多模式学习辅助识别废水处理厂的废水质量和毒性
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023 Jiangsu, P. R. China.
Environmental science & technology
|June 11, 2025
概括
本研究引入了一种多式模式学习 (MML) 框架,用于预测废水处理厂 (WWTP) 废水质量和毒性. 与传统方法相比,MML模型显著提高了预测准确度,有助于环境可持续性.
科学领域:
- 环境科学 环境科学
- 水处理技术 水处理技术
- 机器学习应用 机器学习应用
背景情况:
- 污水处理厂 (WWTP) 的废水由于有毒性而构成生态风险,需要严格的水质监测.
- 由于众多变量和影响因素,评估WWTP废水是复杂的,挑战了传统的数据挖掘和机器学习 (ML) 方法.
- 现有的ML模型往往无法纳入诸如处理过程等多式联络因素,从而限制了预测准确度.
研究的目的:
- 开发和评估一个多模式学习 (MML) 框架,用于预测WWTP废水质量和毒性.
- 通过整合各种数据模式,包括过程信息来提高预测准确性.
- 通过结合多式联运因素来解决传统ML的局限性.
主要方法:
- 提出了一种多式学习 (MML) 框架,用于废水质量和毒性预测.
- 实施了表示融合和决策融合策略,以优化MML模型性能.
- 引入了一种新的3C编码策略,用于有效编码过程信息.
主要成果:
- 在MML模型中,与传统方法相比,显著改善,R2的增加范围从0.057到0.234.4.
- 最终的MML模型实现了高性能,平均R2为0.874的水质和0.904的毒性.
- 拟议的3C编码策略有效地处理了流程信息挑战.
结论:
- 基于MML的框架大大提高了WWTP废水质量和毒性预测的准确性.
- 这种方法促进了知情决策,促进了WWTP中的智能流程控制和优化.
- 该研究通过改善废水处理过程的评估和管理来促进环境可持续性.
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