A reliable sewage quality abnormal event monitoring system. (15th September 2017) - Li, Tianling; Winnel, Melissa; Lin, Hao; Panther, Jared; Liu, Chang; O'Halloran, Roger; Wang, Kewen; An, Taicheng; Wong, Po Keung; Zhang, Shanqing; Zhao, Huijun
Abstract: With closing water loop through purified recycled water, wastewater becomes a part of source water, requiring reliable wastewater quality monitoring system (WQMS) to manage wastewater source and mitigate potential health risks. However, the development of reliable WQMS is fatally constrained by severe contamination and biofouling of sensors due to the hostile analytical environment of wastewaters, especially raw sewages, that challenges the limit of existing sensing technologies. In this work, we report a technological solution to enable the development of WQMS for real-time abnormal event detection with high reliability and practicality. A vectored high flow hydrodynamic self-cleaning approach and a dual-sensor self-diagnostic concept are adopted for WQMS to effectively encounter vital sensor failing issues caused by contamination and biofouling and ensure the integrity of sensing data. The performance of the WQMS has been evaluated over a 3-year trial period at different sewage catchment sites across three Australian states. It has demonstrated that the developed WQMS is capable of continuously operating in raw sewage for a prolonged period up to 24 months without maintenance and failure, signifying the high reliability and practicality. The demonstrated WQMS capability to reliably acquire real-time wastewater quality information leaps forward the development of effective wastewater source management system. The reported self-cleaning and self-diagnostic concepts should be applicable to other online water quality monitoring systems, opening a new way to encounter the common reliability and stability issues caused by sensor contamination and biofouling. Graphical abstract: Highlights: A reliable sewage quality abnormal event monitoring system was developed. A self-cleaning approach overcomes sensor contamination and biofouling. A self-diagnostic concept enables real-time detection of malfunctioned sensor.