School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China.
School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
J Hazard Mater. 2022 Jun 15;432:128732. doi: 10.1016/j.jhazmat.2022.128732. Epub 2022 Mar 17.
The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stability of sewage treatment. Therefore, timely prediction and early warning of heavy metal concentrations in industrial sewer networks is crucial. However, due to the complex sources of heavy metals in industrial sewer networks, traditional physical modeling and linear methods cannot establish an accurate prediction model. Herein, we developed a Gated Recurrent Unit (GRU) neural network model based on a deep learning algorithm for predicting the concentrations of heavy metals in industrial sewer networks. To train the GRU model, we used low-cost and easy-to-obtain urban multi-source data, including socio-environmental indicator data, air environmental indicator data, water quantity indicator data, and easily measurable water quality indicator data. The model was applied to predict the concentrations of heavy metals (Cu, Zn, Ni, and Cr) in the sewer networks of an industrial area in southern China. The results are compared with the commonly used Artificial Neural Network (ANN) model. In this study, it was shown that the GRU had better prediction performance for Cu, Zn, Ni, and Cr concentrations, with the average R significantly increased by 12.35%, 11.94%, 9.21%, and 8.13%, respectively, compared to ANN predictions. The sensitivity analysis based on Shapley (SHAP) values revealed that conductivity (σ), temperature (T), pH, and sewage flow (Flow) contributed significantly to the prediction results of the model. Furthermore, the three input variables including air pressure (AP), land area (A), and population (Pop.) were removed without affecting the prediction performance of the model, which maximized the modeling efficiency and reduced the operational cost. This study provides an economical and feasible technical method for early warning of abnormal heavy metal concentrations in urban industrial sewer networks.
城市工业污水管网中重金属浓度较高，会严重影响污水厂活性污泥中的微生物，从而恶化出水水质，破坏污水处理稳定性。因此，及时预测和预警工业污水管网中重金属浓度至关重要。但是，由于工业污水管网中重金属来源复杂，传统的物理建模和线性方法无法建立准确的预测模型。本文提出了一种基于深度学习算法的门控循环单元（GRU）神经网络模型，用于预测工业污水管网中重金属浓度。为了训练 GRU 模型，我们使用了低成本、易获取的城市多源数据，包括社会环境指标数据、空气环境指标数据、水量指标数据和易于测量的水质指标数据。将模型应用于中国南方某工业区污水管网中重金属（Cu、Zn、Ni 和 Cr）浓度的预测，并与常用的人工神经网络（ANN）模型进行比较。结果表明，GRU 对 Cu、Zn、Ni 和 Cr 浓度的预测性能更好，与 ANN 预测相比，平均 R 值分别显著提高了 12.35%、11.94%、9.21%和 8.13%。基于 Shapley（SHAP）值的敏感性分析表明，电导率（σ）、温度（T）、pH 和污水流量（Flow）对模型预测结果有重要贡献。此外，去除了三个输入变量，包括气压（AP）、土地面积（A）和人口（Pop.），而不影响模型的预测性能，从而最大化建模效率并降低运营成本。本研究为城市工业污水管网中重金属异常浓度的预警提供了一种经济可行的技术方法。
