DeepEcoWatertreat

Development of Digital Solutions for the Cost-Effective Determination of the Operating Status of Wastewater Treatment Plants

Abstract

Municipal wastewater treatment plants increasingly face the challenge of controlling sludge bulking under changing climatic conditions and fluctuating influent loads without relying on energy- and chemical‑intensive operating strategies. In this study, we present a hybrid image analysis algorithm that combines classical pixel‑based processing with deep learning (YOLOv4‑based object detection) to automatically quantify floc area, filament concentration, and microbial morphological features in phase‑contrast micrographs of activated sludge. The algorithm was evaluated at a municipal wastewater treatment plant subject to sporadic bulking events of unknown origin. Over several months, daily microscopy images were acquired, linked to operational and weather data, and compared between a stable reference period and a problem period with documented bulking events. Neither the filament-related metrics derived by the algorithm nor parallel 16S rRNA and ITS amplicon sequencing indicated increases in filamentous microorganisms, floc enlargement, or significant differences in alpha or beta diversity between reference and problem periods, thus excluding classical filamentous overgrowth as the underlying cause. The proposed algorithm therefore proves to be a robust tool for reliably confirming null events and providing ecologically sound operational guidance by helping to avoid unnecessary coagulant dosing, and it constitutes a key component for future early‑warning systems based on automated microscopic image analysis in wastewater treatment plants.