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A proactive approach to safety pressure relief valve fitness
Connecting in data from hundreds of IoT sensors to monitor low temperature events and pre-empt safety incidents.
An alarm was sounded within the Process Safety community: critical safety pressure relief valves (PSVs) were found to be stuck due to hydrates forming under low temperatures.
Operators went to action implementing low temperature events. But with hundreds of PSVs across each facility, how can a robust monitoring strategy be implemented so that no event goes un-noticed?
The existing Knowledge Graph digital twin of the facility provided an easy path to the solution.
Challenge: Our client had been alerted by an industry partner to a problem affecting safety pressure relief valves: gas hydrates were forming during low temperature events. Following investigation, IoT temperature sensors were installed on around a hundred safety critical elements across the offshore facility. These “heat tracing elements” triggered an alarm if the temperature fell below a defined limit. Time and date of alarms were recorded and collected manually each month in spreadsheets.
In a worst-case scenario, a valve may have been exposed to low temperatures and not been performant for several weeks outside of the data collection window – with the engineering team completely unaware.
The operator was understandably keen to enable more proactive monitoring and intervention to protect critical safety barriers. A programme was set up to automate more frequent collection of temperature data, making it more visible to engineering teams – as well as to detect early warning signals through improved trending over time.
Solution: Our client had already invested in building a knowledge graph digital twin to solve two prior safety challenges: automated blowdown verification and gas detection alarm validation. We were tasked with investigating if the heat trace data could be connected into the knowledge graph on a real-time basis and presented in a dashboard that would highlight low temperature events.
Heat trace sensor data was connected to the knowledge graph using a new ‘HeatTraceOverview’ abstract object. This connected to all the data required to report low temperature events, including the pressure relief valve affected and a new ‘HeatTrace’ object associated to a System and Area of the plant. Weather data, from a third-party data source was also collected, to investigate a hypothesis that prevailing wind speed and direction were significant factors in the observed temperatures.
Python scripts were developed by Eigen to continuously check whether the heat trace alarm had been activated by a drop in temperature below a defined limit or deactivated by a rise in temperature.
A Heat tracing Monitoring Dashboard was built presenting all valve performance data, along with temperature and weather, using intuitive charting and trending to analyse performance at the facility, System, Area and valve levels. A sunburst provided a quick snapshot overview to build confidence and prioritise intervention.
Impact: With the Heat tracing Monitoring Dashboard, not only is the operator now much more informed about the risk to barrier performance of its safety critical elements, it can also prioritise where to investigate.
The operator is also able to discern trends that can enable earlier, more proactive intervention if a temperature falls and threatens the performance of a safety critical element. By integrating weather data, the team can build and test hypotheses which further improve relief valve health across the asset.
By automating collection, integration, analysis and presentation of the data, the operator is able to save several days of engineering time, which it can divert to more valuable tasks.
The speed with which this project was delivered was due in large part to the earlier investment the operator had already made in building its knowledge graph digital twin. Connecting new source data was relatively simple, with no need to copy or migrate data – and no need to build a bespoke database that requires ongoing maintenance.
Building knowledge graphs use case by use case like this delivers huge value for relatively low incremental investment and accelerates time to value for operators.
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