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

Atef El Jery, Moutaz Aldrdery, Naoufel Ghoudi, Mohammadreza Moradi, Ismat Hassan Ali (), Hussam H. Tizkam and Saad Sh. Sammen ()
Additional contact information
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

Sustainability, 2023, vol. 15, issue 19, 1-14

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.

Keywords: heavy metals; adsorption; artificial neural networks; machine learning; mesoporous; amino group (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/19/14183/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/19/14183/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:19:p:14183-:d:1247513

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().