Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators.
Journal:
Journal of environmental management
PMID:
40174391
Abstract
Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of water quality indicators on GHGs emissions within the biological nutrient removal process are still unclear, which deserves intensive attention. Here, a lab-scale anoxic/oxic (A/O) process was constructed for raw regenerated papermaking wastewater treatment with different low/high-concentration influent stages for about 110 days to evaluate GHGs emissions. A high-quality dataset included 295 sets of the multi-factors (including COD, suspended solid (SS), NH-N, NO-N, NO-N, and pH/DO/Temperature) was built. Moreover, the corresponding proportion of GHGs emissions were analyzed and a novel hybrid deep learning model TCNA, which integrated the Temporal Convolutional Network (TCN) and Attention Mechanism (AM), was developed to explore the trends and predictions of GHGs emissions based on the dataset. A series of comparisons with model Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Temporal Convolutional Networks (TCN) were also conducted under the same conditions. The TCNA model showed an outstanding performance for CO, CH, and NO emissions prediction, achieving the highest value of R score (CO, 0.8014; CH, 0.8839; NO, 0.9354) and the lowest value of root mean squared error (RMSE) and mean absolute error (MAE) (CO: 2.6137,1.9366; CH: 1.929,0.7214; NO: 0.8897, 0.5777) among the five models above. The results highlight the potential of the TCNA model for accurate and robust prediction of GHGs emissions from industrial wastewater treatment plants with the A/O treatment process, contributing to effective GHGs mitigation strategies and carbon management of industrial wastewater treatment.