太阳能驱动的光催化剂从水中去除素:通过机器学习和自然启发的算法进行过程优化
Sara Zeghbib1, Noureddine Nasrallah1, Haroun Hafsa1
1Laboratory of Reaction Engineering, Faculty of Mechanical Engineering and Process Engineering, USTHB BP 32 Algiers 16111 Algeria.
RSC advances
|September 22, 2025
概括
氧化物纳米颗粒使用太阳能有效降解水中的抗生素Cefuroxime (CFX). 机器学习优化条件实现了84.25%的CFX去除,提供了可持续的水处理解决方案.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 水生环境中的抗生素构成生态风险,并促进抗生素耐药性.
- 素 (CFX) 是一种广泛使用的抗生素,经常在废水中检测到.
- 有效地去除CFX对于环境保护和公共健康至关重要.
研究的目的:
- 在太阳辐射下使用氧化物 (Ag-ZnO) 纳米颗粒用于研究Cefuroxime (CFX) 的光催化降解.
- 为了合成和表征Ag-ZnO纳米颗粒具有不同银兴奋剂百分比.
- 使用机器学习算法和以自然为灵感的优化优化降解过程.
主要方法:
- 通过sol-gel方法合成的Ag-ZnO纳米颗粒,使用不同的Ag兴奋剂 (1-3重量%).
- 使用XRD,SEM-EDS,ATR-FTIR和UV-Vis光谱学进行表征.
- 在太阳辐射下进行光催化降解实验,动力分析和清洁者测试.
- 使用决策树最小平方提升 (DT_LSBOOST) 和龙算法 (DA) 的优化.
主要成果:
- 2重%的Ag-ZnO显示出最高的CFX降解效率,这是由于减少了电子孔重组和增强可见光吸收.
- 波段间隙从3.15 eV (ZnO) 减少到3.01 eV (2%的Ag-ZnO).
- CFX降解遵循伪第一阶动力学,其中基基 (̇OH) 作为主要的反应物种.
- 优化条件 (pH6.11,0.1g L-1催化剂,50mg L-1CFX,180分钟) 产生了84.25%的降解.
- DT_LSBOOST模型实现了高预测精度 (R > 0.9996) 和低RMSE (< 0.88).
结论:
- Ag-ZnO纳米粒子是阳光下CFX降解的有效光催化剂.
- 机器学习和以自然为灵感的算法可以优化水处理的光催化过程.
- 这项研究为开发可持续的基于半导体的水利技术提供了框架.
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