【Applied Energy 最新原创论文】一种结合模态分解和深度学习算法的混合模型用于城市污水管网TP检测

• 结合两阶段分解和LSTM的污水管网TP检测模型

• 两阶段分解降低了TP时间序列的不稳定性和复杂性

• 模态分解程序设置显著影响建模效率、准确性

• 所提出的混合模型具有较好的精度，平均R² = 0.946

• 所提出的混合动力模型具有能源效益和成本效益高的优点

Timely and accurately grasping total phosphorus (TP) concentration in sewer networks is crucial for urban phosphorus flow management and shock load early warning of sewage treatment facilities. Modeling-based methods are cleaner and more energy-saving than traditional methods requiring digestion procedures. However, the TP time series' strong nonlinearity and complexity result in unsatisfactory accuracy in these methods. This work proposes a hybrid model named CEEMDAN-SE-VMD-LSTM that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM) network to improve the accuracies of modeling-based methods. In proposed hybrid model, the two-stage decomposition procedure can decompose the TP time series into several lower-complexity modes, and the powerful nonlinear mapping ability of the LSTM network enables accurate prediction of these modes. In case study, the proposed hybrid model achieves excellent detection accuracy with an average R² of 0.9460 ± 0.0243. Compared with the hybrid models formed by combining other decomposition procedures (i.e., CEEMDAN, VMD, singular spectrum analysis (SSA), CEEMDAN-SE-SSA) and LSTM network, the proposed hybrid model has the highest detection accuracy (1.36-3.94% higher Nash-Sutcliffe efficiency, 1.28-4.58% higher R², 12.14-24.86% lower RMSE). The strategy of setting the mode decomposition procedure based on a comprehensive analysis of the decomposition algorithms and the obtained modes ensures high detection accuracy of the proposed hybrid model while avoiding costly computational burdens. This work is enlightening for improving the accuracy and modeling efficiency of soft detection methods, which are expected to reduce energy consumption and pollution caused by water quality detection.

Keywords

TP soft detection 总磷软检测

Sewer networks 污水管网

Urban multi-source data 城市多源数据

Two-stage decomposition 两阶段分解

Long short-term memory 长短期记忆

Energy-saving 节能

图2 所提出的混合模型（CEEMDAN-SE-VMD-LSTM）的框架

图5 两阶段模式分解的结果（a）CEEMDAN，（b）VMD

图8 LSTM模型和混合模型在TP时间序列预测中的性能

本研究由哈尔滨工业大学（深圳）、哈尔滨工业大学城市水资源与环境国家重点实验室的研究人员共同完成。

李朝林，博士，哈尔滨工业大学（深圳）教授/博导，哈尔滨工业大学城市水资源与环境国家重点实验室研究员，现带领团队从事水处理技术与设备研发、环境新材料及其应用、水环境大数据应用、固体废弃物处理等领域的研究。在Applied Catalysis B:Environmental、Chemical Engineering Journal、Applied Energy、Water Research、Green Chemistry、Journal of Hazardous Materials等期刊上发表论文100余篇。主持国家自然科学基金面上项目等课题20余项。

王文辉，博士，哈尔滨工业大学（深圳）青年拔尖副教授/博导，曾获广东省环境科学学会生态环境青年科技奖和广东省环境保护科学技术奖二等奖。主要从事水环境大数据应用、高级氧化、H₂O₂合成、钠离子电池等领域的研究。以第一/通讯作者在Cell Reports Physical Science、Energy Storage Materials、Applied Catalysis B:Environmental、Water Research、Chemical Engineering Journal等高水平期刊上发表SCI论文50余篇（平均IF~10）。主持国家自然科学基金等课题8项。

张怡拓，博士研究生，从事水环境大数据应用领域的研究。在Applied Energy、Environmental Science & Technology、Journal of Cleaner Production、Journal of Hazardous Materials等期刊发表SCI论文12篇。

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