Interpretable GHG emission prediction for papermaking wastewater treatment process with deep learning

The content of pollutants in papermaking wastewater is highly varied due to heterogeneous raw materials, product types, and biochemical process conditions, resulting in significant uncertainty of GHG emissions mitigation in the treatment process. This study established a modified Benchmark Simulation Model no.1-based mechanism model to reveal the characteristics of GHG emissions and support GHG reduction in the papermaking wastewater treatment process. Due to its heavy dependence on the Benchmark Simulation Model No. 1 (BSM1) and plentiful parameters, it was instead further proposed Deep Neural Networks (DNN) models and Long Short-Term Memory (LSTM) models to estimate GHG emissions with high accuracy (R2 > 0.94) greatly reduced computational complexity (8920.6 times). The global sensitivity analysis upon DNN revealed key factors for GHG emission with strong explainability, supporting the development of GHG reduction control strategies for the papermaking wastewater treatment process.