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使用机器学习方法预测和解释单阶段部分化和anammox系统的去除性能和功能微生物丰度

Xiulin Mu¹, Fangxu Jia¹, Shengming Qiu¹

¹Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing 100044, China; Intelligent Environment Research Center, NO. 1 Guanzhuang, Chaoyang District, Beijing 100080, China.

Bioresource technology

|August 8, 2025

概括

机器学习模型准确地预测了部分化和anammox (PNA) 系统中的去除和微生物丰富性. 确定了最佳条件,以提高PNA的效率和稳定性.

科学领域:

环境微生物学环境微生物学
废水处理工程废水处理工程
计算生物学计算生物学

背景情况:

单阶段部分化和阿纳摩克斯 (PNA) 是一种高效的去除工艺.
控制微生物群落和操作参数对于PNA的稳定性和性能至关重要.
预测建模可以通过识别关键影响因素来优化PNA系统.

研究的目的:

开发机器学习模型来预测PNA系统中的去除率 (NRR) 和功能微生物丰富度.
用SHAP和因果推理来确定影响PNA表现的关键因素及其最佳范围.
为优化 PNA 技术应用提供指导.

主要方法:

使用了机器学习算法,包括人工神经网络 (ANN) 和极端梯度增强 (XGBoost).
沙普利增量解释 (SHAP) 和因果推断被用于因子分析.
用NRR和微生物丰富性预测的R平方值来评估模型性能.

主要成果:

ANN和XGBoost模型显示了NRR (R2 = 0.94) 和微生物丰富度 (R2 ≥ 0.57) 的强有力的预测能力.
自由氨 (FA) 和pH被确定为影响NRR的关键因素.
提出了最佳条件:FA>5 mg/L和O2<0.4 mg/L以抑制酸盐氧化细菌 (NOB).

结论:

具体的微生物污泥类型 (Candidatus Brocadia或Candidatus Kuenenia) 是根据含量,温度和pH稳定性而推的.
开发的模型为PNA技术的实际应用和优化提供了宝贵的见解.
这项研究强调了ML在推进废水处理过程中的潜力.

关键词:

它们是Candidatus Brocadia.候选人库内尼亚尼亚.可解释的机器学习氧化亚酸盐的细菌可以氧化亚酸盐.清除的速度是多少污水处理污水处理污水处理

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