Abstract
The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation of wastewater treatment plants (WWTPs). However, existing detection methods struggle to meet the evolving drainage standards and management requirements. To address this issue, this paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor for predicting effluent BOD, enhancing the prediction accuracy and efficiency. Firstly, the selection of appropriate auxiliary variables for soft-sensor modeling is determined through the calculation of k-nearest-neighbor mutual information (KNN-MI) values between the global process variables and effluent BOD. Subsequently, considering the existence of strong interactions among different reaction tanks, a Bi-LSTM neural network prediction model is constructed with historical data. Then, a multivariate probability density-based auto-reconstruction (MPDAR) strategy is developed for adaptive updating of the prediction model, thereby enhancing its robustness. Finally, the effectiveness of the proposed soft sensor is demonstrated through experiments using the dataset from Benchmark Simulation Model No.1 (BSM1). The experimental results indicate that the proposed soft sensor not only outperforms some traditional models in terms of prediction performance but also excels in avoiding ineffective model reconstructions in scenarios involving complex dynamic wastewater treatment conditions.