Using image processing for determination of settled sludge volume. 2018

Darragh Mullins, and Derek Coburn, and Louise Hannon, and Edward Jones, and Eoghan Clifford, and Martin Glavin
Electrical and Electronic Engineering, College of Engineering and Informatics, National University of Ireland, Galway, Republic of Ireland E-mail: d.mullins4@nuigalway.ie.

Determination of the sludge volume index is key to describing the settling characteristics of sludge in the aeration process of wastewater treatment plants (WWTPs). The two core components of this calculation are the settled sludge volume (SSV) and suspended solids. While the measurement procedure for SSV is generally defined by national or international standards, in practice a wide variety of vessel sizes and shapes are used by operators to monitor WWTP performance. Furthermore, differences in how these tests are carried out can lead to poor data, inefficient WWTP operation and a lack of comparable metrics for WWTP operational monitoring. Thus, there is a requirement to improve operational performance of WWTPs to meet the increasingly stringent legislation regarding discharge limits. The aim of this study was to utilise a novel image-processing system (AutoSSV) to (i) determine its efficacy in describing SSV and (ii) measure and compare different methodologies for measurement of SSV. The AutoSSV system was tested using samples from various WWTPs and the results compared to those determined by standard manual measurement. Both standard and modified settlement tests were conducted on 30 mixed liquor samples, with modified settlement tests consistently resulting in lower SSV measurements. Results from the study showed a strong correlation between the SSV measurements provided by the AutoSSV system and results obtained from current manual measurement methods. The proposed technique would help to standardise the measurement in practice and increase the frequency of monitoring, particularly in small-scale rural WWTPs where there may not be permanent operators on site, and thus provide sufficient performance monitoring for efficient and effective operation.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D012722 Sewage Refuse liquid or waste matter carried off by sewers. Sludge,Sludge Flocs
D014865 Waste Disposal, Fluid The discarding or destroying of liquid waste products or their transformation into something useful or innocuous. Disposal, Fluid Waste,Disposals, Fluid Waste,Fluid Waste Disposal,Fluid Waste Disposals,Waste Disposals, Fluid

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