ASSET MANAGEMENT | DEALING WITH DATA
Data is key to an economic future
Should the nuclear industry invest in machine learning and artificial intelligence in order to better manage its asset base? Janet Wood argues the industry can start getting easy wins right now
IN THE PAST, THE NUCLEAR industry has been wary of digital solutions and artificial intelligence. The industry’s concerns have ranged from making the safety case, to the fast pace of digital evolution and the need to ensure that systems do not become obsolete over the long lifetime of nuclear units. But others argue that the industry should take the opposite viewpoint. Sam Stephens says a digital approach can help the industry cut its asset management costs now and over the lifetime of plant. Stephens should be enthusiastic: he leads on digital
Janet Wood
Expert author on energy issues
transformation for Atkins and its parent company SNC- Lavalin. But he says nuclear’s basis in innovation will enable it to make gains from machine learning and artificial intelligence (ML/AI) because: “The principal factor in the nuclear industry is that we have a commitment to use the best available technology.” He says gains have been made over the last 10 or 20 as digital solutions have matured and been widely adopted in other industries, so “if the nuclear industry doesn’t adopt them it won’t be able to leverage its technology.” Stephens argues that at the same time the industry
has to make sure it is not disadvantaged, technically or economically, as the power sector around it changes. “The energy system we are operating in is becoming increasingly dynamic and increasingly digitalised,” he says, and its nuclear units have to move with it. Meanwhile, the need for low-carbon power supplies makes it still more imperative that nuclear units operate at the optimum and with the minimum of plant downtime.
Improving the safety case Nuclear’s focus on safety means the benefits outweigh costs already in some activities, says Stephens: “By adopting a digital approach on sites we see opportunities to take people away from the hazard and reduce their exposure to it. [We can] also identify smarter approaches and better strategies to optimise the way we deliver work and what work we deliver.” What applications can already deliver benefits to the
nuclear sector? Stephens gives the example of machine vision: “We are capturing very large quantities of photos and videos from sites. With those datasets you can then use machine learning algorithms to identify any potential or common defects.” Walkdowns currently carried out by workers who have to
enter radiological areas could be entirely automated, using a robot or drone to image the route each day and a machine learning algorithm to bring any changes or anomalies to the attention of human specialists. Such applications can be implemented without
connecting with other plant systems, so the risks of implementation are lower and “that is what we see as being an ideal first use-case,” he says. For such uses, Stephens argues against excessive caution:
“the sooner you start to invest in these types of approaches, the more the value that you can get out of it, given that nuclear plants have a finite life,”he says, noting that the investment case is easier to make now than it will be in 10 years’ time, when plants’ remaining lifetimes are lower. Other similar ‘self-contained’ options include planning
optimisation and using artificial intelligence to optimise programmes and schedules both for outages and general delivery programmes. Of course, project management software has been
around for a long time, but he says, “when you consider project programmes and the high number of potential independent interdependencies and constraints, artificial intelligence opens up the opportunity for you to consider many more different scenarios than a human brain could. Then you can use it to help identify the optimum scenario that will deliver your programme ahead of schedule and ahead of budget.” What is more, it can bring together planning and project management software with other types of information like a 3-D understanding of the plant and the resources deployed. A third opportunity for easy wins is using existing
Above: Machine learning can help the nuclear industry cut its asset management costs now and over the lifetime of plant 30 | November 2022 |
www.neimagazine.com
data pools. Stephens explains, “a lot of plants have been collecting data through enterprise asset management systems. Where we see opportunities is around firstly how we can use AI/ML to spot trends”. That may mean changes
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