Real-time quantification of activated sludge concentration and viscosity through deep learning of microscopic images.

The parameters of activatedg sludge are crucial for the daily operation of wastewater treatment plants (WWTPs). In particular, mixed liquor suspended solids (MLSS) and apparent viscosity provide metrics for the biomass and rheological properties of activated sludge. Traditional methods for determining these parameters are time-consuming, require separate measurements for each index, and fail to provide real-time data for future 'smart' WWTPs. Here we show a real-time online microscopic image data analysis system that quantitatively identifies MLSS and apparent viscosity. Microscopic videos of activated sludge are captured in lab-scale sequencing batch reactors under chemical oxygen demand shock, yielding 41482 high-quality images. The Xception convolutional neural network architecture is used to establish both qualitative and quantitative correlations between these microscopic images and MLSS/apparent viscosity. The accuracies of qualitative identification for MLSS and apparent viscosity are both higher than 97%, and the quantitative correlation coefficients are 0.95 and 0.96, respectively. This quantitative correlation between microscopic images of activated sludge and its physical parameters, specifically MLSS and apparent viscosity, provides a basis for real-time online measurements of activated sludge parameters in WWTPs.