Transforming data into actionable knowledge for fault detection, diagnosis and prognosis in urban wastewater systems with AI techniques: A mini-review

Recent advances in artificial intelligence (AI) and data analytics (DA) could provide opportunities for the fault management and the decision-making of the urban wastewater treatment systems (UWS) operations. The UWS is typically a large system, including Sewer networks (SNs), Wastewater Treatment plants (WWTPs) and also considering the Receiving media (RM). However, applications of AI and DA in the UWS can be challenging due to the complexities and size of systems, the large variation in the level of UWS instrumentation, and the relatively poor data quality. This review goes beyond the state of the art by critically analyzing previous work on AI-based data-driven methodologies to system-wide fault detection, life cycle fault management and transformation of big and small data into analytics, particularly, considering two different points of view: process faults (such as bulking sludge, sewer corrosion & technology specifics) and instrumentation faults (such as sensors and actua-tors), thereby offering more opportunities to distinguish complex patterns and dynamics. Our analysis reveals the relative strengths and weaknesses of the different approaches to design fault diagnosis tools and to apply these in the UWS. Finally, the opportunities and challenges about the inter-play among UWS, data and AI are discussed.

Automated summary

Recent advances in AI and DA offer opportunities for fault management and decision-making in urban wastewater treatment systems (UWS). However, the complexity and size of UWS, variations in instrumentation, and poor data quality pose challenges for AI and DA applications. This review critically analyzes previous work on AI-based data-driven methodologies for system-wide fault detection and management, addressing process and instrumentation faults, and explores the interplay among UWS, data, and AI. It provides insights into the strengths and weaknesses of different approaches and discusses opportunities and challenges.