Wu Xue, Chen Ming, Zhu Tengyi, Chen Dou, Xiong Jianglei
School of Civil Engineering, Southeast University, Nanjing 210096, China.
School of Civil Engineering, Southeast University, Nanjing 210096, China.
Sci Total Environ. 2024 Nov 15;951:175411. doi: 10.1016/j.scitotenv.2024.175411. Epub 2024 Aug 10.
Efficient management of wastewater treatment plants (WWTPs) necessitates accurate forecasting of influent water quality parameters (WQPs) and flow rate (Q) to reduce energy consumption and mitigate carbon emissions. The time series of WQPs and Q are highly non-linear and influenced by various factors such as temperature (T) and precipitation (Precip). Conventional models often struggle to account for long-term temporal patterns and overlook the complex interactions of parameters within the data, leading to inaccuracies in detecting WQPs and Q. This work introduced the Pre-training enhanced Spatio-Temporal Graph Neural Network (PT-STGNN), a novel methodology for accurately forecasting of influent COD, ammonia nitrogen (NH-N), total phosphorus (TP), total nitrogen (TN), pH and Q in WWTPs. PT-STGNN utilizes influent data of the WWTP, air quality data and meteorological data from the service area as inputs to enhance prediction accuracy. The model employs unsupervised Transformer blocks for pre-training, with efficient masking strategies to effectively capture long-term historical patterns and contextual information, thereby significantly boosting forecasting accuracy. Furthermore, PT-STGNN integrates a unique graph structure learning mechanism to identify dependencies between parameters, further improving the model's forecasting accuracy and interpretability. Compared with the state-of-the-art models, PT-STGNN demonstrated superior predictive performance, particularly for a longer-term prediction (i.e., 12 h), with MAE, RMSE and MAPE at 12-h prediction horizon of 2.737 ± 0.040, 4.209 ± 0.060 and 13.648 ± 0.151 %, respectively, for the algebraic mean of each parameter. From the results of graph structure learning, it is observed that there are strong dependencies between NH-N and TN, TP and Q, as well as Precip, etc. This study innovatively applies STGNN, not only offering a novel approach for predicting influent WQPs and Q in WWTPs, but also advances our understanding of the interrelationships among various parameters, significantly enhancing the model's interpretability.
高效管理污水处理厂（WWTPs）需要准确预测进水水质参数（WQPs）和流量（Q），以降低能源消耗并减少碳排放。WQPs和Q的时间序列具有高度非线性，且受温度（T）和降水量（Precip）等多种因素影响。传统模型往往难以考虑长期时间模式，忽视数据中参数的复杂相互作用，导致在检测WQPs和Q时出现不准确的情况。这项工作引入了预训练增强时空图神经网络（PT-STGNN），这是一种用于准确预测污水处理厂进水化学需氧量（COD）、氨氮（NH-N）、总磷（TP）、总氮（TN）、pH值和Q的新方法。PT-STGNN利用污水处理厂的进水数据、空气质量数据和来自服务区的气象数据作为输入，以提高预测准确性。该模型采用无监督Transformer模块进行预训练，通过有效的掩码策略有效捕捉长期历史模式和上下文信息，从而显著提高预测准确性。此外，PT-STGNN集成了独特的图结构学习机制来识别参数之间的依赖关系，进一步提高了模型的预测准确性和可解释性。与现有最先进模型相比，PT-STGNN表现出卓越的预测性能，特别是对于长期预测（即12小时），在12小时预测期时，每个参数的代数平均值的平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别为2.737±0.040、4.209±0.060和13.648±0.151%。从图结构学习结果可以看出，NH-N和TN、TP和Q以及Precip等之间存在很强的依赖关系。本研究创新性地应用了STGNN，不仅为预测污水处理厂进水WQPs和Q提供了一种新方法，还增进了我们对各种参数之间相互关系的理解，显著提高了模型的可解释性。
