When lightning strikes twice, reliability modeling to the rescue

In this edition of What Works, SABIC turns to data modeling to find out, “What are the odds?”

By Christine LaFave Grace, managing editor

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On paper, at least, it seemed like sufficient redundancy. Global chemicals company SABIC, headquartered in Riyadh, Saudi Arabia, has a Saudi Arabian affiliate that produces industrial gases for a large number of SABIC businesses and other companies in the region. To avoid the wide-ranging impacts that unplanned downtime would have on customers, the affiliate has a backup power feed and eight production units operating in parallel. 

“We have all kinds of redundancies; it looked very robust,” says John Bruijnooge, director of technical services at SABIC. The affiliate hadn’t suffered a major outage in its 30 years of production, and there was a “high level of confidence” in its ability to stay online, come what may, Bruijnooge says.

But then in May 2017, what came was a major electrical storm. Lightning struck the main power feed; the feed went out. Within a second, another strike hit the backup feed. “And then it became dark,” Bruijnooge says.

Gas production was stopped, and subsequently, many of the plant’s customers had to stop their operations. “We restored (operations) fortunately in a day-and-a-half,” says Bruijnooge, “but then the damage was done, of course.”

It was a highly unlikely occurrence, to be sure. But it happened. And in the wake of it, SABIC executives and reliability engineers alike wanted to know: What were the odds that it (or a similarly debilitating event) could happen again?

To answer the question, Bruijnooge and his team turned to reliability, availability, and maintainability (RAM) modeling using AspenTech’s Aspen Fidelis Reliability software. The software maintains a listing of all critical assets in the system and – crucially – their relationship to one another as it pertains to keeping operations up and running. Then, explains Bruijnooge, “You provide the actual failure rates, repair procedures, and all the work that needs to be done, and then you ask, ‘What is the probability that I will reach a situation where one or more of the units will go out?’ ”

It’s about more than looking for “bad actor” assets, Bruijnooge indicates; it’s about examining how the current health of your assets affects the likelihood that an event such as a bad storm will take production offline and putting actual probabilities behind such an event occurring. So after running the numbers, what a facility winds up with is a more-contextual view of its top 10 (or 20, or 30) contributors to unavailability.

“(You look at) which are the components that have shown in the past to have the most vulnerability?” Bruijnooge says. “And then it becomes a probability calculation that one or more of those events will occur at the same time. And then that leads to the answer to, ‘In the next 20 years or the next 10 years, how often can this happen to us again?’ ”

For the SABIC affiliate, after modeling was conducted in July, the probability of a recurrence “was not zero,” says Bruijnooge. To mitigate that risk, the facility made the big, costly decision to – for now – run with an extra unit operating at all times.

“In theory, that should not be necessary, but the calculations we did scared us so much,” Bruijnooge says, that the facility opted to shoulder the extra costs at least until several identified maintenance priorities can be addressed. “We are now executing and processing and trying to improve,” he says. “Somewhere in the next few months, I would like to redo the modeling and then feed into the data the improved assets and see where the probabilities of failure are at that moment and see if we reach the confidence level to start operating at a more-efficient mode again.”

Confidence in the face of uncertainties is a big part of what simulation modeling – a vital aspect of prescriptive maintenance (RxM) – aims to offer, says AspenTech’s Mike Strobel. “We give them that ability to quantify, what does the future look like if I make this decision rather than that decision?” says Strobel, an engineer by training and longtime reliability pro who helped develop the Fidelis software. (AspenTech acquired the Fidelis offering in 2016.) Equipment health is one part of a larger picture that also includes weather, personnel factors, and geopolitical issues when it comes to setting expectations for production, Strobel notes; the Aspen Fidelis Reliability tool lets SABIC run through different possible scenarios based on calculated probabilities rather than just “averaging away” variables.

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