Abstract
This study focuses on the use of chemically modified steel dust-based adsorbent to remove Eriochrome Black-T (EBT) and Tetracycline (TC) from water. The adsorption isotherm, kinetic modeling, thermodynamic and regeneration studies were conducted. The adsorption process was modeled using Artificial Neural Network. The adsorption kinetics were well fitted by a pseudo-second-order kinetic model and Redlich-Peterson model. The maximum adsorption capacity for TC was 19.78 mg.g−1 (Mod-AL) and 14.37 mg.g−1 (ModAR), and for EBT was 90.71 mg.g−1 (Mod-AL) and 119.02 mg.g−1 (Mod-AR). The thermodynamic evaluation indicated the adsorption was endothermic, spontaneous, and did not cause modification onto an adsorbent surface. The artificial neural based network has predicted the adsorption capacity at equilibrium with a high degree of accuracy quantified with minimum mean squared error of 1.2240 and coefficient of determination of 0.9990. FTIR spectra of free and dye adsorbed adsorbents revealed a slight shift in the peak positions, indicative of the interaction of adsorbents functionalities with the EBT and TC. The XRD spectra of Mod-AL and Mod-AR exhibited fewer sharp peaks signals, suggesting the amorphous nature of these adsorbents. The thermogravimetric analysis (TGA) of these adsorbents revealed their higher thermal stablity, with less than 10 % weight loss up to heating to 900 °C.
| Original language | English |
|---|---|
| Article number | 120559 |
| Journal | Separation and Purification Technology |
| Volume | 287 |
| DOIs | |
| State | Published - 15 Apr 2022 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier B.V.
Keywords
- Artificial Neural Networks
- Chemical modification
- Iron industry
- Steel slag
- Water treatment
ASJC Scopus subject areas
- Analytical Chemistry
- Filtration and Separation
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