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Total nitrogen prediction based on quantum weighted minimal gated unit neural networks in wastewater treatment process

The total nitrogen (TN) in the effluent of wastewater treatment plant is an important index to evaluate the effect of water treatment, and its accurate prediction can provide data support for the optimal operation of wastewater treatment process. The Quantum Weighted Minimal Gated Unit (QWMGU) neural network by introducing a quantum computing mechanism into MGU network is presented to predict the effluent total nitrogen. Firstly, a multi-dimensional single-step time series prediction method was used to construct the TN dataset of wastewater; Then, the above index set was input into QWMGU to complete the TN prediction; Lastly, some comparison experiments with the water outlet data of the actual sewage treatment plant were performed. The results show that the proposed TN prediction model based on QWMGU has higher prediction accuracy than QWGRU/MGU/GRU/LSTM, and faster convergence speed than QWGRU/GRU/LSTM.