The Use of Neural Networks for the Prediction of Zeta Potential of Kaolinite |
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Authors: | Yusuf Erzin Yeliz Yukselen |
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Institution: | (1) Faculty of Engineering, Department of Civil Engineering, Dokuz Eylul University, 35160 Buca, İzmir, Turkey;(2) Faculty of Engineering, Department of Civil Engineering, Celal Bayar University, 45140 Manisa, Turkey |
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Abstract: | The sign and the magnitude of the zeta potential must be known for many engineering applications. For clay soils, it is usually
negative, but it is strongly dependent on the pore fluid chemistry. However, measurement of zeta potential time is time-consuming
and requires special and expensive equipment. In this study, the prediction of zeta potential of kaolinite has been investigated
by artificial neural networks (ANNs) and multiple regression analyses (MRAs). To achieve this, ANN and MRA models based on
zeta potential measurements of kaolinite in the presence of salt and heavy metal cations at different pH values have been
developed. The results of the models were compared with the experimental results. The performance indices, including coefficient
of determination, root mean square error, mean absolute error, and variance, were used to assess the performance of the prediction
capacity of the models developed in this study. The obtained indices make it clear that the constructed ANN models were able
to predict zeta potential of kaolinite quite efficiently and outperformed the MRA models. Results showed that ANN models can
be used satisfactorily to predict zeta potential of kaolinite as a rapid inexpensive substitute for laboratory techniques. |
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