This study describes a robust bubble image recognition algorithm that detects the in-focus, ellipse-like bubble images from experimental images with heavily overlapping bubbles. The principle of the overlapping object recognition (OOR) algorithm is that it calculates the overall perimeter of a segment, finds the points at the perimeter that represent the connecting points of overlapping objects, clusters the perimeter arcs that belong to the same object and fits ellipses on the clustered arcs of the perimeter. The accuracy of the algorithm is studied with simulated images of overlapping ellipses, providing an RMS error of 0.9 pixels in size measurement. The algorithm is utilized in measurements of bubble size distributions with a direct imaging (DI) technique in which a digital camera and a pulsed back light are used to detect bubble outlines. The measurement system is calibrated with stagnant bubbles in a gel in order to define the bubble size dependent effective thickness of the measurement volume and the grey scale gradient threshold as a focus criterion. The described concept with a novel bubble recognition algorithm enables DI measurements in denser bubbly flows with increased reliability and accuracy of the measurement results. The measurement technique is applied to the study of the turbulent bubbly flow in a papermaking machine, in the outlet pipe of a centrifugal pump.

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This study introduces a novel optical monitoring method to image and characterize activated sludge flocs and to study the dependency of sludge settling properties on the floc structure. The novel method can easily analyse thousands of particles in a short timeframe using the developed image analysis program. The main advantage of this method is its applicability for in situ use because the only required pre-treatment is sample dilution. This study tested real process samples from activated sludge plants treating wastewater from a pulp mill. The sludge samples were collected in bulking and non-bulking situations, and the image analysis results were compared to the settling speed of the samples. The structure of the activated sludge flocs was clearly different in bulking sludge situations as characterized by more fragile and elongated flocs. Additionally, excessive amounts of filamentous bacteria hold the flocs apart, hindering sludge settling. These results show that this method is suitable for studying and optimizing activated sludge processes.

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A charge-coupled device camera was used for the optical monitoring of activated sludge flocs and filaments, and the image analysis results were compared with the effluent clarity at a full-scale activated sludge plant during a three-month period. The study included a maintenance stoppage at the wastewater treatment plant, which was followed by a settling problem. Thus, the study presents the development of floc morphology from poor flocculation to good flocculation. In this case, the evolution of flocs was a slow process, and the optimum floc morphology was achieved before the purification results improved. To diagnose the cause of the settling problems using optical monitoring, four major factors were found to be relevant: the mean area of the flocs, the amount of small particles, the amount of filament and the shape parameters of the flocs. In this case, the settling problem was caused by dispersed growth based on the image analysis results. In conclusion, the method used has the potential for usefulness in the development of monitoring applications to predict plant performance and also to diagnose the causes of the settling problems.