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topicnews · October 4, 2024

How Can Artificial Intelligence Prevent Fire and Explosions in Refineries?

How Can Artificial Intelligence Prevent Fire and Explosions in Refineries?

Between 2014 and 2019, 470 workers perished in the oil and gas industry, and between 2018 and 2023, there were more than 1500 injuries recorded, with one Pennsylvania refinery logging 119 injuries. Explosions and fires are, thankfully, rare, but in 2005, an explosion at the Texas City refinery killed 15 workers and injured 180 more.

Oil refineries are extremely complex industrial plants that process crude oil into refined products like gasoline, diesel, and jet fuels. The distillation towers, catalytic crackers, and hydrocrackers operate under extremely high temperatures and pressures, making these facilities prone to fires and explosions that can have disastrous consequences.

These accidents can spread fire quickly across a refinery if they are not detected on time, as was the case in the Phillips 66 Wood River refinery explosion in 2006, where 11 workers died, and also the Sunoco Marcus Hook Refinery explosion, where 5 workers were killed, leading to a $100 million settlement for victims and their families, according to a plant explosion attorney.

Artificial intelligence presents promising solutions to help prevent these incidents, reducing the risk of fatalities and life-altering injuries for workers.

Predictive Maintenance Using AI

Most fires and explosions at refineries originate from equipment failures, such as leaks, corrosion, metal fatigue, etc. AI-driven predictive maintenance can closely monitor the health of critical equipment and detect any anomalies well before they escalate into catastrophic failures. Machine learning algorithms continuously analyze sensor data to identify deviations from normal operating conditions. Any decreased efficiency, increased vibrations, or temperature changes can trigger proactive maintenance. This allows issues to be fixed before they lead to failures.

AI Process Monitoring and Control

Operating refinery units outside safe parameters is a major cause of accidents. Artificial intelligence can be used to continuously monitor process variables like temperatures, pressures, flow rates, etc. in real time. Machine learning models can analyze sensor data to detect any parameter deviations and autonomously make control adjustments to maintain safe operations. Abnormal process conditions that may lead to runaway reactions or explosions can be immediately flagged, and operators are alerted to take corrective actions.

AI-Powered Leak Detection

Refineries consist of hundreds of miles of pipelines, pumps, and valves. Leakages from any of these components can quickly escalate into fires. AI-based acoustic sensors can be deployed across the plant to detect ultrasonic sounds of even minute leaks. The system is trained with leak sound profiles to identify anomalies. Pinpointing the leak location allows rapid leak isolation and repair. Gas detectors augmented with AI can also sniff out hydrocarbon vapor leaks before they create hazardous situations.

AI for Improved Fire Detection Accuracy

AI-driven computer vision algorithms can analyze video feeds from CCTV cameras spread across the facility to identify fires much faster and more accurately than human monitoring. Deep learning models can distinguish between real fires and false alarms caused by reflections or light and shadow changes, significantly improving fire detection reliability. Fire alerts are communicated immediately to emergency response teams and control room operators.

The combination of predictive maintenance, process monitoring, leak detection, and fire detection enabled by artificial intelligence gives refineries an effective shield against catastrophic accidents. AI does not get bored or fatigued like human operators monitoring screens. It provides persistent vigilance across millions of sensor data points to identify risks before they escalate.

Leveraging the power of AI is critical for refineries to prevent fires and explosions that can have disastrous human and environmental impacts. With advances in sensor technology and computing, AI-based systems are becoming more robust, scalable, and affordable – making AI integration a prudent investment for refinery safety and reliability.

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