Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP

AI can help water treatment plant operators understand complex systems: The study demonstrates that machine learning and explainable AI can transform massive amounts of operational data from wastewater treatment plants into clear, understandable insights that plant managers can quickly grasp, rather than leaving them overwhelmed by raw data.

Explainability is essential for real-world infrastructure applications: The researchers specifically chose explainable AI (XAI) methods rather than "black box" approaches, recognizing that operators managing critical water treatment infrastructure need to understand why the AI is making recommendations, not just what it recommends.

AI supports sustainable water management goals: By helping wastewater treatment plants operate more efficiently, this technology contributes to two critical environmental challenges: enabling water reuse in water-scarce regions and ensuring clean water is safely returned to natural environments.

Historical operational data contains valuable hidden patterns: The study found that the large databases of monitoring data already collected by wastewater treatment plants contain significant embedded information about how the facility operates, which AI can extract and make actionable for plant managers.

Practical testing on real facilities validates responsible AI deployment: Rather than testing only in controlled settings, the researchers applied their XAI methodology to an actual wastewater treatment plant (AQUAVALL in Spain), demonstrating a commitment to ensuring the technology works reliably in real-world conditions before widespread adoption.