Research on sewage water quality prediction based on the PSO-LSTM-SATN model
摘要
In order to solve the problem of large fluctuations, strong randomness, and lack of periodicity in the inflow water quality of industrial wastewater treatment, Particle Swarm Optimization(PSO)-Long Short-Term Memory (LSTM)-Self-Attention(SATN) sewage quality prediction model was proposed. Based on 7 357 sets of historical wastewater quality data from a coal water treatment plant in Shanxi Province, LSTM was firstly used to capture the long-term dependence relationship of COD datas in the influent water quality. Then SATN was used to solve the problem of uneven distribution of water quality information. Finally, PSO was combined to optimize the LSTM-SATN model, which helped the network automatically to obtain the best parameters and model configuration. The results showed that the Mean Square Error(MSE) of the model was 0.528 4 (mg/L)2, the Mean Absolute Error(MAE) was 0.236 9 mg/L, and the Mean Absolute Percentage Error(MAPE) was 4.127 7%. Compared with the LSTM, Gated Recurrent Unit(GRU) and Bidirectional Long Short-Term Memory Network(BiLSTM), the MSE, MAE, and MAPE of the proposed model were significantly reduced. The PSO-LSTM-SATN model could more accurately predict the inflow water quality and provide reasonable guidance for the daily operation and management plan of the factory.