ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

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, Shepherd M. Tichapondwa, Michael O. Daramola, and Samuel A. Iwarere

Department of Chemical Engineering, Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, Hatfield, Pretoria, South Africa

E-mail: hambaliuh313@gmail.com

Received: 19 January 2026  Accepted: 2 June 2026

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.

Graphical abstract

Keywords: Textile dye; Machine learning; Fe-BiOCl; Fenton-like process; Industrial wastewater treatment; Random Forest model

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-026-05159-9

Chemical Papers 80 (11) 12955–12976 (2026)