Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this problem, we proposed a deep learning (DL) method to automatically detect and quantify microparticles free from biomass and entrapped in biomass from microscope images. Firstly, we introduced a “TU Delft-Interaction between Particles and Biomass” dataset containing labeled microscope images. Then, we built DL models using this dataset with seven state-of-the-art model architectures for a instance segmentation task, such as Mask R-CNN, Cascade Mask R-CNN, Yolact and YOLOv8. The results show that the Cascade Mask R-CNN with ResNet50 backbone achieves promising detection accuracy, with a mAP50_box and mAP50_mask of 90.6 % on the test set. Then, we benchmarked our results against the conventional ImageJ processing method. The results show that the DL method significantly outperforms the ImageJ processing method in terms of detection accuracy and processing cost. The DL method shows a 13.8 % improvement in micro-average precision, and a 21.7 % improvement in micro-average recall, compared to the ImageJ method. Moreover, the DL method can process 70 images within 1 min, while the ImageJ method costs at least 6 h. The promising performance of our method allows it to offer a potential alternative to examine the interaction between microparticles and biomass in biological wastewater treatment process in an affordable manner. This approach offers more useful insights into the treatment process, enabling further reveal the microparticles transfer in biological treatment systems.

To investigate energy-saving approaches in wastewater treatment plants and decrease aeration energy consumption, this study successfully established a floc-granule coexistence system in a sequencing batch airlift reactor (SBAR) employing micro-bubble aeration. The analysis focused on granule formation and pollutant removal under various aeration intensities, and compared its performance with a traditional floc-based coarse-bubble aeration system. The results showed that granulation efficiency was positively associated with aeration intensity, which enhanced the secretion of extracellular polymeric substances (EPSs) and facilitated granule formation. The SBAR with the micro-aeration intensity of 30 mL·min-1 showed the best granulation performance (granulation efficiency 52.6%). In contrast to the floc-based system, the floc-granule coexistence system showed better treatment performance, and the best removal efficiencies of NH4+-N, TN, and TP were 100.0, 77.0, and 89.5%, respectively. The floc-granule coexistence system also enriched higher abundance of nutrients removal microbial species, such as Nitrosomonas (0.05-0.14%), Nitrospira (0.14-2.32%), Azoarcus (2.95-12.17%), Thauera (0.43-1.95%), and Paracoccus (0.76-2.89%). The energy-saving potential was evaluated, which indicated it is feasible for the micro-aeration floc-granule coexistence system to decrease the aeration consumption by 14.4% as well as improve the effluent.