WC142 MayJune 2025 - Magazine - Page 35
cally estimate nitrous oxide (N2O) and methane (CH4) emissions
based on operational conditions and influent characteristics. This
provides utilities with a more accurate and responsive alternative to
fixed emission factors, which often fail to capture real variability.
Finally, ML supports predictive maintenance by detecting
anomalies in equipment behavior, allowing operators to anticipate
failures in pumps, blowers, or valves before they happen. This enables condition-based maintenance, improves equipment reliability,
and reduces unplanned downtime.
Together, these applications illustrate how ML can transform
raw data into operational intelligence—enabling WWTPs to move
from reactive control to predictive, optimized, and sustainable
management.
Challenges and what to watch out for
Despite its growing potential, the adoption of AI and ML in
wastewater treatment comes with a unique set of challenges that
must be addressed for successful implementation:
DATA QUALITY: ML models are only as good as the data they learn
from, as model efficiency depends on the quality and availability of data. Issues such as missing values, sensor drift, incorrect
calibrations, or inconsistent sampling frequencies can introduce
significant noise into the models and lead to incorrect predictions.
Preprocessing, validation, and ongoing monitoring of data streams
are critical for building reliable tools.
INTERPRETABILITY: Many operators and regulators are understandably cautious about relying on “black-box” models. Without clear
explanations for why a model made a certain prediction or recommendation, trust and adoption can be limited. Techniques such
as explainable AI (e.g., SHAP values, decision trees) and hybrid
models that integrate domain knowledge can improve interpretability and transparency.
PERCEPTION OF FULL AUTOMATION: One persistent misconception is
that ML implies full automation or a loss of operator control. In
reality, many of the most effective ML applications are advisory
rather than autonomous. They provide predictions, forecasts,
or optimization recommendations that enhance the operator’s
decision-making process. When implemented this way, ML acts
as a co-pilot—not a replacement—helping staff respond more
effectively under variable and uncertain conditions.
Looking ahead: the future of AI in wastewater
As digital infrastructure improves and data becomes more accessible, the role of AI and ML in wastewater treatment will continue
to grow. The future lies in systems that are not only predictive but
also adaptive—learning from changing conditions and evolving
over time to support resilient, efficient, and sustainable operations.
One major development is the rise of digital twins—virtual
replicas of treatment systems that integrate real-time data, process
models, and ML components. These platforms can simulate various operational scenarios and help utilities test strategies before implementation, reducing risk and enhancing process understanding.
At the same time, it’s essential to remember that the simpler
solution is often the best solution. ML is not always necessary—
nor is it always the most effective option. In many cases, clear
operational rules, robust process monitoring, and well-designed
visualizations can achieve the desired outcome more reliably and
transparently. The key is selecting the right tool for the right
challenge.
Ultimately, the integration of AI will be most successful when it
strengthens—not supplants—the expertise of operators and engineers. By embedding ML into operational workflows and pairing
it with clear guidance, utilities can unlock the full potential of their
data while maintaining trust, reliability, and human oversight.
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