scholarly journals Image Processing and Analysis of Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment Plants

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 1778-1791 ◽  
Author(s):  
Muhammad Burhan Khan ◽  
Humaira Nisar ◽  
Choon Aun Ng
Keyword(s):  
Image Processing ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Phase Contrast ◽  
Wastewater Treatment Plants ◽  
Microscopic Images ◽  
Image Processing And Analysis

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

2017 ◽  
Vol 23 (6) ◽  
pp. 1130-1142 ◽  
Author(s):  
Muhammad Burhan Khan ◽  
Humaira Nisar ◽  
Choon Aun Ng ◽  
Kim Ho Yeap ◽  
Koon Chun Lai
Keyword(s):  
Wastewater Treatment ◽  
Edge Detection ◽  
Activated Sludge ◽  
Phase Contrast ◽  
Clustering Algorithm ◽  
Wastewater Treatment Plants ◽  
False Negative ◽  
Color Space ◽  
Ground Truth ◽  
Space Representation

AbstractImage 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.

scholarly journals Combining Unmanned Aircraft Systems and Image Processing for Wastewater Treatment Plant Asset Inspection

2020 ◽  
Vol 12 (9) ◽  
pp. 1461
Author(s):  
Jorge Sancho Martínez ◽  
Yadira Bajón Fernández ◽  
Paul Leinster ◽  
Mónica Rivas Casado
Keyword(s):  
Image Processing ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Trickling Filters ◽  
Aircraft Systems

Wastewater treatment plants are essential for preserving the water quality of freshwater and marine ecosystems. It is estimated that, in the UK, as much as 11 billion liters of wastewater are treated on a daily basis. Effective and efficient treatment of wastewater requires treatment plants to be maintained in good condition. Recent studies have highlighted the potential of unmanned aircraft systems (UASs) and image processing to be used in autonomous and automated monitoring systems. However, the combined use of UASs and image processing for wastewater treatment plant inspections has not yet been tested. This paper presents a novel image processing-UAS framework for the identification of failures in trickling filters and activated sludge facilities. The results show that the proposed framework has an accuracy of 95% in the detection of failures in activated sludge assets, with the accuracy ranging between 55% and 81% for trickling filters. These results are promising and they highlight the potential use of the technology for the inspection of wastewater treatment plants.

scholarly journals Segmentation of Activated Sludge Filaments using Phase Contrast Images

2018 ◽  
Vol 11 (3) ◽  
pp. 145-153
Author(s):  
Yuen Hang Ho ◽  
Humaira Nisar ◽  
Muhammad Burhan Khan
Keyword(s):  
Waste Water ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Phase Contrast ◽  
Treatment Plant ◽  
Aeration Tank ◽  
Wastewater Sample ◽  
Microscopic Images ◽  
Segmentation Algorithms ◽  
Edge Based

Segmentation algorithms play an important role in image processing and analysis. The identification of objects and process monitoring strongly depends on the accuracy of the segmentation algorithms. Waste water treatment plants are used to treat wastewater from municipal and industrial plants. Activated sludge process is used in wastewater treatment plants to biodegrade the organic constituents present in waste water. This biodegradation is done with the help of microorganisms and bacteria. There are two important types of microscopic organisms present in the activated sludge plants, named as flocs as filaments, which are visible under microscope. In this paper we study the microscopic images of wastewater using phase contrast microscopy. The images are acquired from wastewater sample using a microscope. The samples of wastewater are collected from domestic wastewater treatment plant aeration tank. Our main aim is to segment threadlike organisms knows as filaments. Several segmentation algorithms (such as edge based algorithm, k-means algorithm, texture based algorithm, and watershed algorithm) will be explored and their performance will be compared using gold approximations of the images. The performance of the algorithms are evaluated using different performance metrics, such as Rand Index, specificity, variation of information, and accuracy. We have found that edge based segmentation works well for phase contrast microscopic images of activated sludge wastewater.

scholarly journals The capacity of wastewater treatment plants drives bacterial community structure and its assembly

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Young Kyung Kim ◽  
Keunje Yoo ◽  
Min Sung Kim ◽  
Il Han ◽  
Minjoo Lee ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Community Structure ◽  
Bacterial Community ◽  
Activated Sludge ◽  
Community Assembly ◽  
Bacterial Communities ◽  
Wastewater Treatment Plants ◽  
High Capacity ◽  
Operating Conditions ◽  
Rrna Gene

