The effect of turbidity levels and Moringa oleifera concentration on the effectiveness of coagulation in water treatment.

Laboratory experiments were carried out to assess the water purification and antimicrobial properties of Moringa oleifera (MO). Hence different concentrations (25 to 300 mg/L) were prepared from a salt (1 M NaCl) extract of MO fine powder and applied to natural surface water whose turbidity levels ranged from 50 to 450 NTU. The parameters determined before and after coagulation were turbidity, pH, colour, hardness, iron, manganese and Escherichia coli. The experiments showed that turbidity removal is influenced by the initial turbidity since the lowest turbidity removal of 83.2% was observed at 50 NTU, whilst the highest of 99.8% was obtained at 450 NTU. Colour removal followed the same trend as the turbidity. The pH exhibited slight variations through the coagulation. The hardness removal was very low (0 to 15%). However, high removals were achieved for iron (90.4% to 100%) and manganese (93.1% to 100%). The highest E. coli removal achieved was 96.0%. Its removal was associated with the turbidity removal. The optimum MO dosages were 150 mg/L (50 NTU and 150 NTU) and 125 mg/L for the rest of the initial turbidity values. Furthermore all the parameters determined satisfied the WHO guidelines for drinking water except for E. coli.

Use of image analysis for sludge characterisation: studying the relation between floc shape and sludge settleability.

This paper starts by presenting a fully automatic image analysis procedure for characterisation of flocs and filaments in activated sludge images. Thereafter the attention is directed towards the results of four lab-scale experiments, in which image information is related to sludge settleability in terms of sludge volume index. This relation is statistically confirmed by applying a principal component analysis to the data. In addition, the redundancy in the data sets is studied with regard to floc shape descriptors and the monitoring potential of image analysis is demonstrated by means of a multiple linear regression exercise.

Activated sludge image analysis system: monitoring settleability and effluent clarity.

A fully automized image analysis procedure is presented for fast and reliable characterization of the activated sludge composition. In previous research, relations between filament abundance and floc shape on the one hand, and the Sludge Volume Index on the other hand were sought. This work aims at demonstrating the use of the image analysis information as an indication for the amount of suspended solids in the effluent.

Monitoring activated sludge settling properties using image analysis.

The goal of this study is to develop a monitoring system for activated sludge properties, as this is an essential tool in the battle against filamentous bulking. A fully automatic image analysis procedure for recognising and characterising flocs and filaments in activated sludge images has been optimised and subsequently used to monitor activated sludge properties in a lab-scale installation. The results of two experiments indicate that the image information correlates well with the Sludge Volume Index. It is shown that, at the onset of filamentous bulking, there is an increase in total filament length on the one hand, and a change in floc shape on the other hand.

Predicting the onset of filamentous bulking in biological wastewater treatment systems by exploiting image analysis information.

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.

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

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.