Li Wenting, Li Yonggang, Li Dong, Zhou Jiayi
School of Automation, Central South University, Changsha 410083, China.
Sensors (Basel). 2024 Nov 25;24(23):7508. doi: 10.3390/s24237508.
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.
精确检测出水生物需氧量（BOD）对于污水处理厂（WWTPs）的稳定运行至关重要。然而，现有的检测方法难以满足不断变化的排水标准和管理要求。为解决这一问题，本文提出了一种基于多变量概率密度的自动重构双向长短期记忆（MPDAR-Bi-LSTM）软传感器，用于预测出水BOD，提高预测精度和效率。首先，通过计算全局过程变量与出水BOD之间的k近邻互信息（KNN-MI）值，确定软传感器建模合适的辅助变量。随后，考虑到不同反应池之间存在强相互作用，利用历史数据构建Bi-LSTM神经网络预测模型。然后，开发了一种基于多变量概率密度的自动重构（MPDAR）策略，用于预测模型的自适应更新，从而提高其鲁棒性。最后，使用基准模拟模型1（BSM1）的数据集进行实验，验证了所提出软传感器的有效性。实验结果表明，所提出的软传感器不仅在预测性能方面优于一些传统模型，而且在避免复杂动态污水处理条件下无效模型重构方面表现出色。
