Research on intelligent prediction of water quality in sewage treatment process based on event triggering

Aiming at the problem that the non-stationary and multi-working conditions of wastewater treatment process (WWTP) make it difficult to predict water quality efficiently and accurately, this paper proposes an event-triggered fuzzy neural network (ETFNN) model to predict total phosphorus (TP) of WWTP. This method can perceive the non-stationary and multi-working conditions during the evolution process of the TP state in the form of events, thereby achieving efficiently-accurately tracking and prediction. First, the fuzzy neural network (FNN) is trained using the historical data of total phosphorus, and events are defined according to the trend of training error changes that can reflect the switching of multiple operating conditions. Second, an event-triggered learning is designed to adaptively update the parameters of FNN, where the different learning steps will be triggered when some different events occur. This event-triggered learning can perceive and recognize the non-stationary and multiple operational conditions in WWTP. Meanwhile, the convergence analysis of the ETFNN model is given by analyzing the performance potential function of the equivalent Markov decision process. Finally, the ETFNN is considered as the soft-sensing model to predict TP of WWTP, and then a comprehensive analysis is given as well. Experimental results show that the proposed ETFNN-based soft-sensing model not only improves the accuracy of TP prediction, but also identifies and skips invalid data in the form of events, thereby reducing the computational complexity of the prediction model. © 2025 Materials China. All rights reserved.