深度学习 - - 以催化剂为抗生素降解剂的导向优化
Siyuan Jiang1, Shengwen Zhou1, Ce Wang1
1Key Laboratory for Environmental Pollution Prediction and Control, Gansu Province, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, PR China.
Environmental research
|September 17, 2025
概括
这项研究使用机器学习和优化算法来增强从水中去除抗生素. 开发的模型准确地预测了退化速度,并确定了关键因素,有助于为更清洁的水生环境开发催化剂.
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污染是一个日益严重的全球性问题.
- 先进的氧化过程 (AOP) 显示出抗生素去除的希望,但需要优化.
- 开发高效的无机催化剂对于有效的AOP至关重要.
研究的目的:
- 整合机器学习和优化算法,以改善AOP中的抗生素去除.
- 加快开发用于环境修复的新型无机催化剂.
- 确定影响抗生素降解机制的关键因素.
主要方法:
- 来自207篇研究论文的数据预处理和探索性数据分析.
- 应用TabNet深度学习模型用于分类和回归任务.
- 利用Sparrow搜索算法 (SSA) 来优化实验条件.
- 基于的催化剂 (Co-CuO,Co3O4,CoFe2O4) 的合成和测试.
- 使用夏普利添加式扩展 (SHAP) 分析解释模型预测.
主要成果:
- 在TabNet模型中,分类准确度达到82.02%,回归的R2为0.96.
- 搜索算法确定了抗生素降解的最佳实验条件.
- 对于催化剂降解率的模型预测在2%的误差范围内.
- SHAP分析有效地区分了自由基和非自由基降解机制.
结论:
- 综合机器学习和优化方法显著提高了AOP中的抗生素去除效率.
- 开发的模型准确地预测了催化剂的性能,并有助于设计新材料.
- 这项研究为与抗生素污染有关的环境工程挑战提供了一个新的理论框架和实际解决方案.
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