  Microbial communities play a key role in biological wastewater treatment
processes. Activated sludge settling characteristics, for example, are affected
by microbial community composition, varying by changes in operating conditions
and influent characteristics of wastewater treatment plants (WWTPs). Timely
assessment and prediction of changes in microbial composition leading to
settling problems, such as filamentous bulking (FB), can prevent operational
challenges, reductions in treatment efficiency, and adverse environmental
impacts. This study presents an innovative computer vision-based approach to
assess activated sludge-settling characteristics based on the morphological
properties of flocs and filaments in microscopy images. Implementing the
transfer learning of deep convolutional neural network (CNN) models, this
approach aims to overcome the limitations of existing quantitative image
analysis techniques. The offline microscopy image dataset was collected over
two years, with weekly sampling at a full-scale industrial WWTP in Belgium.
Multiple data augmentation techniques were employed to enhance the
generalizability of the CNN models. Various CNN architectures, including
Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested
to evaluate their performance in predicting sludge settling characteristics.
The sludge volume index was used as the final prediction variable, but the
method can easily be adjusted to predict any other settling metric of choice.
The results showed that the suggested CNN-based approach provides less
labour-intensive, objective, and consistent assessments, while transfer
learning notably minimises the training phase, resulting in a generalizable
system that can be employed in real-time applications.
