Analysis Model of Greenhouse Gas Emissions from Papermaking Wastewater Treatment Process Based on DNN-LSTM
This study employed deep learning algorithms to model and analyze greenhouse gas(GHG)emissions in the papermaking wastewater treatment process,providing insights for GHG reduction and control.Based on simulation experiments on the basehine simulation model 1(BSM1),combined with the mechanisms of GHG generation in the papermaking wastewater treatment process,the deep neural network(DNN)and long short-term memory models(LSTM)were proposed for modeling and analyzing GHG emissions in the papermaking wastewater treatment process,to facili-tate real-time monitoring and analysis of GHG.The results showed that deep learning could effectively describe GHG emission characteristics,with validation results showing R2＞0.99 and average relative errors below 1％.The DNN model-based sensitivity analysis results showed that sludge dis-charge,dissolved oxygen concentration,and internal circulation flow rate were key manipulated variables influencing GHG emissions,while the in-teractions between water quality variables and manipulated variables constituted potential influencing factors for the GHG emissions.
deep neural networklong short-term memorypapermaking wastewater treatmentgreenhouse gas
国家科技期刊平台
NSTL
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