Sewer Corrosion Prediction for Sewer Network Sustainability

Abstract

A large amount of wastewater is generated every moment in the earth because of various activities of humans. The safe delivery with sewer pipe from its occurrence place to the treatment factory significantly affects the earth’s land, air and clean water. The leaking of wastewater will result in serious environmental consequences and social problems, e.g. odour complaint. One of key reasons of leaking of sewer pipes is the sewer corrosion. Sewer corrosion is a widespread and costly issue for water utilities. Knowing the corrosion status of a sewer network could help the water utility to improve efficiency and save costs in sewer pipe maintenance and rehabilitation. However, inspecting the corrosion status of all sewer pipes is impractical. To prioritize sewer pipes in terms of corrosion risk, the water utility requires a corrosion prediction model built on influential factors that cause sewer corrosion, such as hydrogen sulphide (H$_2$S). Based on the estimation of H$_2$S, chemicals can be put to corrosion locations to control H$_2$S to reduce the level of sewer corrosions. This chapter presents a predictive analytics toolkit, which is based on the emerging spatiotemporal data analysis techniques, for the estimation of hydrogen sulphide (H$_2$S) gas distribution, chemical dosing requirements and prediction of higher-risk areas for sewer concrete corrosion. The inputs to the toolkit are the sewer network geometry, monitored factors and hydraulic information; the outputs of the toolkit are spatiotemporal estimates of H$_2$S gas concentration and concrete corrosion levels on the entire sewer network with uncertainties of the predictions. The toolkit is also able to integrate expert domain knowledge or existing physical model results as prior knowledge into the analytics model. The final outcomes of the toolkit can be used to prioritize high-risk areas, recommend chemical dosing locations and suggest deployment of sensors. The chapter demonstrates that AI can help wastewater management systems to more efficiently monitor sewer corrosion and to more effectively optimize sewer water processing by suggesting reasonable chemical dosing at the right location to lessen environmental impacts. AI therefore greatly improves environmental sustainability.