AI USE CASE
Process Safety Event Prediction
Predict process safety incidents before they occur using ML on sensor and operational data.
What it is
By applying machine learning to real-time sensor streams and operational parameters, this use case enables HSE teams to detect precursor patterns that precede process safety events—fires, releases, or equipment failures—typically 30–90 minutes before they escalate. Early warning reduces incident frequency by 20–40%, cuts unplanned downtime costs significantly, and supports regulatory compliance documentation. Organizations that have deployed similar systems report reductions in recordable safety events and multi-million euro avoided losses per year.
Data you need
Historical and real-time sensor time-series data from process equipment, along with labeled records of past safety incidents and near-misses.
Required systems
- erp
- data warehouse
Why it works
- Engage process engineers early to validate model outputs and ensure alerts map to actionable operating procedures.
- Establish a robust data pipeline from SCADA/DCS systems with data quality checks before model training begins.
- Implement a continuous retraining schedule and model monitoring to handle process drift.
- Start with a focused pilot on one high-risk process unit to demonstrate value before scaling plant-wide.
How this goes wrong
- Insufficient historical incident labels make it impossible to train a reliable predictive model.
- Sensor data quality is poor or inconsistently logged, causing high false-positive alert rates that operators begin to ignore.
- Model drift occurs as process conditions change seasonally or after equipment upgrades, degrading prediction accuracy over time.
- Lack of operational buy-in means alerts are not acted upon in time, negating the safety benefit.
When NOT to do this
Do not deploy this system in facilities where sensor infrastructure is sparse or poorly maintained—noisy, incomplete data will generate unreliable alerts and erode operator trust faster than any safety benefit can be realised.
Vendors to consider
Sources
This use case is part of a larger Data & AI catalog built from 50+ enterprise transformation programs. Take the free diagnostic to see how it ranks against your specific context.