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Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective.
- Source :
-
Chemosphere . Mar2024, Vol. 352, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and sustainable process. Herein, we focus on optimizing the photocatalytic degradation of 2,6-DCP using artificial intelligence modeling, aiming at minimizing initial capital outlay and ongoing operational expenses. Hence, Fe/Zn@biochar, a novel material, was synthesized, characterized, and applied to harness the dual capabilities of 2,6-DCP adsorption and degradation. Fe/Zn@biochar exhibited an adsorption energy of −21.858 kJ/mol, effectively capturing the 2,6-DCP molecules. This catalyst accumulated photo-excited electrons, which, upon interaction with adsorbed oxygen and/or dissolved oxygen generated •O 2 −. The •OH radicals could also be produced from h+ in the Fe/Zn@biochar valence band, cleaving the C–Cl bonds to Cl− ions, dechlorinated byproducts, and phenols. An artificial neural network (ANN) model, with a 4-10-1 topology, " trainlm " training function, and feed-forward back-propagation algorithm, was developed to predict the 2,6-DCP removal efficiency. The ANN prediction accuracy was expressed as R2 = 0.967 and mean squared error = 5.56e–22. The ANN-based optimized condition depicted that over 90% of 2,6-DCP could be eliminated under C 0 = 130 mg/L, pH = 2.74, and catalyst dosage = 168 mg/L within ∼4 h. This optimum condition corresponded to a total cost of $7.70/m3, which was cheaper than the price estimated from the unoptimized photocatalytic system by 16%. Hence, the proposed ANN could be employed to enhance the 2,6-DCP photocatalytic degradation process with reduced operational expenses, providing practical and cost-effective solutions for petrochemical wastewater treatment. [Display omitted] • Fe/Zn@biochar was synthesized for 2,6-dichlorophenol photocatalytic degradation. • Adsorption energy of Fe/Zn@biochar (−22 kJ/mol) was utilized for 2,6-DCP removal. • ANN model was developed to optimize factors affecting 2,6-DCP photodegradation. • Over 90% of 2,6-DCP could be eliminated at initial concentration of 130 mg/L. • Application of ANN (4-10-1) model reduced photocatalytic treatment cost by 15.6%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00456535
- Volume :
- 352
- Database :
- Academic Search Index
- Journal :
- Chemosphere
- Publication Type :
- Academic Journal
- Accession number :
- 175848713
- Full Text :
- https://doi.org/10.1016/j.chemosphere.2024.141476