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

SUBMITTER: Fang G 

PROVIDER: S-EPMC11613165 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature