Wastewater treatment plants (WWTPs) influent conditions can dramatically affect a treatment unit's state and effluent quality. WWTP sensors may record faulty measurements due to abnormal events or the malfunction of the system, leading to serious problems in the system's operation and the violation of effluent discharge standards. Therefore, automatic fault detection and faulty data reconciliation are crucial for an efficient and stable WWTP monitoring. In this study, a holistic framework for sensor validation of WWTP influent conditions is presented considering the non-linearity, measurement noise, and complexity of the WWTP's data. A stacked denoising autoencoder (SDAE) model is proposed to detect, identify, and reconcile faulty data based on data from a real WWTP in South Korea. The proposed SDAE architecture presented a detection rate (DR) between 74% and 98%. The faulty sensor was identified using an SDAE-based sensor validity index (SVI). Data reconciliation showed that the SDAE was the most suitable reconciliation method based on the root mean square error (RMSE) for total nitrogen (TN) influent conditions of 4.04 mg N/L. Finally, faulty, noisy, and reconciled measurements were evaluated in a WWTP model to determine the proposed method's resilience potential.

污水处理厂 (WWTP) 的进水条件会显着影响处理单元的状态和出水质量。污水处理厂传感器可能会因异常事件或系统故障而记录错误测量，从而导致系统运行出现严重问题和违反污水排放标准。因此，自动故障检测和故障数据协调对于高效稳定的污水处理厂监测至关重要。在这项研究中，考虑到 WWTP 数据的非线性、测量噪声和复杂性，提出了一个用于 WWTP 进水条件的传感器验证的整体框架。基于韩国真实污水处理厂的数据，提出了一种堆叠去噪自编码器 (SDAE) 模型来检测、识别和协调故障数据。所提出的 SDAE 架构的检测率 (DR) 介于 74% 和 98% 之间。使用基于 SDAE 的传感器有效性指数 (SVI) 识别故障传感器。数据核对表明，对于 4.04 mg N/L 的总氮 (TN) 进水条件，基于均方根误差 (RMSE) 的 SDAE 是最合适的核对方法。最后，在污水处理厂模型中评估了错误、嘈杂和协调的测量，以确定所提出方法的恢复潜力。