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The Challenge
At around 10 AM, an unexpected increase in flare flow was detected, lasting approximately 15 minutes. On its own, the event was not critical. However, leaving the root cause uninvestigated posed a larger risk. If similar spikes occurred more frequently, they could accumulate into significant production losses, increased emissions, and higher process safety exposure.
The immediate question was not only what happened, but whether it had happened before, how often it was occurring, and what operational behavior was triggering it.
- Unplanned flare flow spike with unknown root cause
- Potential recurring events increasing emissions over time
- Elevated process safety risk if left unresolved
- No structured way to detect and analyze similar historical events
The Approach
The team shifted from reactive event review to systematic pattern detection and correlation analysis.
- Pattern recognition: Similar flare spikes were identified across historical data using signal pattern search
- Incident reporting dataset: Previous occurrences were exported and structured by date and severity, measured by maximum flare flow
- High-throughput correlation analysis: Advanced analytics were used to correlate flare spikes with process variables and control actions
- Root cause identification: Analysis revealed fast valve-closing behavior as the primary trigger
- Control logic adjustment: Valve control strategy was modified to eliminate the triggering condition
Key Insight
The flare spike was not an isolated anomaly, it was a repeatable pattern driven by control behavior. Once identified, the issue could be permanently corrected rather than repeatedly managed.
Results
The Takeaway
By transforming a short, seemingly minor flare event into a data-driven investigation, the team eliminated a recurring source of flaring, reduced emissions, lowered process safety risk, and improved the expected reliability of critical valves, turning reactive reporting into proactive environmental and operational control.
The Challenge
At around 10 AM, an unexpected increase in flare flow was detected, lasting approximately 15 minutes. On its own, the event was not critical. However, leaving the root cause uninvestigated posed a larger risk. If similar spikes occurred more frequently, they could accumulate into significant production losses, increased emissions, and higher process safety exposure.
The immediate question was not only what happened, but whether it had happened before, how often it was occurring, and what operational behavior was triggering it.
- Unplanned flare flow spike with unknown root cause
- Potential recurring events increasing emissions over time
- Elevated process safety risk if left unresolved
- No structured way to detect and analyze similar historical events
The Approach
The team shifted from reactive event review to systematic pattern detection and correlation analysis.
- Pattern recognition: Similar flare spikes were identified across historical data using signal pattern search
- Incident reporting dataset: Previous occurrences were exported and structured by date and severity, measured by maximum flare flow
- High-throughput correlation analysis: Advanced analytics were used to correlate flare spikes with process variables and control actions
- Root cause identification: Analysis revealed fast valve-closing behavior as the primary trigger
- Control logic adjustment: Valve control strategy was modified to eliminate the triggering condition
Key Insight
The flare spike was not an isolated anomaly, it was a repeatable pattern driven by control behavior. Once identified, the issue could be permanently corrected rather than repeatedly managed.
Results
The Takeaway
By transforming a short, seemingly minor flare event into a data-driven investigation, the team eliminated a recurring source of flaring, reduced emissions, lowered process safety risk, and improved the expected reliability of critical valves, turning reactive reporting into proactive environmental and operational control.
Access now
Share with a co-worker
The Challenge
At around 10 AM, an unexpected increase in flare flow was detected, lasting approximately 15 minutes. On its own, the event was not critical. However, leaving the root cause uninvestigated posed a larger risk. If similar spikes occurred more frequently, they could accumulate into significant production losses, increased emissions, and higher process safety exposure.
The immediate question was not only what happened, but whether it had happened before, how often it was occurring, and what operational behavior was triggering it.
- Unplanned flare flow spike with unknown root cause
- Potential recurring events increasing emissions over time
- Elevated process safety risk if left unresolved
- No structured way to detect and analyze similar historical events
The Approach
The team shifted from reactive event review to systematic pattern detection and correlation analysis.
- Pattern recognition: Similar flare spikes were identified across historical data using signal pattern search
- Incident reporting dataset: Previous occurrences were exported and structured by date and severity, measured by maximum flare flow
- High-throughput correlation analysis: Advanced analytics were used to correlate flare spikes with process variables and control actions
- Root cause identification: Analysis revealed fast valve-closing behavior as the primary trigger
- Control logic adjustment: Valve control strategy was modified to eliminate the triggering condition
Key Insight
The flare spike was not an isolated anomaly, it was a repeatable pattern driven by control behavior. Once identified, the issue could be permanently corrected rather than repeatedly managed.
Results
The Takeaway
By transforming a short, seemingly minor flare event into a data-driven investigation, the team eliminated a recurring source of flaring, reduced emissions, lowered process safety risk, and improved the expected reliability of critical valves, turning reactive reporting into proactive environmental and operational control.
Access now
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