Automatic identification of activated sludge disturbances and assessment of operational parameters

Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater

Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (N-NH₄⁺), nitrite (N-NO₂^-), nitrate (N-NO₃^-), salinity (Cl^-), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R² of 0.94), N-NH₄⁺ (R² of 0.98), N-NO₂^- (R² of 0.96), N-NO₃^- (R² of 0.95), Cl^- (R² of 0.98), and TSS (R² of 0.94). While the COD and N-NO₂^- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for N-NH₄⁺ assessment. The N-NO₃^-, Cl^- and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.

Monitoring morphological changes from activated sludge to aerobic granular sludge under distinct organic loading rates and increasing minimal imposed sludge settling velocities through quantitative image analysis

Quantitative image analysis (QIA) was used for monitoring the morphology of activated sludge (AS) during a granulation process and, thus, to define and quantify, unequivocally, structural changes in microbial aggregates correlated with the sludge properties and granulation rates. Two sequencing batch reactors fed with acetate at organic loading rates of 1.1 ± 0.6 kg_COD m^-3 d^-1 (R1) and 2.0 ± 0.2 kg_COD m^-3 d^-1 (R2) and three minimal imposed sludge settling velocities (0.27 m h^-1, 0.53 m h^-1, and 5.3 m h^-1) induced distinct granulation processes and rates. QIA results evidenced the turning point from flocculation to granulation processes by revealing the differences in the aggregates' stratification patterns and quantifying the morphology of aggregates with equivalent diameter (Deq) of 200 μm ≤ Deq ≤ 650 μm. Multivariate statistical analysis of the QIA data allowed to distinguish the granulation status in both systems, by clustering the observations according to the sludge aggregation and granules maturation status, and successfully predicting the sludge volume index measured at 5 min (SVI₅) and 30 min (SVI₃₀). These results evidence the possibility of defining unequivocally the granulation rate and anticipating the sludge settling properties at early stages of the process using QIA data. Hence, QIA could be used to predict episodes of granules disruption and hindered settling ability in aerobic granulation sludge processes.

Development of Digital Image Processing as an Innovative Method for Activated Sludge Biomass Quantification

Activated sludge process is the most common method for biological treatment of industrial and municipal wastewater. One of the most important parameters in performance of activated sludge systems is quantitative monitoring of biomass to keep the cell concentration in an optimum range. In this study, a novel method for activated sludge quantification based on image processing and RGB analysis is proposed. According to the results, the intensity of blue color in the macroscopic image of activated sludge culture can be a very accurate index for cell concentration measurement and R² coefficient, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) which are 0.990, 2.000, 0.323, and 13.848, respectively, prove this claim. Besides, in order to avoid the difficulties of working in the three-parameter space of RGB, converting to grayscale space has been applied which can estimate cell concentration with R ² = 0.99. Ultimately, an exponential correlation between RGB values and cell concentrations in lower amounts of biomass has been proposed based on Beer-Lambert law which can estimate activated sludge biomass concentration with R ² = 0.97 based on B index.

Control strategy for filamentous sludge bulking: Bench-scale test and full-scale application

Sludge bulking caused by the overgrowth of filamentous bacteria, especially Microthrix parvicella, has been observed in WWTPs worldwide during low-temperature periods. In this study, the impacts of sludge load on the in situ growth of M. parvicella and sludge settleability were first evaluated at 15 °C over a period of 500 d using a bench-scale anaerobic-anoxic-aerobic reactor fed with raw sewage from a full-scale WWTP. When the reactor was operated at a sludge load of 0.07 ± 0.015 kg Chemical Oxygen Demand (COD) (kg MLSS·d)^-1 for 120 d, the sludge volume index (SVI) increased gradually from 85 mL g^-1 to 157 mL g^-1, and the abundance of M. parvicella quantified by qPCR and FISH methods also increased from 0.42% to 4.63% and 1.56%-13.59%, respectively. When the sludge load was further reduced to 0.04 ± 0.004 kg COD (kg MLSS·d)^-1, the SVI value varied in a narrow range of 135-164 mL g^-1 over a duration of 280 d, while the M. parvicella abundance increased to the maximum values of 10.13% (qPCR) and 18.53% (FISH), respectively. When the sludge load was increased to 0.12 ± 0.016 kg COD (kg MLSS·d)^-1, filamentous abundance and SVI were reduced to 1.06% (qPCR) and 105 mL g^-1 within 100 d, suggesting that it might be possible to control the growth of M. parvicella by keeping the sludge load above 0.1 kg COD (kg MLSS·d)^-1. The feasibility of the strategy was further validated in the same WWTP. It was found that the SVI and filamentous abundance in winter were successfully controlled for two successive years at below 120 mL g^-1 and 7% (FISH), respectively, when the sludge load was maintained at 0.14 ± 0.04 kg COD (kg MLSS·d)^-1 by adjusting sludge discharge, proving that this sludge-load-based strategy could be an efficient approach to control filamentous bulking.

