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Neural Network Modelling for Prediction of Zeta Potential

Author

Listed:
  • Roman Marsalek

    (Department of Chemistry, Faculty of Science, University of Ostrava, 30. Dubna 22, 701 03 Ostrava, Czech Republic)

  • Martin Kotyrba

    (Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 701 03 Ostrava, Czech Republic)

  • Eva Volna

    (Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 701 03 Ostrava, Czech Republic)

  • Robert Jarusek

    (Department of Informatics and Computers, Faculty of Science, University of Ostrava, 30. Dubna 22, 701 03 Ostrava, Czech Republic)

Abstract

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

Suggested Citation

  • Roman Marsalek & Martin Kotyrba & Eva Volna & Robert Jarusek, 2021. "Neural Network Modelling for Prediction of Zeta Potential," Mathematics, MDPI, vol. 9(23), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3089-:d:691802
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    References listed on IDEAS

    as
    1. Jiang, Yu & Sulgani, Mohsen Tahmasebi & Ranjbarzadeh, Ramin & Karimipour, Arash & Nguyen, Truong Khang, 2019. "Hybrid GMDH-type neural network to predict fluid surface tension, shear stress, dynamic viscosity & sensitivity analysis based on empirical data of iron(II) oxide nanoparticles in light crude oil mixt," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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