通过机器学习和微观宇宙实验,对活性污泥系统中硫甲醇去除的机制性见解
Junjie Chen1, Lipin Li1, Xiaowei Lv1
1State Key Laboratory of Urban-rural Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Water research
|January 13, 2026
概括
机器学习准确地预测了废水处理中的抗生素去除,确定了适应期和pH等关键因素,以优化激活污泥过程,以打击抗菌素耐药性.
科学领域:
- 环境微生物学与工程
- 废水处理技术 废水处理技术
- 降低抗微生物药物耐药性的缓解
背景情况:
- 废水中的抗生素残留物对生态和公共卫生构成重大风险,包括抗菌素耐药性 (AMR) 的发展.
- 活性污泥 (AS) 工艺对于废水处理至关重要,但其抗生素去除效率是可变的,并且在机理上不清楚.
- 硫甲醇 (SMX) 是一种代表性的抗生素,其在AS中的去除机制需要进一步阐明.
研究的目的:
- 开发基于机器学习 (ML) 的综合框架,以确定控制AS过程中SMX生物降解的关键因素.
- 阐明影响SMX移除在不同尺度 (实验室,试点,完整) 的过程级机制.
- 为优化AS过程提供可操作的指导,以减轻抗生素污染.
主要方法:
- 开发和优化一个随机森林回归 (RFR) 模型,使用来自实验室,试点和全规模AS系统的集成数据集.
- 应用特征归因,依赖性和因果分析来确定关键因素和机制.
- 微观宇宙验证实验,以确认模型预测和生物可信性.
主要成果:
- 该RFR模型实现了高预测性能 (测试R2=0.83) 和概括性 (预测误差<20%).
- 过程控制参数 (43%) 是SMX删除的主要因素,其次是影响力 (30%) 和过程性能参数 (27%).
- 确定的关键因素包括适应期 (AP),pH,有影响的SMX和氨 (NH4+-N),确定了新的值.
结论:
- ML框架成功地确定了控制SMX生物降解的关键因素和值效应 (例如,AP,pH,有影响的SMX,NH+-N).
- 氨去除率被确定为比COD去除率更强的SMX生物降解AS性能指标.
- 该研究提供了一种可概括的方法,将数据驱动的预测与优化废水处理和减轻抗生素污染的机制理解相结合.
关键词:
活性污泥活动污泥抗生素 抗生素是一种抗生素.机器学习是机器学习.随机森林回归随机森林回归硫甲佐 (sulfamethoxazole) 是一种硫甲佐 (sulfamethoxazole) 的一种化合物.更多相关视频
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