AI USE CASE
ML-Based Emissions Monitoring and Prediction
Predict and control industrial emissions in real time to stay within regulatory limits.
What it is
Machine learning models trained on sensor, process, and weather data continuously forecast emissions levels before thresholds are breached, enabling proactive process adjustments. Over time, process optimisation informed by the model can also reduce energy and raw material waste.
Data you need
Continuous time-series data from emissions sensors and process control systems (DCS/SCADA), ideally at least 12–24 months of historical readings including any past exceedance events.
Required systems
- erp
- data warehouse
Why it works
- Establish a robust data pipeline with automated sensor validation and outlier flagging before model training.
- Involve HSE engineers in feature selection and model validation to build trust in predictions.
- Start with a single emission type (e.g. NOx or SO2) and one production unit before scaling.
- Define clear escalation workflows so operators know exactly what action to take when an alert fires.
How this goes wrong
- Sensor data quality is poor or inconsistently calibrated, making model predictions unreliable.
- Model trained on normal operating conditions fails to generalise during plant upsets or seasonal changes.
- HSE teams distrust the model outputs and revert to manual monitoring without acting on predictions.
- Integration with SCADA or DCS systems is blocked by legacy infrastructure or IT security policies.
When NOT to do this
Do not deploy this when the plant's sensor network is sparse, poorly maintained, or lacks real-time data transmission, the model will produce noisy forecasts that erode trust and may actually increase compliance risk.
Example vendors
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