关键词: 城市污水处理, 氨氮浓度预测, 神经网络, 特征提取, 优化, 算法

Abstract:

To address the issue of accurately measuring the effluent ammonia nitrogen concentration in real-time during urban wastewater treatment processes, this paper constructs a convolutional layer (CL) and squeeze-and-excitation attention mechanism (SEAM) based long short-term memory network (CSA-LSTM) model. First, by introducing the CL, nonlinear features within the data are deeply extracted. The SEAM adaptively allocates weights to feature channels, achieving feature decoupling. Secondly, the long short-term memory network (LSTM) extracts long-term dependencies in time-series data to realize real-time prediction of effluent ammonia nitrogen concentration. Then, a Bayesian optimization algorithm with an adaptive acquisition function is proposed to optimize model parameters, further enhancing model accuracy. Finally, the effectiveness of CSA-LSTM is tested based on benchmark experiments and actual wastewater treatment plant (WWTP) data. The results show that the model has high predictive accuracy for effluent ammonia nitrogen concentration, can effectively handle the strong nonlinearity, coupling, and time-dependency of data in urban sewage treatment, and has good generalization ability.

Key words: urban wastewater treatment, ammonia nitrogen concentration prediction, neural network, feature extraction, optimization, algorithm