数据增强机器学习改善了水处理设计:精确预测PPCP与活性基的反应
Jiaqi Wu1, Yanzhou Ding1, Chengfei Zhu1
1State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, 200237 Shanghai, China.
Water research
|December 31, 2025
概括
使用变化自编码器 (VAE) 的新数据增强框架改进了机器学习模型,用于预测废水处理中的极端污染物反应. 这种方法提高了准确性,并为流程优化提供了关键的分子见解.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 激素介导的先进氧化/减少过程 (AOP/ARP) 对于废水污染物降解是有效的.
- 准确的动力预测和机械理解对于优化AOP/ARP至关重要.
- 机器学习 (ML) 模型经常与小数据集扎,导致性能和解释差.
研究的目的:
- 开发一种使用变量自编码器 (VAE) 的新型数据增强框架,以克服ML中的数据限制,用于激素介导过程.
- 提高废水处理中的ML模型的预测准确性和机械解释性.
- 确定控制激素-污染物反应动力学的关键分子描述因素.
主要方法:
- 用于数据增强,采用了变化自编码器 (VAE) 策略.
- 一个VAE-人工神经网络 (VAE-ANN) 模型被开发和优化.
- 该框架使用基于二氧化碳基 (CO2•−) 的还原过程进行了测试,并扩展到基于基的AOP.
- 分析了诸如HOMO-LUMO差距 (E_GAP),分子硬度 (S) 和分子静电潜力的表面积 (ESP_pos per) 等关键分子描述符.
主要成果:
- 由VAE生成的合成数据显著改善了ML模型的性能,特别是在非线性模型中,测试组R2增加了0.15-0.29.
- 优化的VAE-ANN模型实现了高预测准确性 (R2 = 0.99用于培训,R2 = 0.88用于测试).
- 确定了HOMO-LUMO间隙,分子硬度和正静电电位表面积是影响反应动力学的关键因素.
- VAE-ML框架表现出极好的通用性,与基于基的AOP表现良好.
结论:
- 开发的基于VAE的数据增强框架有效地解决了环境应用ML的小样本限制.
- 该研究为优化激素介导废水处理过程提供了有价值的分子层面的见解.
- 这种方法为提高ML模型性能和可解释性在化学动力学和环境科学中提供了可通用的策略.
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