An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants

Mathematical modeling plays a critical role toward the mitigation of nitrous oxide (N₂O) emissions from wastewater treatment plants (WWTPs). In this work, we proposed a novel hybrid modeling approach by integrating the first principal model with deep learning techniques to predict N₂O emissions. The hybrid model was successfully implemented and validated with the N₂O emission data from a full-scale WWTP. This hybrid model is demonstrated to have higher accuracy for N₂O emission modeling in the WWTP than the mechanistic model or pure deep learning model. Equally important, the hybrid model is more applicable than the pure deep learning model due to the lower requirement of data and the pure mechanistic model due to the less calibration requirement. This superior performance was due to the hybrid nature of the proposed model. It integrated the essential wastewater treatment knowledge as the first principal component and the less understood N₂O production processes by the data-driven deep learning approach. The developed hybrid model was also successfully implemented under different circumstances for the prediction of N₂O flux, which showed the generalizability of the model. The hybrid model also showed great potential to be applied for the N₂O mitigation work. Nevertheless, the capability of the hybrid model in evaluating N₂O mitigation strategies still requires validation with experiments. Going beyond N₂O modeling in WWTP, the novel hybridization modeling concept can potentially be applied to other environmental systems.