New insights in morphological analysis for managing activated sludge systems

In activated sludge (AS) process, the impact of the operational parameters on process efficiency is assumed to be correlated with the sludge properties. This study provides a better insight into these interactions by subjecting a laboratory-scale AS system to a sequence of operating condition modifications enabling typical situations of a wastewater treatment plant to be represented. Process performance was assessed and AS floc morphology (size, circularity, convexity, solidity and aspect ratio) was quantified by measuring 100,000 flocs per sample with an automated image analysis technique. Introducing 3D distributions, which combine morphological properties, allowed the identification of a filamentous bulking characterized by a floc population shift towards larger sizes and lower solidity and circularity values. Moreover, a washout phenomenon was characterized by smaller AS flocs and an increase in their solidity. Recycle ratio increase and COD:N ratio decrease both promoted a slight reduction of floc sizes and a constant evolution of circularity and convexity values. The analysis of the volume-based 3D distributions turned out to be a smart tool to combine size and shape data, allowing a deeper understanding of the dynamics of floc structure under process disturbances.

Identifying critical components causing seasonal variation of activated sludge settleability and developing early warning tool

Settleability of activated sludge is one of the most common problems that restricts the efficiency of activated sludge system. Obvious seasonal variation of settleability was found in the activated sludge system of a full scale wastewater treatment plant (WWTP) during 2 years of observation. Principal component analysis (PCA) was applied to study the correlation between diluted sludge volume index (DSVI), operational and environmental factors. As a result, temperature and mixed liquid suspended solids (MLSS) were found as the most significant variables relating with DSVI variation. Multivariate regression, partial least squares regression and support vector machine regression were applied to develop early warning models for DSVI prediction. The multivariate regression model was proved as a simple and easy-to-interpret early warning tool to be applied in practice. Based on the ratio of volatile substances in biomass, the original cause of seasonal variation of settleability was further discussed by referring the storage-biodegradation mechanism. Moreover, the results of this work also suggested that modern statistical techniques were important to investigate complicated engineering problems. This study provided insights of seasonal variation of activated sludge settleability by systematic investigation of long-term data of a full scale WWTP.

Generalized classification modeling of activated sludge process based on microscopic image analysis

The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

The effect of seasonal variations on floc morphology in the activated sludge process

The effect of seasonal variations on floc formation in the activated sludge process (ASP) was studied in a municipal wastewater treatment plant in Finland nearly 16 months. Floc formation was measured with an online optical monitoring device, and results were correlated with the temperature of the upcoming wastewater and the treatment efficiency of the ASP. Results showed that floc formation has a clear, seasonal pattern, with flocs in summer being larger and rounder and having fewer filaments and small particles. In addition, treatment efficiency increased in summer. The study correlated the results of image analysis with the composition (chemical oxygen demand and suspended solids content) and temperature of the wastewater before and after the ASP. Results showed that the composition of upcoming wastewater has no clear correlation with floc morphological parameters. However, the wastewater temperature clearly correlated with floc formation. Results indicated that cold winter conditions enhanced the growth of filamentous bacteria in wastewater, decreasing treatment efficiency. Furthermore, these results confirmed that floc formation has seasonal variations.

Statistical monitoring and dynamic simulation of a wastewater treatment plant: A combined approach to achieve model predictive control

The on-line monitoring of Chemical oxygen demand (COD) and total phosphorus (TP) restrains wastewater treatment plants to achieve better control of aeration and chemical dosing. In this study, we applied principal components analysis (PCA) to find out significant variables for COD and TP prediction. Multiple regression method applied the variables suggested by PCA to predict influent COD and TP. Moreover, a model of full-scale wastewater treatment plant with moving bed bioreactor (MBBR) and ballasted separation process was developed to simulate the performance of wastewater treatment. The predicted COD and TP data by multiple regression served as model input for dynamic simulation. Besides, the wastewater characteristic of the wastewater treatment plant and MBBR model parameters were given for model calibration. As a result, R² of predicted COD and TP versus measured data are 81.6% and 77.2%, respectively. The model output in terms of sludge production and effluent COD based on predicted input data fitted measured data well, which provides possibility to enabled model predictive control of aeration and coagulant dosing in practice. This study provide a feasible and economical approach to overcome monitoring and modelling restrictions that limits model predictive control of wastewater treatment plant.

Granulation of Non-filamentous Bulking Sludge Directed by pH, ORP and DO in an Anaerobic/Aerobic/Anoxic SBR

In an anaerobic/aerobic/anoxic (A/O/A) sequencing batch reactor (SBR), non-filamentous bulking sludge granulated after the adjustment of cycle duration and influent composition directed by pH, oxidation-reduction potential (ORP) and dissolved oxygen (DO). The turning points and plateaux of pH, ORP and DO profiles indicated the end of biochemical reactions, such as chemical oxygen demand (COD) consumption, P release, ammonium oxidation, P uptake and denitrification. The difference of nutrient concentration between the beginning and turning points represented the actual treatment capability of the sludge. Non-filamentous bulking with SVI30 of 255 mL g(-1) resulted in a huge biomass loss. After regulation, the cycle duration was shortened from 310 to 195 min without unnecessary energy input. In addition, the settling ability was obviously improved as SVI30 reduced to 28 mL g(-1). Moreover, matured granules with an average diameter of 600 μm were obtained after 45 days, and simultaneous COD, ammonium and phosphate (P) removal was also realized after granulation.

Digital image processing and analysis for activated sludge wastewater treatment

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

A comparison between floc morphology and the effluent clarity at a full-scale activated sludge plant using optical monitoring

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.