Mullins Darragh, Coburn Derek, Hannon Louise, Jones Edward, Clifford Eoghan, Glavin Martin
Electrical and Electronic Engineering, College of Engineering and Informatics, National University of Ireland, Galway, Republic of Ireland E-mail:
School of Physics, College of Science, National University of Ireland, Galway, Republic of Ireland.
Water Sci Technol. 2018 Mar;77(5-6):1469-1482. doi: 10.2166/wst.2018.030.
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.
废水处理设施一直面临着既要符合环境法规又要降低运营成本（尤其是能源和人力成本）的挑战。提高污水处理厂（WWTPs）运营监测的效率需要制定和实施适当的性能指标；特别是那些易于测量、与污水处理厂性能密切相关、易于自动化且人工操作员只需进行最少维护或干预的指标。浊度是衡量流体相对清澈程度的指标。它是一种光学特性的表现，会导致光线被悬浮的细颗粒散射和吸收（而不是在流体样本中毫无方向或通量水平变化地透射）。在废水处理中，浊度通常用作出水水质的指标，而非绝对的性能指标，尽管已发现浊度与悬浮固体之间存在相关性。现有的用于污水处理厂浊度测量的基于实验室的方法虽然相对简单，但需要人工干预且劳动强度大。用于现场测量废水排放浊度的自动化系统并不常用，现有的系统大多基于浸没式传感器，由于流体中颗粒物的污染，需要定期清洁和校准。本文提出了一种用于估计流体浊度的新型自动化系统。对出水样本进行成像，使光吸收特性作为流体深度的函数得到突出显示，并使用计算机视觉处理技术对该特性进行量化。将该系统的结果与既定的基于实验室的方法的结果进行比较，发现具有可比性。使用合成乳制品废水以及多个市政和工业污水处理厂的出水进行了测试。该系统相对于现有方法具有优势，因为它提供了一种多点分析，可轻松对大量废水排放进行重复测量。尽管该系统是专门为废水处理应用设计和测试的，但它可能在饮用水处理等领域以及流体浊度是重要测量指标的其他领域有应用。
