>50 % of the organic matter in sewage consist of particulate chemical oxygen demand (pCOD). This study used 250 μm fluorescent microbeads, 130±58 μm microparticles and 100 nm nanobeads to simulate sewage particles, and investigated the fate of these particles under both plug flow feeding and aeration phases in an aerobic granular sludge (AGS) system. Filtration performance was dominantly influenced by the particle size rather than the upflow velocity (V_upflow). The microbeads exhibited 95±3 % filtration efficiency with obvious accumulation around the AGS bed bottom, even as slight fluidization started at the V_upflow of 5.0 m·h^-1. In contrast, the nanobeads filtration efficiency was significantly lower (43±6 %). During the aeration phase, the attachment efficiency increased with the decrease of particle size. The microbeads attachment efficiency variated between 39–49 %, whereas the microparticles and nanobeads achieved better attachment of 89.4–95.2 % and 98.8–99.3 %, respectively. Furthermore, aeration batch tests showed both nanobeads and the irregular microparticles attachment by AGS was strong, and the detach-attach of nanobeads/microparticles between different sized AGS was very limited duration aeration. This work provides insight into the fate of particles in AGS system. The optimal sludge treatment was also evaluated in the scope of this removal of non-biodegradable, and potentially harmful particles.

Particle-bubble collisions in dissolved air flotation (DAF) systems play a crucial role in the removal of total suspended solids (TSS). DAF particle-bubble collision models incorporate factors such as particle diameters, charge and density, bubble diameters, and collision factors. The challenge lies in accounting for the wide range of particle and bubble sizes and obtaining complex model inputs. To address this, a simplified model for TSS removal in DAF units was established using low-cost laboratory measurements, including particle size distribution and density. Additionally, microbubble diameter profiles were derived from bubble velocities using particle image velocimetry software (PIV). Six independent variables, encompassing influent particle characteristics (such as particle size distribution and density) and DAF running characteristics (temperature, contact zone detention time, inflow and recycle flows), were employed in the simplified model. The model's accuracy was evaluated using a laboratory-scale DAF system with two different influents: Delft canal water and anaerobic sludge. The predicted TSS removal from the simplified model aligned well with the laboratory-scale DAF results, yielding removal efficiencies of 68 ± 1 % and 77 ± 3 % for Delft canal water and anaerobic sludge, respectively. Furthermore, when the simplified model was applied to two full-scale DAF systems, it successfully identified an underperforming system (DAF2) with a TSS removal efficiency of 91 %, contrasting with the theoretical removal model-predicted efficiency of 98 %. This study highlights the utility of combining bubble size distribution measured by PIVlab and particle size distribution obtained using FIJI-ImageJ as an economical and efficient approach to acquiring the necessary inputs for predicting TSS removal in DAF systems.

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.

The Barapullah drain crosses through New Delhi, India, and transports millions of cubic meters of stormwater, municipal sewage and industrial sewage to the Yamuna River. Seasonal variations and ambiguous annual discharges cause 20-fold fluctuations in hydraulic flows, pollutants type and concentration. Furthermore, New Delhi is among the most densely populated areas on the planet, with limited surface area and high water stress. Dissolved Air Flotation (DAF) units are known to be highly compact, robust, and an efficient suspended solids separation technology that enables further water recovery in a treatment train. Thus, a down-scaled column DAF was designed and used to determine the total suspended solids removal efficiencies, under different influent conditions. Three influents that resemble the Barapullah drain seasonal variations in composition, and a fourth that imitates the feed of DAF when located after an anaerobic bioreactor were tested. A total of 60 batch DAF experiments were completed and used to assess seven independent control variables for DAF operation, which are influent Total Suspended Solids (TSS), pH, temperature, DAF particles residence time, white water pressure, coagulants and flocculants concentration, and coagulation and flocculation time. Results showed that the down-scaled DAF could be steered from low to high removal efficiencies, comparable to full-scale systems. Maximum TSS removal varied between 92 and 96%. The effect and statistical relevance of the different performance variables on the measured separation efficiencies depended on the influent type. All variables, except temperature and pH, had a significant performance effect with a p-value below 0.1, for at least one influent. Pressure had a positive effect on separation efficiency, due to its importance in bubble formation. Moreover, the down-scaled DAF system had low removal efficiency for particles with spherical shapes, and diameters below 10 µm. Based on the high TSS removal for all tested influents, and the effect of the studied control variables, a full-scale DAF could efficiently remove the suspended solids of the Barapullah drain. The unit robustness for different flows and pollutant concentrations, and small footprint, show DAF suitability as part of a treatment train for water recovery, in densely populated areas.

Small-scale electrical power generation (<100 kW) from biogas plants to provide off-grid electricity is of growing interest. Currently, gas engines are used to meet this demand. Alternatively, more efficient small-scale solid oxide fuel cells (SOFCs) can be used to enhance electricity generation from small-scale biogas plants. Most electricity generators require a constant gas supply and high gas quality in terms of absence of impurities like H ₂S. Therefore, to efficiently use the biogas from existing decentralized anaerobic digesters for electricity production, higher quality and stable biogas flow must be guaranteed. The installation of a biogas upgrading and buffer system could be considered; however, the cost implication could be high at a small scale as compared to locally available alterna-tives such as co-digestion and improved digester operation. Therefore, this study initially describes relevant literature related to feedstock pre-treatment, co-digestion and user operational practices of small-scale digesters, which theoretically could lead to major improvements of anaerobic digestion process efficiency. The theoretical preamble is then coupled to the results of a field study, which demonstrated that many locally available resources and user practices constitute frugal innovations with potential to improve biogas quality and digester performance in off-grid settings.