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Experimental Investigation and Proposal of Artificial Neural Network Models of Lead and Cadmium Heavy Metal Ion Removal from Water Using Porous Nanomaterials

Author

Listed:
  • Atef El Jery

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Moutaz Aldrdery

    (Department of Chemical Engineering, College of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

  • Naoufel Ghoudi

    (Laboratory: Applied Thermodynamics, Engineers National School of Gabes, Gabes University, Gabes 6029, Tunisia)

  • Mohammadreza Moradi

    (Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15289, USA)

  • Ismat Hassan Ali

    (Department of Chemistry, College of Science, King Khalid University, Abha 61231, Saudi Arabia)

  • Hussam H. Tizkam

    (Pharmacy Department, Al Safwa University College, Karbala 56001, Iraq)

  • Saad Sh. Sammen

    (Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah 10047, Iraq)

Abstract

This study used porous nanomaterials MCM-41 and SBA-15, as well as their modified species, to remove lead and cadmium ions from water. We used X-ray diffraction (XRD), a scanning electron microscope (SEM), the Brunauer–Emmett–Teller (BET), and the Fourier transform infrared (FT-IR) method to investigate the characteristics of porous nanomaterials. Additionally, atomic absorption spectroscopy (AAS) measured the concentration of lead and cadmium ions. The stratigraphic analysis showed the samples’ isothermal shape to be type IV. This study investigated the amount, absorbent, pH changes, and adsorption time parameters. We observed that the adsorption efficiency of lead by the synthesized samples was higher than that of the adsorption of cadmium. Mesoporous structures also displayed increased adsorption efficiency due to the amino group. Four testing stages were conducted to determine the reproducibility of the adsorption by the synthesized samples, with the results showing no significant changes. As a result of the adsorption process, the structure of the recycled sample N H 2 - M C M - 41 was preserved. We also used artificial neural networks (ANN) to propose predictive models based on the experimental results. The ANN models were very accurate, such that the mean absolute error (MAE) was less than 2% and the R 2 was higher than 0.98.

Suggested Citation

  • Atef El Jery & Moutaz Aldrdery & Naoufel Ghoudi & Mohammadreza Moradi & Ismat Hassan Ali & Hussam H. Tizkam & Saad Sh. Sammen, 2023. "Experimental Investigation and Proposal of Artificial Neural Network Models of Lead and Cadmium Heavy Metal Ion Removal from Water Using Porous Nanomaterials," Sustainability, MDPI, vol. 15(19), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14183-:d:1247513
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    References listed on IDEAS

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    1. Mariana Mariana & Farid Mulana & Lisa Juniar & Dinda Fathira & Risna Safitri & Syawaliah Muchtar & Muhammad Roil Bilad & Amir Husni Mohd Shariff & Nurul Huda, 2021. "Development of Biosorbent Derived from the Endocarp Waste of Gayo Coffee for Lead Removal in Liquid Wastewater—Effects of Chemical Activators," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
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