Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders

Missing data imputation and automatic fault detection of wastewater treatment plant (WWTP) sensors are crucial for energy conservation and environmental protection. Given the dynamic and non-linear characteristics of WWTP measurements, the conventional diagnosis models are inefficient and ignore potential valuable features in the offline modeling phase, leading to false alarms and inaccurate imputations. In this study, an inclusive framework for missing data imputation and sensor self-validation based on integrating variational autoencoders (VAE) with a deep residual network structure (ResNet-VAE) is proposed. This network structure can automatically extract complex features from WWTP data without the risk of vanishing gradients by learning the potential probability distribution of the input data. The proposed framework is intended to increase the reliability of faulty sensors by imputing missing data, detecting anomalies, identifying failure sources, and reconstructing faulty data to normal conditions. Several metrics were utilized to assess the performance of the suggested approach in comparison with other different methods. The VAE-ResNet approach showed superiority to detect (DRSPE = 100%), reconstruct faulty WWTP sensors (MAPE = 15.41%-5.68%) and impute the missing values (MAPE = 10.44%-3.98%). Lastly, the consequences of faulty data, missing data, reconstructed and imputed data were evaluated considering electricity consumption and resilience to demonstrate the ResNet-VAE model's superior performance for WWTP sustainability.

Automated summary

This study proposed a framework based on VAE and ResNet for missing data imputation and automatic fault detection of WWTP sensors, aiming to increase the reliability of faulty sensors by automatically extracting complex features and outperforming other methods in performance.