Effluent quality soft sensor for wastewater treatment plant with ensemble sparse learning-based online next generation reservoir computing

Abstract

Real-time monitoring of key quality variables is essential and crucial for stable and safe operations of wastewater treatment plants (WWTPs). Next generation reservoir computing (NG-RC) has recently garnered significant attention in quality prediction, such as COD and BOD, as an effective alternative to traditional reservoir computing (RC), then is able to act as a data-driven soft sensor to twin a hardware sensor for quality variable measurements. Unlike RC, NG-RC does not require random sampling matrices to define the weights of recurrent neural networks and has fewer hyperparameters. However, NG-RC is usually used online but trained offline, thus leading to model degradation under dynamic scenarios. This paper proposes a sparse online NG-RC approach to meet the real-time requirements of WWTPs and mitigate the impact of measurement noise on the model. First, inspired by the Woodbury matrix identity, an incremental strategy is designed, using sequentially arriving data blocks to learn the output weights of NG-RC online. Then, an ensemble sparse strategy is combined to alleviate overfitting issues of the prediction model. Moreover, a soft sensor based on the ensemble sparse online NG-RC is developed to perform real-time prediction of quality indicators in wastewater treatment processes. Finally, two datasets from actual WWTPs are used to validate the effectiveness of the proposed model.