Integrating data-driven models and process expertise in soft-sensor design for a wastewater treatment digital twin application

Digital twin models offer great potential for process improvements in wastewater treatment plants (WWTPs). Such models require a constant real-time input data feed from the physical process. Collecting these data is challenging, especially in the harsh conditions in the headworks of the process. In this study, data-driven models and process and sewer system expertise were combined to design soft-sensors for primary effluent COD and NH₄-N prediction. Ordinary least squares regression and the seasonal autoregressive integrated moving average model with exogenous variables were tested using flow rate and suspended solids concentration as model input. An excellent NH₄-N prediction was achieved, and the prediction accuracy was further improved by implementing process-insight-driven weights. The tested models were able to achieve either good COD estimation accuracy or effectively capture the variability in the target data. However, achieving both simultaneously remained challenging, with or without weights. Simulation tests using the calibrated process model demonstrated that the developed soft-sensors were able to provide real-time predictions leading to goodness-of-fit in simulations comparable to or better than that achieved using laboratory data influent quality.