数字农科院2.0

Advanced machine learning modelling and experimental analysis of ortho-nitrophenol degradation during UV254/chloramine treatment in the presence of Fe(III)

文献类型： 外文期刊

作者： Muhammad Asif;Lin Deng;Hidayat Ullah Khan;Changbo Zhang;Rajendra Prasad Singh;Gongde Wu

作者机构：

关键词： DFT;Ecotoxicity;Ortho-nitrophenol;Regression model;UV254/chloramine/Fe(III)

期刊名称： Journal of Water Process Engineering

ISSN： 2214-7144

年卷期： 2025 年 74 卷

页码：

收录情况： SCIE（2025版）

摘要： UV254/chloramine (UV254/NH2Cl) efficiently degrades recalcitrant contaminants in the wastewater, while Fe(III) notably impacts the performance of the advanced oxidation treatment. This study investigated the combined and separate effects of UV254, chloramine, and Fe(III) on ortho-nitrophenol degradation under varying influencing factors experimentally and computationally. The degradation of ortho-nitrophenol was enhanced from 33 % to 85 % during the UV254/chloramine treatment via OH• (54.24 %) and RCSs (31.06 %) with the addition of Fe(III). Beyond the optimum level, increasing the oxidant concentration inhibited ortho-nitrophenol degradation. Among the four models, the Gradient Boosting and XGBoost models showed the best prediction performance. Possible degradation pathways of ortho-nitrophenol were proposed based on the identification of intermediate products (IPs) and density functional theory (DFT) calculations. The ECOSAR model predicted the ecotoxicity of ortho-nitrophenol and possible intermediate products. Finally, the formation of HNMs and the associated toxicity were explored. The UV254/chloramine/Fe(III) treatment demonstrated significantly lower electrical energy per order (EE/O, as low as 2.02 kWh/m3/order), highlighting its superior energy efficiency compared to other advanced oxidation processes for nitrophenol degradation. Overall, the integration of Fe(III) into the UV254/chloramine system significantly enhanced ortho-nitrophenol degradation efficiency (from 33 % to 85 %) by promoting •OH and RCS generation, while the use of advanced machine learning and DFT modelling provided a reliable predictive framework for treatment optimization and ecotoxicological risk assessment.

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