Prediction of COD Emission Concentration of Petroleum Refining Waste Water Treatment Plant Based on CNN-LSTM Hybrid Neural Network
It is important and urgent for petroleum refining enterprises to quickly and accurately predict the chemical oxygen demand(COD)of organic pollutants.To this end,a CNN-LSTMhybrid neural network algorithm with prior knowledge is proposed,a COD dis-charge concentration prediction model for refinery waste water treatment plants is established by using production factors and COD dis-charge concentration data from a refinery waste water treatment plant as research samples.The results show that the model can not only give full play to the advantages of CNN in better characterizing and extracting local feature information,but also has the characteristics of LSTM's good inheritance of continuous time series data.Its root mean square error is 22.321 7,the determination coefficient is 0.873 2,and the average relative error is reduced by 5.45%compared to the model constructed by LSTMnetwork.
refining waste water treatmenthybrid neural networkCOD concentrationpollution emission prediction
万方数据
维普
