通过自动化知识提取和机器学习加速对水处理的催化过程优化.
Siyuan Jiang1, Ying Yang1, Ziang Liu1
1Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu Province 730000, PR China.
Journal of hazardous materials
|February 4, 2026
概括
使用大型语言模型进行水处理的自动化催化剂发现速度比手动审查快270倍. 这种数据驱动的方法加速了有效的先进氧化过程 (AOP) 催化剂的识别.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 传统的水处理催化剂发现受到手册文献审查的阻碍,导致数据采集瓶.
- 开发高效的催化剂对于推进水处理技术至关重要.
研究的目的:
- 开发一条自动化管道,用于在水处理中有效地发现催化剂.
- 通过数据驱动的方法来加速环境催化材料的优化.
主要方法:
- 开发了一种集高保真性文档解析和大型语言模型的自动化管道.
- 从3000多份关于高级氧化过程 (AOP) 的出版物中构建了一个包含3276个记录的综合数据库.
- 在精选的数据上训练了一个多阶段优化的XGBoost模型.
主要成果:
- 与手动方法相比,自动化管道的效率大约是手动方法的270倍.
- 在XGBoost模型中,降解速率常数达到0.6753的R2值,去除效率为0.8351.
- 三种有前途的催化剂 (Co3O4@BC,Fe3O4@GO和CoFe2O4) 被确定并经过实验验证,误差为<4%.
结论:
- 数据驱动的方法极大地加速了环境催化剂的催化剂发现和材料优化.
- 开发的管道和数据库为水处理研究人员提供了宝贵的资源.
- 自动化方法可以克服传统的瓶,将研究重点从数据收集转移到创新.
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