Abstract Bacterial communities in wastewater treatment plants (WWTPs) affect plant functionality through their role in the removal of pollutants from wastewater. Bacterial communities vary extensively based on plant operating conditions and influent characteristics. The capacity of WWTPs can also affect the bacterial community via variations in the organic or nutrient composition of the influent. Despite the importance considering capacity, the characteristics that control bacterial community assembly are largely unknown. In this study, we discovered that bacterial communities in WWTPs in Korea and Vietnam, which differ remarkably in capacity, exhibit unique structures and interactions that are governed mainly by the capacity of WWTPs. Bacterial communities were analysed using 16S rRNA gene sequencing and exhibited clear differences between the two regions, with these differences being most pronounced in activated sludge. We found that capacity contributed the most to bacterial interactions and community structure, whereas other factors had less impact. Co-occurrence network analysis showed that microorganisms from high-capacity WWTPs are more interrelated than those from low-capacity WWTPs, which corresponds to the tighter clustering of bacterial communities in Korea. These results will contribute to the understanding of bacterial community assembly in activated sludge processing.

scholarly journals Deep learning-based morphology classification of activated sludge flocs in wastewater treatment plants

2021 ◽  
Author(s):  
Hisashi Satoh ◽  
Yukari Kashimoto ◽  
Naoki Takahashi ◽  
Takashi Tsujimura
Keyword(s):  
Wastewater Treatment ◽  
Deep Learning ◽  
Activated Sludge ◽  
Morphological Change ◽  
Wastewater Treatment Plants ◽  
Filamentous Bacteria ◽  
Sludge Flocs

A deep learning-based two-label classifier 1 recognized a 20% morphological change in the activated flocs. Classifier-2 quantitatively recognized an abundance of filamentous bacteria in activated flocs.

Controlling the Settling of Activated Sludge in Pulp and Paper Wastewater Treatment Plants

1999 ◽  
Vol 40 (11-12) ◽  
pp. 223-229 ◽  
Author(s):  
Frédéric Clauss ◽  
Christel Balavoine ◽  
Delphine Hélaine ◽  
Gaëtan Martin
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Wastewater Treatment Plants ◽  
Pulp And Paper ◽  
Organic Load ◽  
Forest Industry ◽  
Filamentous Bulking ◽  
Mineral Particles ◽  
Mineral Powder

Forest industry wastewaters are difficult to clean: hydraulic and organic load variations, filamentous bulking or pin-point flocs negatively impact depollution processes. The addition of a fine, mineral, talc-based powder, Aquatal, into the aeration tanks of wastewater treatment plants connected to pulp and paper factories has been successfully tested since end of '97. The first case-study presents full results obtained over a period of 18 months in a 20,000 p.e. plant connected to a paper factory. The mineral powder was regularly added to control sludge volume index, thereby ensuring low suspended solids concentration in the outfluent. Plant operators could easily adapt biomass concentration to match organic load variation, thereby maintaining pollution micro-organisms ratio constant. In a second case study, a trouble-shooting strategy was implemented to counteract filamentous bulking. A one-off, large dosage enabled the plant operator to deal effectively with poor settleability sludge and rapidly control sludge blanket expansion. In both cases, the main common characteristics observed were an increase in floc aggregation and the production of heavier and well-structured flocs. The sludge settling velocity increased and an efficient solid/liquid separation was obtained. After a few days, the mineral particles of Aquatal were progressively integrated into the sludge floc structure. When the mineral powder was added to the activated sludge in the aeration basin, chemical interactions frequently encountered with other wastewater treatment additives did not pose a problem. Moreover, with this mineral additive, the biological excess sludge displayed good thickening properties and dewatering was improved. Despite the addition of the insoluble mineral particles, the amount of wet sludge expelled did not increase. Aquatal offers a rapid solution to floc settleability problems which so frequently arise when physical or biological disorders appear in forest industry wastewater treatment plants.

PARAMETERS FOR DYNAMIC SIMULATION OF W ASTEW ATER TREATMENT PLANTS WITH HIGH-RATE AND LOW-RATE ACTIVATED SLUDGE TANKS

1994 ◽  
Vol 30 (4) ◽  
pp. 211-214 ◽  
Author(s):  
E. Brands ◽  
M. Liebeskind ◽  
M. Dohmann
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Dynamic Simulation ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
High Rate ◽  
Yield Coefficient ◽  
Municipal Wastewater Treatment ◽  
Municipal Wastewater Treatment Plants ◽  
Low Rate

This study shows a comparison of important parameters for dynamic simulation concerning the highrate and low-rate activated sludge tanks of several municipal wastewater treatment plants. The parameters for the dynamic simulation of the single-stage process are quite well known, but parameters for the high-ratellow-rate activated sludge process are still missi ng, although a considerable number of wastewater treatment plants are designed and operated that way. At present any attempt to simulate their operation is restricted to the second stage due to missing data concerning growth rate, decay rate, yield coefficient and others.

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