Innovative multistep and synchronous soft sensing prediction of COD and NH in WWTPs via multimodal data and multiple attention mechanisms.

Accurate prediction of Chemical Oxygen Demand (COD) and ammonia nitrogen (NH₃) is crucial for maintaining stable and effective wastewater treatment processes. Traditional methods rely on costly, high-maintenance sensors, limiting their application in resource-limited wastewater treatment plants. Soft sensing methods provide an alternative by reducing dependence on costly sensors. However, existing approaches cannot perform multitarget and multistep predictions, limiting their practical applicability. This study introduced a novel triple attention-enhanced encoder-decoder temporal convolutional network (TAED-TCN) to address this problem. The model used multimodal inputs, including easily accessible water quality parameters and wastewater surface images, for multistep and synchronous prediction of COD and NH₃. When it was validated with real-world sequencing batch reactor wastewater data, the model demonstrated superior multistep prediction performance. Specifically, the R² for 1-h predictions of COD and NH₃ was over 26.03 % and 20.51 % higher than the baseline model, respectively. By incorporating multiple attention mechanisms (feature, temporal, and cross-attention), TAED-TCN effectively captured essential features, model nonlinear relationships, and identified long-term dependencies, thus enabled consistent multitarget prediction results even under abnormal conditions. Additionally, economic analysis revealed that TAED-TCN could reduce COD and NH₃ monitoring costs by 79 % over the equipment life cycle. This study offers a cost-effective solution for water quality prediction, enhancing the operational efficiency of wastewater management.