Distinção de fenómenos de bulking em lamas activadas por técnicas de análise de imagem

No corrente trabalho pretendeu-se detectar e identificar diferentes tipos de perturbações em lamas activadas (bulking filamentoso, bulking viscoso e crescimento de flocos pin point) por técnicas de processamento e análise de imagem. Para o efeito foram determinados os parâmetros operacionais sólidos suspensos totais (SST) e índice volumétrico de lamas (IVL), assim como diversos parâmetros morfológicos (conteúdo e morfologia da biomassa agregada e filamentosa), obtidos por análise de imagem. Os resultados obtidos permitiram o esclarecimento das diferentes inter-relações presentes entre cada uma das condições estudadas e os parâmetros que caracterizaram a biomassa microbiana, assim como a aferição do parâmetro operacional IVL, a partir da caracterização da biomassa

Study of chemical oxygen demand and ammonia removal efficiencies by image analysis and multivariate statistics tools

Activated sludge systems are frequently used in wastewater treatment for chemical oxygen demand (COD) and ammonia removal. However, several problems can affect the operation of these systems leading to abnormal conditions such as filamentous bulking, viscous bulking and pinpoint flocs, among others. These occurrences, which may lead to the decrease of COD and ammonia removal efficiencies, are linked to biomass morphological and physiological changes and can be studied by microscopic evaluation. However, traditional microscopic inspection by a human operator, and correspondent manual assessment, is a subjective and labor intensive procedure. Automated image processing and analysis presents considerable convenience in such cases. For this study, a lab-scale activated sludge reactor was operated for 100 days and monitored through microscopic staining and image analysis. The operational parameters were modified inducing the above mentioned abnormal conditions, apart from the normal operation. Biomass morphology was obtained by bright field microscopy combined with grayscale image processing. Biomass physiology was also studied by employing epifluorescence combined with color image processing. The LIVE/DEAD® BacLight™ Bacterial Viability Kit was employed to determine the biomass viability, and the LIVE BacLight™ Bacterial Gram Stain Kit for the biomass Gram status. Two ad-hoc Matlab specially developed programs were employed. COD and ammonia removal efficiencies were studied by clustering the data points in two large clusters: “95% or above” and “below 95%” for the COD, and “90% or above” and “below 90%” for ammonia. These clusters were selected based on the behavior of these two parameters throughout the experiment time. The results showed that the COD removal efficiency was well predicted by the best 10 physiological parameters with an overall accuracy of 94.1%, for the ensemble of the tested conditions. Relatively high accuracies of 90.6% and 91.2% were also obtained for the ammonia removal efficiency regarding the best 9 physiological and morphological parameters, respectively. Thus, for the ammonia removal efficiency both types of parameters are equally useful, leading to 95.3% accuracy when the best 3 physiological and 6 morphological parameters were used

Monitoring filamentous bulking and pin-point flocs in a lab-scale activated sludge system using image analysis

Activated sludge processes are the most frequently used techniques regarding biological wastewater treatment. However, depending on the process operation conditions, several malfunctions could take place, in which filamentous bulking and deflocculation processes, such as pin-point flocs, are the most common problems, causing the sludge settling ability decrease and effluent quality deterioration. Bright field Image analysis is nowadays considered a powerful tool to quantitatively characterize aggregated and filamentous bacteria. Furthermore, the use of epifluorescent staining techniques, coupled to image analysis, presents a promising method to determine bacteria gram nature and viability. Encouraged by the success of image analysis procedures over the last years, the present work studied a lab-scale activated sludge system, under operation conditions causing filamentous bulking and pin-point flocs phenomena. Sludge settling ability and turbidity values were measured verifying the nature of the settling problem. COD contents, as well as nitrogen contents, in terms of N-NH4+, N-NO3- and N-NO2-, were surveyed in the feeding effluent, reactor bulk and settler. Regarding the biomass characterization, four major morphological descriptors groups were studied, covering free filamentous bacteria contents, aggregates contents, aggregates size and aggregates morphology. With respect to the aggregates characterization, these were divided in 3 classes (large, intermediate and small aggregates) according to their size. Percentages of gram-positive bacteria, gram-negative bacteria, viable and damaged bacteria were also evaluated based on fluorescent image analysis. Finally, the raw resulting data was fed into a multivariate statistical analysis, in order to enlighten the relationships between the obtained image analysis information and operational parameters. An improvement of the sludge morphological characterisation was found by combining fluorescent and bright field image analysis procedures. Furthermore, the results obtained during the monitoring period indicate that automated image analysis can help clarifying the nature of the events within the aeration tank, when the system is submitted to disturbances

Automatic identification of activated sludge disturbances and assessment of operational parameters

