基于多特征融合与改进长短期记忆网络的污水处理厂出水总氮预测OA

Addressing the challenge of accurately predicting total nitrogen concentrations in wastewater treatment plant effluent in real time,a multi-feature fusion prediction model integrating isolation forest(IF),whale optimization algorithm(WOA),multi-head self-attention(MA),and long short-term memory(LSTM)network was proposed.Using historical data from a wastewater treatment plant in Heilongjiang Province as the research subject,the IF algorithm was employed to detect and remove data noise,thereby enhancing data quality.Building upon this foundation,the MA mechanism was introduced to improve the LSTM model,enhancing its ability to capture long-term dependencies in water quality time series data.The WOA algorithm was utilized for adaptive optimization of model parameters to further improve prediction accuracy.Experimental results demonstrate that the proposed IF-WOA-M A-LSTM model effectively mitigates data noise interference and multi-factor coupling issues,with a mean squared error of 0.17,mean absolute error of 0.04,root mean squared error of 0.21,and coefficient of determination of 0.94.A reliable technical reference for real-time monitoring and process optimization of total nitrogen concentrations in effluent from small and medium-sized wastewater treatment plants is provided by this model.