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AI sensors keep fuel flowing at the Royal Dutch Shell refinery


AI sensors keep fuel flowing at the Royal Dutch Shell refinery

Advanced equipment can detect problems 75 days before they occur

The equipment and operating conditions at the vast Pernis refinery in Rotterdam, where Royal Dutch Shell processes 20m tonnes of crude oil a year, are monitored using 50,000 sensors that generate 100,000 measurements a minute, Financial Times reports.

Last year Shell started using machine learning to better analyse and process that data. The model was designed to predict failures in control valves, and it allowed workers to carry out maintenance or adjust operating conditions as needed.

The work at Pernis is an example of how oil and gas companies use artificial intelligence and machine learning to notice problems before they occur, sometimes months in advance.

In just the first two weeks after deploying this [model at Pernis] . . . we think we prevented two trips with benefits of about $2m. What we have seen in Pernis is notification periods [of potential failures] from four to 75 days. Sometimes, well in advance, you can see something happening that wouldn’t be possible to observe with the human eye or conventional computer systems.

Alexander Boekhorst, vice-president for digitalisation and computational science at Royal Dutch Shell

While machine learning and the concept of predictive maintenance have existed for decades, sharp falls in the cost of cloud computing have led to a change in how companies can create value from data, according to Ed Abbo, president and chief technology officer at c3.ai, an artificial intelligence software provider.

Sensors used in predictive maintenance, which have been around since the 1950s, have not only become more advanced but cheaper. Back in the 1950s you’d have wired sensors. Now we have better technologies to be able to communicate sensor data over wireless. The movement is now towards wireless because it brings advantages, such as being able to retrofit sensor systems to devices or machines. It brings the costs down considerably as well.

Julie McCann, professor of computer systems at Imperial College London

Researchers at Imperial have also developed CogniSense, a system that does away with sensors altogether.

“We have a box that sits in the corner of the room and monitors the machines through sending just the same radio [signal] that you would have for WiFi through the machine. We can detect if something is going wrong without even having to touch the machine,” Prof McCann says.

“If it starts to deviate then we can use machine learning to analyse if it is decaying or something has changed in the machine. We can derive things like the temperature change.”

While AI and machine learning offer great potential, deploying it at scale is “non-trivial” and projects need to be selected with care.

Ed Abbo, president and chief technology officer at c3.ai, an artificial intelligence software provider

Mr Boekhorst argues that companies have to start with the problem they want to tackle, then work back to the best technology to solve it, rather than looking at where they could apply AI. To reap the benefits, companies also have to change their way of working, Mr Boekhorst says. “AI is not a journey about technology only. In fact it’s much more about ways of working and getting data-centric,” Mr Boekhorst says.

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