Activated sludge systems are prone to be affected by changes in operating conditions leading to problems such as pinpoint flocs formation, filamentous bulking, dispersed growth, and viscous bulking. These problems are often related with the floc structure and filamentous bacteria contents. In this work, a lab-scale activated sludge system was operated sequentially obtaining filamentous bulking, pinpoint floc formation, viscous bulking and normal conditions. Image processing and analysis techniques were used to characterize the contents and structure of aggregated biomass and the contents of filamentous bacteria. Further principal component and decision trees analyses permitted the identification of different conditions from the collected morphological data. Furthermore, a partial least squares analysis allowed to estimate the sludge volume index and suspended solids key parameters. The obtained results show the potential of image analysis procedures, associated with chemometric techniques, in activated sludge systems monitoring.The authors acknowledge the financial support to D.P.M. through the post-doctoral grant SFRH/BPD/82558/2011 and to the Project PTDC/EBB-EBI/103147/2008 both funded by Fundacao para a Ciencia e a Tecnologia (Portugal) and Fundo Social Europeu (FSE)

Improved image analysis procedures for monitoring activated sludge systems with filamentous bulking

Activated sludge systems are frequently used in wastewater treatment plant. This process is the most suitable and studied system. However, several problems are being always detected, such as filamentous bulking. Filamentous bulking is typically caused by an overabundance of filamentous organisms that interfere with the settling and compaction process. This phenomenon can be studied and related with settling parameters by automated image analysis using different microscopy acquisitions. However, by using these standard image analysis procedures some relevant information about the state of the sludge is enclosed. Conventional routines, using monochrome images are not suitable to detect the filamentous bacteria which are gram-positive or gram-negative. Moreover, the traditional image acquisition methodologies are not capable to detect both viable and damaged bacteria present within the sludge. Presently, the gram-stain evaluation is performed by visual inspection and manual counting using a microscope which is a tedious procedure. Also, to overcome the lack of viability information, an epifluorescence staining method composed with two nucleic acid-binding stains can be used. For this study, a lab-scale activated sludge reactor was monitored during 100 days through image analysis information and the operational parameters were modified inducing filamentous bulking. Morphological changes were investigated by using new acquisition methods such as epifluorescence staining LIVE/DEAD® BacLight™ Bacterial Viability Kit, the LIVE BacLight™ Bacterial Gram Stain Kit and the traditional bright field. The overall results revealed an improvement of the sludge morphological characterisation, combining these new image analysis procedures with the conventional routines

Predicting SVI from activated sludge systems in different operating conditions through quantitative image analysis

Book of abstracts of the Meeting of the Institute for Biotechnology and Bioengineering, 2, Braga, Portugal, 2010In wastewater treatment it is well documented that a variety of bulking phenomena, as well as other disturbances, can affect the normal behaviour of an activated sludge system, leading to lower treatment efficiency and biomass settleability. In the last few years, quantitative image analysis approaches, coupled to multivariate statistical analysis, have been increasingly used to clarify filamentous bulking detection and monitoring in activated sludge processes [1,2]. The present study focuses on predicting the Sludge Volume Index (SVI) for different types of conditions affecting an activated sludge system. To that effect, four experiments were conducted simulating filamentous bulking, zoogleal bulking, pin-point floc formation, and normal conditions. Alongside the SVI determination, the aggregated and filamentous biomass contents and morphology was studied, as well as the biomass Gram and viability status. Upon the determination of the image analysis data, regression analysis and partial least squares were used to reduce the dataset and model each studied condition. The obtained biomass contents and morphology data allowed establishing an SVI prediction ability presenting a regression value (R2) of 0.8834, whereas the Gram and viability status data allowed for a regression value (R2) of 0.793. It was also found that reasonable to good SVI prediction abilities were obtained using the biomass contents and morphology data, presenting correlation factors (R2) of 0.7686 for the filamentous bulking conditions, 0.7831 for pin point floc formation, 0.9261 for zoogleal bulking and 0.8275 for normal conditions

Characterisation of activated sludge abnormalities by image analysis and multivariate statistics

The ability to distinguish between different types of abnormalities affecting wastewater activated sludge systems, by means of image analysis and chemometrics methodologies, was studied in the current work. Three experiments were performed in a pilot plant activated sludge system, during periods ranging from 47 to 85 days each, in a total of 108 samples, reflecting filamentous bulking, zoogleal bulking and pin point flocs conditions. These samples were further analyzed for the determination of image analysis parameters and of the most commonly surveyed operating parameters. Regarding the image analysis methodology three aliquots of each sample were visualized by bright field and fluorescence microscopy, and a total of 150 images per sample were acquired. These images were then treated by image processing software allowing the study of the contents and morphology of aggregated and filamentous bacteria, resulting in the determination of parameters reflecting the aggregates size distribution, filamentous to aggregated biomass ratio, and biomass viability. A Principal Components Analysis was then carried out on the obtained data to identify each studied conditions, with cross-validation (CV) as the criterion to determine the optimal number of significant components. Although the optimal number of principal components, obtained by the CV method, was found to be 7, explaining 91.9 % of the data variability, it was found that the use of the 2 first principal components (explaining 61.2 % of the data variability) allowed to clearly identify all three conditions (pin point flocs, filamentous and zoogleal bulking). In fact, the samples score plot in these two principal components presented three distinct and non-overlapping zones, reflecting the three different studied conditions. Furthermore, it allowed for the identification and exclusion of 5 outliers within the dataset. Analysing the loading contribution for these 2 first principal components, it was evident the preponderance of the aggregates size distribution and of the filamentous to aggregated biomass ratio, reflecting the importance of these two groups of parameters in the identification and enlightenment of activated sludge abnormalities

