Preparation of iron-based Fenton-like catalysts using sufficient pyrolysis method: Rapid evaluation of performance, prediction of performance by machine learning, and characterization

表征(材料科学) 催化作用 热解 化学 计算机科学 化学工程 材料科学 纳米技术 工程类 有机化学
作者
Wenyong Ma,Tianxing Li,Subinuer Kadier,Jinxuan Li,Junfeng Yang,Jiayi Li,Zhenguo Chen,Yongxing Chen,Xiaojun Wang
出处
期刊:Results in engineering [Elsevier BV]
卷期号:27: 106610-106610 被引量:2
标识
DOI:10.1016/j.rineng.2025.106610
摘要

• First to utilize the sufficient pyrolysis method to study pyrolysis holding time. • Evaluation of catalyst performance via volatile matter and low-valent iron content. • XGBoost has the best-performing with R 2 of 0.999 in catalyst performance prediction. • The catalysts were used to treat real wastewater superior to many reported systems. Preparation of Fenton sludge into magnetic biochar is a promising direction for resource utilization. In this study, iron-based Fenton-like catalysts were prepared by sufficient pyrolysis with catalyst performance evaluation according to their volatile matter and low-valent iron content. Machine learning models were then applied to predict catalyst performance and elucidated key controlling factors. The best-performing catalyst could remove 82% of TOC and 98% of chroma in treating real MBR effluent of landfill leachate. EDS and XRD analysis results confirmed abundant zero-valent iron loaded on catalyst, and VSM measurements yielded saturation magnetization of 54.89 emu/g, indicating facile magnetic recovery. The volatile matter and low-valent iron content of each catalyst were linearly correlated to the TOC/chroma removal rate with an R 2 approximately 0.82∼0.86, suggesting that the catalyst performance could be roughly assessed. Comparison of SVR and XGBoost models showed that XGBoost provided superior prediction, with R 2 and RMSE values of 0.999 and 0.132 for chroma removal, and 0.999 and 0.149 for TOC removal rate, respectively. Finally, the marginal contribution of the input features to the predicted results of the XGBoost model was quantified and visualized using the Shapley Additive exPlanations Plot, revealing that pyrolysis temperature was the most important factor. These methods and conclusions provide new strategies for the research and application of iron-based Fenton-like catalysts, and are conducive to engineering applications.
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