人工智能在乡镇废水处理中的创新应用

Innovative Applications of Artificial Intelligence in Rural Wastewater Treatment

摘要: 面向乡镇污水治理的复杂性与不确定性，本文系统梳理了人工智能在污染物精准去除、水质监测预警与工艺优化控制中的创新应用与可行路径。针对乡镇污水水量水质波动大、氮磷负荷高、季节性差异显著等特征，构建了以监督式与无监督式学习为主的技术路线：通过回归与分类模型实现COD、氨氮、总氮、总磷等关键指标的预测与分类决策，借助聚类、异常检测与降维提升工况识别、冲击负荷预警与可视化分析能力，并以特征工程与正则化、集成学习增强小样本与不平衡样本条件下的泛化性能。在工程实践层面，强化数据治理与可解释性成为落地关键：以标准化采集、清洗、转换、特征选择与降维构建高质量数据底座，采用K均值、DBSCAN、PCA等方法支撑实时监测与运维闭环；依托SHAP、部分依赖图与局部解释提升黑箱模型透明度，结合白箱模型与规则库满足监管可追溯要求。与此同时，本文提出伦理与前瞻视角下的治理框架：以模型透明度、问责机制与全生命周期环境足迹评估保障可持续应用，通过数据公平与隐私安全治理弥合城乡差异；面向中长期，倡导数据驱动与机理模型深度融合的混合建模，提升模型在工况突变下的鲁棒性与可迁移性，并以多源真实运行数据持续校准，推动“预测–优化–控制”闭环在更多乡镇场景中规模化复用，最终实现环境效益、经济效率与社会接受度的协同提升。

Abstract: Addressing the complexity and uncertainty of township wastewater treatment, this paper systematically reviews innovative applications of artificial intelligence in pollutant removal, real-time monitoring, and process optimization. Considering the challenges of large fluctuations in flow and water quality, high nitrogen and phosphorus loads, and pronounced seasonal variability in township wastewater, we propose a technical route centered on supervised and unsupervised learning: regression and classification models for predicting and classifying key indicators such as COD, ammonia nitrogen, total nitrogen, and total phosphorus; clustering, anomaly detection, and dimensionality reduction for condition identification, shock load early warning, and visualization; and feature engineering enhanced by regularization and ensemble methods to improve generalization under small-sample and imbalanced settings. For field deployment, data governance and interpretability are critical enablers: a standardized pipeline of acquisition, cleaning, transformation, feature selection, and dimensionality reduction builds a high-quality data foundation; K-means, DBSCAN, PCA, and related methods underpin real-time monitoring, maintenance closed loops, and operator interpretability; explainable AI with SHAP and partial dependence plots complements white-box models and rule bases to meet regulatory traceability. Meanwhile, this paper proposes a governance framework from an ethical and forward-looking perspective: ensuring sustainable applications through model transparency, accountability mechanisms, and full life-cycle environmental footprint assessment; and bridging urban-rural disparities through data fairness and privacy-preserving governance. Looking to the medium and long term, we advocate for hybrid modeling that deeply integrates data-driven and mechanistic models to enhance the robustness and transferability of models under sudden changes in operating conditions. This approach, continuously calibrated with multi-source real-world operational data, will promote the large-scale reuse of the “prediction-optimization-control” closed loop in more rural scenarios, ultimately achieving synergistic improvements in environmental benefits, economic efficiency, and social acceptance.