Abstract
This paper explores a method for automating the monitoring process of activated sludge quality control at biological wastewater treatment plants using computer vision technology. The implementation of such a system will reduce the need for manual intervention on the part of microbiological laboratory technicians and provide continuous monitoring of the cleaning process. This method is based on in-depth analysis of activated sludge images obtained using an automated microscope, and subsequent processing of the data using a machine learning model to determine the number and types of microorganisms. The results obtained make it possible to assess the quality of activated sludge based on its microbiological composition in order to, based on the assessment obtained, take the necessary actions to change and adjust the processes of biological wastewater treatment.
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