Evolving Deep Delay Echo State Network for Effluent NH4-N Prediction in Wastewater Treatment Plants
Cuili Yang, Sheng Yuan Yang, Jian Tang, Junfei Qiao, Wen Yu · IEEE Transactions on Instrumentation and Measurement · 2023
In wastewater treatment plants (WWTPs), the prediction of effluent ammonia nitrogen (NH4-N) concentration is vital, which is a major cause of lake eutrophication. To solve this problem, the evolving deep delay echo state network (EDDESN) is proposed. Firstly, the EDDESN is decomposed into several serially connected sub-reservoirs, which are inserted delay units to learn the temporal relationships within sequence data. Secondly, the input and reservoir internal weights are generated by singular value decomposition-based matrix design strategy, which can reduce searching dimensions and guarantee the echo state property (ESP). Moreover, the architecture hyperparameters and weights of EDDESN are simultaneously optimized by competitive swarm optimizer (CSO) based two-stages optimization approach. Finally, the experimental results on practical NH4-N dataset and simulated Mackey-Glass time series demonstrate the superiority of EDDESN as compared with other time series prediction approaches.