Identifying different types of bulking in an activated sludge system through quantitative image analysis

The present study proposes an image analysis methodology for the identification of different types of disturbances in wastewater treatment activated sludge systems. Up to date, most reported image analysis methodologies have been used in activated sludge processes with the aim of filamentous bulking detection, however, other disturbances could be foreseen in wastewater treatment plants. Such disturbances can lead to fluctuations in the biomass contents, affecting the mixed liquor suspended solids (MLSS), and in the sludge settling ability, affecting the sludge volume index (SVI). Therefore, this work focuses on predicting the MLSS and SVI parameters for different types of disturbances affecting an activated sludge system. Four experiments were conducted simulating filamentous bulking, zoogleal or viscous bulking, pinpoint floc formation, and normal operating conditions. Alongside the MLSS and SVI determination, the aggregated and filamentous biomass contents and morphology were studied as well as the biomass Gram and viability status, by means of image analysis.The authors acknowledge the financial support to D.P.M. through the Grant SFRH/BD/32329/2006 and the project PTDC/EBB-EBI/103147/2008 provided by Fundacao para a Ciencia e a Tecnologia

Polyhydroxyalkanoate granules quantification in mixed microbial cultures: Sudan Black B versus Nile Blue A staining

Polyhydroxyalkanoates (PHAs) are intracellular granules found in a wide variety of microorganisms under limited nutrient conditions when carbon source is available in excess. These polymers, usually from lipid nature, are used as carbon and energy sources for metabolic synthesis and growth. Despite the important role of PHAs in cell physiology, they are regarded as potential substitutes of traditional petrochemical plastics with the additional advantage of being completely biodegradable and produced from mixed microbial cultures (MMC). PHA quantification is regularly accomplished using a digestion step prior to chromatography analysis which is a labor and time-consuming technique. To overcome these limitations in polymers quantification, the present work investigates two methods for PHA granules identification based on quantitative image analysis (QIA) procedures in an enhanced biological phosphorus removal (EBPR) system operated for three months. MMC were analyzed for PHA granules detection by Sudan Black B (SBB) and Nile Blue A (NBA) staining using bright-field and epifluorescence microscopy, respectively. The captured color images were evaluated through QIA and the image analysis data was further processed using multivariate statistical analysis. Quite satisfactory partial least squares (PLS) regressions (R2) of 0.85 for NBA and 0.86 for SBB were established between PHA concentrations predicted from QIA parameters and determined by the standard analytical method. Although SBB staining procedure was found to provide a somewhat higher estimation of PHA concentrations in MMC, the consistency between PLS results allowed to conclude that both SBB and NBB staining methods combined with QIA procedures demonstrated the capability to estimate PHA concentrations. Concluding, both staining procedures are promising alternative for a faster PHA assessment relatively to the laborious standard PHA quantification

Estimation of effluent quality parameters from an activated sludge system using quantitative image analysis

Abstract The efficiency of an activated sludge system is generally evaluated by determining several key parameters related to organic matter removal, nitrification and/or denitrification processes. Off-line methods for the determination of these parameters are commonly labor, time consuming, and environmentally harmful. In contrast, quantitative image analysis (QIA) has been recognized as a prompt method for assessing activated sludge contents and structure. In the present study an activated sludge system was operated under different experimental conditions leading to a variety of operational data. Key parameters such as chemical oxygen demand (COD) and ammonium (N-NH4+), and nitrate (N-NO3-) concentrations, throughout the experimental periods, were measured by classical analytical techniques. QIA was further used for the microbial community characterization. Partial Least Squares (PLS) models were used to correlate QIA information and the aforementioned key parameters. It was found that the use of the morphological and physiological data allowed predicting, at some extent, the effluent COD, N-NH4+, and N-NO3- concentrations based on chemometric techniques.The authors thank the FCT Strategic Project of UID/BIO/04469/2013 unit and the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462). The authors also acknowledge the financial support to Daniela P. Mesquita through the postdoctoral Grant (SFRH/BPD/82558/2011) provided by FCT - Portugal