E.N Banadda

Detection of Filamentous Bulking Problems: Developing an Image Analysis System for Sludge Composition Monitoring

This article describes a fully automatic image analysis procedure for fast and reliable characterization of the activated sludge composition, that is, the floc and filament features. The algorithms developed for each of the analysis steps, that is, segmentation, object recognition, and characterization, are described in detail. Although the application range of the recognition method is a priori expanded by introducing a number of control parameters, the procedure proves to be intrinsically robust as it produces satisfactory results for a fixed set of parameter values for a wide variety of image types.

Image analysis as a monitoring tool for activated sludge properties in lab-scale installations.

Abstract An important step in the battle against filamentous bulking is the development of a monitoring system for activated sludge properties. Therefore, a fully automatic image analysis method for recognizing and characterizing flocs and filaments in activated sludge images has been developed. This procedure has been subsequently used to monitor activated sludge properties in a lab-scale installation. The results of a 100-days experiment indicate that the image information correlates well with the evolution of standard settling properties, in this case the Sludge Volume Index. It is shown that, at the onset of severe filamentous bulking, there is an increase in total filament length on the one hand, and a significant change in floc shape on the other hand.

Identification and Modeling of the Sludge Volume Index by Exploiting Image Analysis Information

Abstract For many years now, activated sludge systems have been employed to treat a wide variety of wastewater. The system performance, however, is limited among other things by the failure of the sedimentation tank to separate and concentrate the activated sludge from the treated effluent. Filamentous bulking is one of the major problems encountered in activated sludge systems. Image analysis is a promising technique that can be used for early detection of filamentous bulking. The underlining aim of this work is twofold; (i) to seek correlations between image analysis information (e.g., the total filament length per image, the mean form factor, the mean equivalent floc diameter, the mean floc roundness, the mean floc reduced radius of gyration) and classical measurements (such as the Sludge Volume Index (SVI)) and, (ii) to both explore and exploit this information in order to identify dynamic ARX and state space type models whose performance is compared based on two criteria.

Optimization of an Image Analysis Procedure for Monitoring Activated Sludge Settleability

Abstract In this paper a fully automated image analysis procedure for quantification and characterization of flocs and filaments is presented, and a number of optimization steps are suggested. This procedure was used to monitor the activated sludge properties in two lab-scale experiments, in order to study the relations between the sludge settleability and the image information. It was found that at the onset of filamentous bulking, an increase of the total filament length and the formation of an, on average, more elongated and rougher floc coincide

Dynamic Modeling of Filamentous Bulking in Lab-Scale Activated Sludge Processes

Abstract Activted sludge is a complex ecosystem constituted mainly of bacteria and protozoa. FIlamentous. bulking, a phenomenon when the filamentous organisms dominate the activated sludge is still a widespread problem in the operation of activated sludge processes. Image Analysis offers Promising Perspectives for early detection of filamentous bulking because the morphology parameters of the activated sludge react very fast to changing process conditions. This paper is aimed at identifying dynamic ARX and state space models as a function of organic loading and digital image analysis information (such as the total filament length per image and some representative mean floc shape parameters) to describe the evolution of the Sludge Volume index (SVI). Their performances are compared based on an adequate quality performance criterion