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

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.

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.

Correction of ocular and atmospheric wavefronts: a comparison of the performance of various deformable mirrors.

The main applications of adaptive optics are the correction of the effects of atmospheric turbulence on ground-based telescopes and the correction of ocular aberrations in retinal imaging and visual simulation. The requirements for the wavefront corrector, usually a deformable mirror, will depend on the statistics of the aberrations to be corrected; here we compare the spatial statistics of wavefront aberrations expected in these two applications. We also use measured influence functions and numerical simulations to compare the performance of eight commercially available deformable mirrors for these tasks. The performance is studied as a function of the size of the optical pupil relative to the actuated area of the mirrors and as a function of the number of modes corrected. In the ocular case it is found that, with the exception of segmented mirrors, the performance is greatly enhanced by having a ring of actuators outside the optical pupil, as this improves the correction of the pupil edge. The effect is much smaller in the case of Kolmogorov wavefronts. It is also found that a high Strehl ratio can be obtained in the ocular case with a relatively low number of actuators if the stroke is sufficient. Increasing the number of actuators has more importance in the Kolmogorov case, even for the relatively weak turbulence considered here.