关键词: Wastewater treatment plant, Influent quality prediction, Discrete wavelet transform, Transformer, Local feature, Long-term dependencies

Abstract: Accurate prediction of wastewater treatment plants (WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management. However, since existing methods overlook the data noise generated from harsh operations and instruments, while the local feature pattern and long-term dependency in the wastewater quality time series, the prediction performance can be degraded. In this paper, a discrete wavelet transform and convolutional enhanced Transformer (DWT-CeTransformer) method is developed to predict the influent quality in WWTPs. Specifically, we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform, effectively removing noise while preserving key data characteristics. Further, a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features, and then these local features are combined with Transformer's self-attention mechanism, so that the model can not only capture long-term dependencies, but also retain the sensitivity to local context. In this study, we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2. The experimental results show that, compared to baseline models, DWT-CeTransformer can significantly improve the prediction performance of influent COD and -N. Specifically, MSE, MAE, and RMSE improve by 78.7%, 79.5%, and 53.8% for COD, and 79.4%, 70.2%, and 54.5% for -N. On simulated data, our method shows strong improvements under various weather conditions, especially in dry weather, with MSE, MAE, and RMSE for COD improving by 68.9%, 48.0%, and 44.3%, and for -N by 78.4%, 54.8%, and 53.2%.

Key words: Wastewater treatment plant, Influent quality prediction, Discrete wavelet transform, Transformer, Local feature, Long-term dependencies