“Houston, we’ve had a problem here.”
When astronaut John Swigert sent this message from the Apollo 13 in 1970 during their attempt to land on the moon, NASA had to find a way for the three astronauts to fix the space vessel quickly before they ran out of oxygen. The team in Houston had to find a way to visualise the exact issue based on the description that the team in space relayed to them from the vessel, and then they had to find a way to help the team in space fix the problem so they could return to earth safely.
Crewmen aboard the USS Iwo Jima, prime recovery ship for the Apollo 13 mission, guide the Command Module atop a dolly onboard the ship. The Command Module, with the three crewmen aboard, splashed down at 12:07:44 p.m. (CST), April 17 1970, only about 4 miles from the recovery vessel in the South Pacific Ocean.
It is interesting to imagine how the scientists and engineers at NASA used a physical twin of the Apollo 13 module to conjure up the solution. Since that incident, NASA has invested more and more in technologies that can predict the risk of different failures and the potential resolutions for the same.
As an engineer, one of the subjects I studied in school was about finite element methods (FEMs) for design purposes. Working as an engineer and building process equipment for the oil and gas industry, it is now clear how equipment is supposed to behave in its operating environment and as part of its operating standards. However, the real-world situation for that well-designed pressure vessel or heat exchanger is very different. Not only does the equipment have differing operating environments, but also they must be able to interact with other equipment, which likely has been designed and engineered in a different way. Once a process plant is operational, the asset is introduced to a variety of risks and uncertainties that, again, were never part of the original design.
The asset has software available that can provide information about the changing parameters for the individual equipment. Engineers can use this data to modify work environments or consider maintenance for the various parts. In the past few decades, Industry 3.0, which is the revolution of automation and computer-operated systems, has given plant operators in the hazardous industries a good understanding of what needs to be done for the individual equipment’s safety and performance. However, experience has shown that most incidents do not occur because one particular piece of equipment failed or performed out of its operating window. Accidents and disasters happen when a series of infractions (big and small) come together lining up to make and create a situation where an accident is triggered.
Hence, it is important that the asset operators get an overall view of the entire asset and understand how the interaction of the various automated equipment, sensors, probes, etc., along with the human maintenance and operational tasks have a combined effect on the safe working of the asset. Industry 4.0 has emerged and is now at a stage to deliver the promise of the true deployment of the Industrial Internet of Things (IIoT). Using all the information available from the individual sensors and equipment, we can now draw a virtual picture of what the real-life plant status is. We can then overlay information on what human interactions need to happen in the plant to then start to get a better picture of what is the true operational reality of the plant. Once we start to get the picture and we plan our operations, first in the digital space and then in the physical asset, we can start to manage the real operational risk of the asset.
Today’s technology allows us to predict and plan for risk at the asset location based on the digital information acquired from it. We can now deliver a true operational risk management digital twin of the asset, and operators in the hazardous industries can start to predict what is going to happen in the asset, simulate the changes based on planned operations and then offer a prescriptive behaviour of what needs to be managed at the asset to ensure the planned operations are carried out in a safe manner and minimise the chances of a disaster occurring at the asset.
An ORM digital twin brings together human, system and sensor-derived inputs in a combined way and provides a continuously updated picture of operational risk on the specific asset. By connecting people and processes and closing the loop between operations; maintenance; engineering, environmental health and safety; and other functions, digital solutions can deliver meaningful, actionable insights with powerful visualisations of risk and activity. That holds true on Earth—and anywhere we explore throughout the solar system.
Author: Abhilash Menon, Sphera
Read the article online at: https://www.oilfieldtechnology.com/digital-oilfield/01112019/digital-twins-the-next-frontier-in-operational-risk-management/