Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Technology, Biotechnology & Applied Microbiology, Engineering, Chemical, Engineering, filamentous bulking, image analysis, system identification, activated sludge systems, ARX and state space models, FLOCS, Algorithms, Bacteria, Aerobic, Biodegradation, Environmental, Biofilms, Bioreactors, Cell Culture Techniques, Colony Count, Microbial, Image Interpretation, Computer-Assisted, Industrial Waste, Microscopy, Video, Models, Biological, Sewage, Statistics as Topic, Water Microbiology, Water Purification, 0903 Biomedical Engineering, 0904 Chemical Engineering, 1003 Industrial Biotechnology, Biotechnology, 3106 Industrial biotechnology, 4004 Chemical engineering

Abstract:

The performance of the activated sludge process is limited by the ability of the sedimentation tank (1) to separate the activated sludge from the treated effluent and (2) to concentrate it. Apart from bad operating strategies or poorly designed clarifiers, settling failures can mainly be attributed to filamentous bulking. Image analysis is a promising technique that can be used for early detection of filamentous bulking. The aim of this paper is therefore twofold. Foremost, correlations are sought between image analysis information (i.e., the total filament length per image, the mean form factor, the mean equivalent floc diameter, the mean floc roundness and the mean floc reduced radius of gyration) and classical measurements (i.e., the Sludge Volume Index (SVI)). Secondly, this information is both explored and exploited in order to identify dynamic ARX and state space-type models. Their performance is compared based on two criteria.