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.

Real‐time monitoring of water consumption activities can be an effective mechanism to achieve efficient water network management. This approach, largely enabled by the advent of smart metering technologies, is gradually being practiced in domestic and industrial contexts. In particular, identifying water consumption habits from flow‐signatures, i.e., the specific end‐usage patterns, is being investigated as a means for conservation in both the residential and nonresidential context. However, the quality of meter data is bivariate (dependent on number of meters and data temporal resolution) and as a result, planning a smart metering scheme is relatively difficult with no generic design approach available. In this study, a comprehensive medium‐resolution to high‐resolution smart metering program was implemented at two nonresidential trial sites to evaluate the effect of spatial and temporal data aggregation. It was found that medium‐resolution water meter data were capable of exposing regular, continuous, peak use, and diurnal patterns which reflect group wide end‐usage characteristics. The high‐resolution meter data permitted flow‐signature at a personal end‐use level. Through this unique opportunity to observe water usage characteristics via flow‐signature patterns, newly defined hydraulic‐based design coefficients determined from Poisson rectangular pulse were developed to intuitively aid in the process of pattern discovery with implications for automated activity recognition applications. A smart meter classification and siting index was introduced which categorizes meter resolution in terms of their suitable application.

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The paper will present an overview of one of the Fault Detection and Diagnosis (FDD) systems developed within the Waternomics project. The FDD system has been developed basing on the hydraulic modeling of the water network, the real time values of flow and pressure obtained from installation of innovative ICT and commercial smart meters and the application of the Anomaly Detection with fast Incremental ClustEring (ADWICE) algorithm adapted for the drinking water network. The FDD system developed is useful when we have to consider more than one parameter at the same time to determine if an anomaly or fault is in place in a complex water network and the system is designed on purpose to cope with a larger features set. The new FDD system will be implemented in an Italian demo site, the Linate Airport Water network in Milan, where a large water distribution network is in place and where, due the many variables coming into play, it could be very difficult to detect anomalies with a low false alarm rate.

This paper explores the experiences of partners in the multi-national, EU-funded INNOQUA project, who have developed and are currently demonstrating the potential for novel nature-based, decentralised wastewater treatment solutions in ten different countries. Four solutions are under investigation, each at different Technology Readiness Levels (TRLs): Lumbrifilter; Daphniafilter; Bio-Solar Purification unit; UV disinfection unit. An overview of the solutions is provided, along within data from pilot sites. The project is currently entering an intensive demonstration phase, during which sites will be open for visits and act as the focus for training and dissemination activities on sustainable wastewater treatment. Barriers to market for nature-based solutions are also explored.