Abstract

Water reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved Air Flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens undistortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and Local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough Transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research.