A novel image processing-based system for turbidity measurement in domestic and industrial wastewater

Abstract

Wastewater treatment facilities are continually challenged to meet both environmental regulations and reduce running costs (particularly energy and staffing costs). Improving the efficiency of operational monitoring at wastewater treatment plants (WWTPs) requires the development and implementation of appropriate performance metrics; particularly those that are easily measured, strongly correlate to WWTP performance, and can be easily automated, with a minimal amount of maintenance or intervention by human operators. Turbidity is the measure of the relative clarity of a fluid. It is an expression of the optical property that causes light to be scattered and absorbed by fine particles in suspension (rather than transmitted with no change in direction or flux level through a fluid sample). In wastewater treatment, turbidity is often used as an indicator of effluent quality, rather than an absolute performance metric, although correlations have been found between turbidity and suspended solids. Existing laboratory-based methods to measure turbidity for WWTPs, while relatively simple, require human intervention and are labour intensive. Automated systems for on-site measuring of wastewater effluent turbidity are not commonly used, while those present are largely based on submerged sensors that require regular cleaning and calibration due to fouling from particulate matter in fluids. This paper presents a novel, automated system for estimating fluid turbidity. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth, and computer vision processing techniques are used to quantify this characteristic. Results from the proposed system were compared with results from established laboratory-based methods and were found to be comparable. Tests were conducted using both synthetic dairy wastewater and effluent from multiple WWTPs, both municipal and industrial. This system has an advantage over current methods as it provides a multipoint analysis that can be easily repeated for large volumes of wastewater effluent. Although the system was specifically designed and tested for wastewater treatment applications, it could have applications such as in drinking water treatment, and in other areas where fluid turbidity is an important measurement.

Using image processing for determination of settled sludge volume

“…In future, it is envisaged that the turbidity of the supernatant could also be examined, as it is indicative of effluent turbidity. From the work conducted previously (Hongve and Åkesson, 1998;Mullins et al, 2018), it is known that IR light is required for this analysis, as the dissolved organic matter present absorbs broadband white light in different ways. Also, the camera used for this analysis was unsuitable for viewing IR light, as like most consumer cameras, it contains an IR-UV cut filter.…”

“…There has been ongoing research in wastewater assessment and monitoring over the last 20-30 years highlighting the need for the development of low-cost and robust sensors that are capable of providing real-time feedback, therefore enabling operators to make informed decisions (Council of the European Communities, 1991; Gitelson et al, 2008;Mullins et al, 2018). Furthermore, optimising the performance of WWTPs is a key area of focus for the sector in order to meet discharge limits, set by EU legislation (European Commission, 1998;Kim and Hao, 2001;Fitzsimons et al, 2016), as well as reducing operational costs (Fernández et al, 2011;O'Reilly et al, 2011).…”

Using a Digital Camera Combined With Fitting Algorithm and T-S Fuzzy Neural Network to Determine the Turbidity in Water

“…A novel and automatic turbidity estimation system for fluids was proposed [24]. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth and computer vision processing techniques are used to quantify this characteristic.…”

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (Review of 2018 Literature) DIRECT

“…Process performance and floc structure were assessed using computer imaging by Oliveira, Alliet, Coufort-Saudejaud, and Frances (2018) under various conditions. Mullins et al (2018) demonstrated a system for measuring turbidity using computer visioning. Savings in the order of 80% were reported by Lockey and Bhartia (2018) following the implementation of a food processing automated dissolved air flotation (DAF) control platform including online sensors, control algorithms based on artificial intelligence (AI) and remote monitoring was presented.…”