Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater

Assessing the cyanobacteria disinfection in sewage and its compliance with international-standards requires determining the concentration and viability, which can be achieve using Imaging Flow Cytometry device called FlowCAM. The objective is to thoroughly investigate the sonolytic morphological changes and disinfection-performance towards toxic cyanobacteria existing in sewage using the FlowCAM. After optimizing the process conditions, over 80% decline in cyanobacterial cell counts was observed, accompanied by an additional 10–15% of cells exhibiting injuries, as confirmed through morphological investigation. Moreover, for the first time, the experimentally collected data was utilized to build deep-learning probabilistic-neural-networks (PNN) and natural-gradient-boosting (NGBoost) models for predicting disinfection efficiency and ABD area as target outputs. The findings suggest that the NGBoost model exhibited superior prediction performance for both targets, with high test coefficient of determination (R2 > 0.87) and lower test errors (RMSE < 7.10, MAE < 4.14). The confidence interval examination in NGBoost prediction performance showed a minute variation from the experimentally calculated values, suggesting a high accuracy in model prediction. Finally, SHAP analysis suggests the sonolytic time alone contributes around 50% to the cyanobacteria disinfection. Overall, the findings demonstrate the effectiveness of the FlowCAM device and the potential of machine-learning modeling in predicting disinfection outcomes.

Professor Ki Young Park was supported by the Korea government (MSIT) [NRF-2021R1A2C2011536, No. RS-2023–00219272] and by Korea Environment Corporation through Waste to Energy-Recycling Human Resource Development Project [YL-WE-21–001], while Professor Kyung-Hwa Cho was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) [No. RS-2023–00219272].

Elsevier BV

Journal of Hazardous Materials


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