摘要：

针对污水处理厂曝气能耗高、加药冗余大、运行波动性强的问题，提出基于LSTM-RF融合算法的智能协同调控系统。通过部署多维度感知网络（COD/氨氮/DO等8参数实时监测），建立LSTM时序预测模型（MAPE=2.3%）与RF故障诊断模型（精度98.3%），创新构建曝气-加药联动的决策中枢。在某5万m³/d（服务人口约20万）A²/O工艺污水厂的应用表明：系统使出水达标率升至96.8%，吨水电耗降低28.6%，药剂消耗减少19.2%，年综合成本节约162.6万元。同步实现设备故障率下降85.7%，关键参数波动幅度减小64.3%，证明该系统在节能降耗与运行稳定性提升中的显著价值。

关键词:

AI智能, 污水处理, LSTM-RF融合, 曝气-加药协同, 智能调控, 成本优化

Abstract:

Addressing the issues of high aeration energy consumption，excessive dosing redundancy，and strong operational volatility in wastewater treatment plants，this paper proposes an intelligent collaborative regulation system based on the LSTM-RF fusion algorithm. By deploying a multi-dimensional perception network（real-time monitoring of 8 parameters including COD，ammonia nitrogen，DO，etc.），an LSTM time series prediction model（MAPE=2.3%） and an RF fault diagnosis model（accuracy 98.3%） are established，innovatively constructing a decision-making hub for aeration-dosing linkage. The application of the A²/O process sewage plant with a capacity of 50 000 m³/d（serving a population of approximately 200 000） shows that the system has increased the effluent compliance rate to 96.8%，reduced power consumption per ton of water by 28.6%，decreased chemical consumption by 19.2%，and saved an annual comprehensive cost of 1.626 million yuan. Simultaneously，it has achieved a reduction in equipment failure rate by 85.7% and a decrease in fluctuation range of key parameters by 64.3%，proving the significant value of the system in energy saving，consumption reduction，and operational stability improvement.

Key words:

AI intelligence, wastewater treatment, LSTM-RF fusion, aeration-dosing coordination, intelligent regulation, cost optimization