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When lightning strikes twice, reliability modeling to the rescue

April 9, 2018
In this edition of What Works, SABIC turns to data modeling to find out, “What are the odds?”

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?

“You’re trying to produce more product; you’re trying to be more efficient with your production; you’re trying to spend less money on maintenance; you’re trying to react to changing markets or logistics,” Strobel says. “(Modeling) just allows you to squeeze the most money out of any facility ... it allows you to play the what-if scenarios of, ‘How can I improve that, that event that’s stealing money from me?’ If a piece of equipment fails too often, it’s robbing money from me. If a ship is showing up late, it’s robbing money from me. If a spare part is missing, it’s robbing money from me.”

Attaching dollars to different scenarios run through the modeling software has been enormously valuable in getting buy-in from leadership for taking specific action, Bruijnooge says. “Having the ability to quantify and to estimate risk and to estimate and quantify vulnerability ... here in Saudi Arabia, I think I’ve blown my colleagues out of the water, more or less,” he says.

“This is what I have learned,” Bruijnooge continues: “To address senior and executive leaders with such solid decision support information ... they were very much amazed that I was able to say, ‘This is the probability that we have.’ ”

In addition, simulation modeling offers Bruijnooge and his team the opportunity to present worst-case and best-case outcomes – again, with probabilities behind different scenarios, rather than hunches. “It’s very easy once you have the model to play with it,” he says. “You have fantastic output opportunities in the tool to show the confidence around the answer that you’re giving.”

Strobel, for his part, notes that it remains a hurdle in many organizations to get stakeholders on board with purely data-driven decision-making.

“These are big multinational multibillion-dollar companies that we deal with, and I was surprised that even today they make decisions based on emotion,” he says. “Even when data is available, nobody wants to do the dirty work of collecting that data, cleaning that data, and then finally putting that data into a smart tool that will do something with it.” When it comes to backing up your arguments for major equipment purchases or revisions to asset management processes, modeling “forces you to do your homework,” he says.

Bruijnooge is quick to point out that reliability modeling isn’t necessary for addressing each and every reliability issue or supporting every asset management decision in a plant. Decision-support technology such as reliability modeling software is “always in my backpack, as I would call it,” he says. “Depending on the problem statements that arrive on my path, I take a grab in my backpack and try to use the right tools.”

Modeling and the discussions it can lead to takes time and thus personnel resources, so it’s best applied when making major CapEx or OpEx decisions, Bruijnooge suggests. “It is important that use of this tool is applied when it provides the most value,” he says. “It’s like a carpenter has a hammer, but he also has other tools.”

When designing and opening a new facility, RAM modeling has the potential to save millions, Bruijnooge says. “I’m now pulling the tool again ... because we are building a new plant, which is designed on paper, and I said, ‘Look, before we throw the money over the fence and build it, I want to see, on paper and in the reliability model, how reliable is this plant really going to be?’ Even before we pour one kilogram of concrete or before we erect one meter of steel, I can already predict to you how this plant will perform in the next 25 years based on how it’s designed and what types of components are used. There are great opportunities.”

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