New insights in morphological analysis for managing activated sludge systems

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks

The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in real-time and consistently has been challenging due to the lack of suitable technologies and analytical methods. Here we present a laboratory setup capable of capturing instantaneous microscopic images of activated sludge, along with algorithms to interpret these images. To improve floc identification, an advanced Mask R-CNN-based segmentation that integrates a Dual Attention Network (DANet) with an enhanced Feature Pyramid Network (FPN) was used to enhance feature extraction and segmentation accuracy. Additionally, our novel PointRend module meticulously refines the contours of boundaries, significantly minimising pixel inaccuracies. Impressively, our approach achieved a floc detection accuracy of >95%. This development marks a significant advancement in real-time sludge monitoring, offering essential insights for optimising wastewater treatment operations proactively.

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (Review of 2018 Literature) DIRECT

A review of the literature published in 2018 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented. PRACTITIONER POINTS: Summary of advances in the field of modeling, instrumentation, automation, and optimization in 2018. This review outlines the major contributions of researchers and practitioners that have been published in peer-reviewed journals and conference proceedings. The article is organized into sections for ease of reference, but several reviewed articles are related to more than one section.