Evaluation of activated sludge settling characteristics from microscopy images with deep convolutional neural networks and transfer learning

• DOI: 10.1016/j.jwpe.2024.105692
• Publication Date: 2024-06-27T00:47:55Z
• Authors: Sina Borzooei; Leonardo Scabini; Gisele Miranda; Saba Daneshgar; Lukas Deblieck; Odemir Bruno; Piet De Langhe; Bernard De Baets; Ingmar Nopens; Elena Torfs
• Keywords: FOS: Computer and information sciences; Filamentous bulking; Technology and Engineering; Wastewater treatment plant; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; FILAMENTOUS BACTERIA; BULKING; CLASSIFICATION; Transfer learning; WASTE-WATER TREATMENT; Computational Engineering, Finance, and Science (cs.CE); Microscopy images; Earth and Environmental Sciences; SEPARATION; MORPHOLOGY; Convolutional neural networks; Eigen-CAM; FLOCS; Computer Science - Computational Engineering, Finance, and Science

34 Pages, 8 figures<br/>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.<br/>