Degradation of textile dye in industrial wastewater effluent and deionised water matrices using a Fenton-like Fe-BiOCl catalyst: synergistic effects and machine learning study

The Fenton-like reaction has attracted increasing attention in recent years due to the persistent release of organic pollutants from textile industries. Herein, Fe-doped Bi12O17Cl2 (BiOCl) hierarchical catalysts were synthesised for photo-assisted Fenton degradation of textile dyes. Incorporation of iron species significantly enhanced degradation efficiency. For example, Fe-BiOCl-3 (k = 0.085 min−1) exhibited 2.5-fold higher performance than undoped BiOCl. The optimised Fe-BiOCl-3 catalyst, with moderate Fe loading (0.26 wt%), achieved degradation efficiencies of 99%, 74%, 99%, 97%, and 73.8% for rhodamine B, Bismarck brown, Congo red, indigo carmine, and orange G, respectively. The TOC removal of the representative rhodamine B dye in deionised water (72.5%) was higher than in stream water (42.8%) and industrial wastewater (53.6%), suggesting that water composition influences Fenton oxidation rates. The activity of Fe-BiOCl-3 in the absence of light (classical Fenton) or H2O2 (photocatalysis) was very slow. The enhanced catalytic performance arises from the synergy between Fenton oxidation and the photocatalytic process, where photogenerated electrons facilitate the conversion of Fe3+ to Fe2+, resulting in rapid H2O2 decomposition to generate ·OH radicals. Moreover, quenching experiments revealed that ·OH and ·O2 were the main active species in the degradation process. Five machine learning models were trained to predict the degradation efficiency and the effects of process parameters in the Fenton-like process. Among ML models, the Random Forest model demonstrated the best performance, strong generalisation, and mechanistic consistency with the underlying photo-assisted Fenton process. The outcomes collectively demonstrated that the combined experimental–computational approach provided synergistic insights into the performance, mechanistic behaviour, and process optimisation of the Fe-BiOCl/H2O2/visible light system for practical wastewater treatment applications.

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Publication Details

Journal
Chemical Papers
Published
2026-06-29
DOI
https://doi.org/10.1007/s11696-026-05159-9
Primary Topic
Advanced oxidation water treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

Degradation of textile dye in industrial wastewater effluent and deionised water matrices using a Fenton-like Fe-BiOCl catalyst: synergistic effects and machine learning study

Hambali Umar Hambali, Osemeikhian Ogbeifun, Ahmad Abulfathi Umar, Michael O. Daramola et al.
Chemical Papers
Advanced oxidation water treatment
article

Degradation of textile dye in industrial wastewater effluent and deionised water matrices using a Fenton-like Fe-BiOCl catalyst: synergistic effects and machine learning study

Hambali Umar Hambali, Osemeikhian Ogbeifun, Ahmad Abulfathi Umar, Michael O. Daramola, Samuel A. Iwarere, Shepherd M. Tichapondwa
article en

Abstract

The Fenton-like reaction has attracted increasing attention in recent years due to the persistent release of organic pollutants from textile industries. Herein, Fe-doped Bi12O17Cl2 (BiOCl) hierarchical catalysts were synthesised for photo-assisted Fenton degradation of textile dyes. Incorporation of iron species significantly enhanced degradation efficiency. For example, Fe-BiOCl-3 (k = 0.085 min−1) exhibited 2.5-fold higher performance than undoped BiOCl. The optimised Fe-BiOCl-3 catalyst, with moderate Fe loading (0.26 wt%), achieved degradation efficiencies of 99%, 74%, 99%, 97%, and 73.8% for rhodamine B, Bismarck brown, Congo red, indigo carmine, and orange G, respectively. The TOC removal of the representative rhodamine B dye in deionised water (72.5%) was higher than in stream water (42.8%) and industrial wastewater (53.6%), suggesting that water composition influences Fenton oxidation rates. The activity of Fe-BiOCl-3 in the absence of light (classical Fenton) or H2O2 (photocatalysis) was very slow. The enhanced catalytic performance arises from the synergy between Fenton oxidation and the photocatalytic process, where photogenerated electrons facilitate the conversion of Fe3+ to Fe2+, resulting in rapid H2O2 decomposition to generate ·OH radicals. Moreover, quenching experiments revealed that ·OH and ·O2 were the main active species in the degradation process. Five machine learning models were trained to predict the degradation efficiency and the effects of process parameters in the Fenton-like process. Among ML models, the Random Forest model demonstrated the best performance, strong generalisation, and mechanistic consistency with the underlying photo-assisted Fenton process. The outcomes collectively demonstrated that the combined experimental–computational approach provided synergistic insights into the performance, mechanistic behaviour, and process optimisation of the Fe-BiOCl/H2O2/visible light system for practical wastewater treatment applications.

Chemical Papers
King Fahd University of Petroleum and Minerals (SA), University of Ilorin (NG), University of Pretoria (ZA)
University of Pretoria
Clean water and sanitation
Openalex Percentile: Top 14%
Advanced oxidation water treatment
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