, Na+, Li+ and Ca2+ and metals Zn2+, Pb2+, Cu2+, and Al3+ at different pH values, and observed values pointed to a different behavior for this mineral in the presence of salt and heavy metal cations. Artificial neural networks were successfully developed for the prediction of the zeta potential of montmorillonite in the presence of salt and heavy metal cations at different pH values and ionic strengths. Resulting zeta potential of montmorillonite shows different behavior in the presence of salt and heavy metal cations, and two ANN models were developed in order to be compared with experimental results. The ANNs results were found to be close to experimentally measured zeta potential values. The performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance account for were used to control the performance of the prediction capacity of the models developed in this study. These indices obtained make it clear that the predictive models constructed are quite powerful. The constructed ANN models exhibited a high performance according to the performance indices. This performance has also shown that the ANNs seem to be a useful tool to minimize the uncertainties encountered during the soil engineering projects. For this reason, the use of ANNs may provide new approaches and methodologies.