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

作者：Liu, Yiqi^*; Ramin, Pedram; Flores-Alsina, Xavier; Gernaey, Krist., V

来源：PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2023, 172: 501-512.

DOI：10.1016/j.psep.2023.02.043

• Fault detection
• Fault diagnosis
• Fault prognosis
• Data analytics
• Artificial intelligence

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.