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Analysis Model of Greenhouse Gas Emissions from Papermaking Wastewater Treatment Process Based on DNN-LSTM
Received:October 01, 2023  
DOI：10.11980/j.issn.0254-508X.2024.04.020
Key Words:deep neural network  long short-term memory  papermaking wastewater treatment  greenhouse gas
Fund Project:国家自然科学基金（52000078）。
Author NameAffiliationPostcode
LI Shizhong State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640） 510640
MAN Yi State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640） 510640
HE Zhenglei* State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640） 510640
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Abstract: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 facilitate 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 discharge， dissolved oxygen concentration， and internal circulation flow rate were key manipulated variables influencing GHG emissions， while the interactions between water quality variables and manipulated variables constituted potential influencing factors for the GHG emissions.
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