Last week I was riding in a powerful jetboat, isolated in the stark, windswept, remote landscape of Hudson Bay, north of the 57th parallel. We pass a polar bear, on a nearby island, and see a seal in the water. If the boat breaks down here, in remote and frigid water, we are at the mercy of the environment, the sand bars... and the polar bears!
I'm sitting next to Clint Sawchuk, owner of the jetboat and of Nelson River Adventures. Hiring him was our only option for getting us and our canoes from this remote spot we had canoed to and back to the road and our transport southwards.
As I watched Clint pilot the boat expertly to avoid sand bars and rocks, I looked over the powerful, well-cared-for boat: a Bentz Boat, made in Idaho, USA, with the jets made by Hamilton Jet in New Zealand. Clint's base is Gillam, Manitoba, which is 14 hours’ drive north of Winnipeg. I asked him if he services the boat himself.
"I do everything myself," he told me confidently. "Taken these engines apart and back together eight times already." I watched him controlling the throttle with his fingers and thighs.
"I can feel how the engine's running with the touch of my finger," he said. "Based on the feel, I know if something's wrong and what's wrong."
With Clint's knowledge, capability and experience, he's got a complete handle of the reliability of his equipment, on which his business and the very lives of his clients depend.
But what happens when his business success scales to two or three jetboats and a stable of employees maintaining and driving his equipment?
This, on a small scale, is the challenge of a refinery or offshore oil platform. When the health and reliability of an asset run to the limit is everything, how do you make every worker who cycles through these jobs as expert as the operator with 20 years’ experience? You essentially need to embed expert knowledge in the asset, so you can maximize uptime while reducing your dependency on costly expertise.
Aspen Mtell®, the machine learning solution from AspenTech, does all this and more. Beyond providing the same level of insight into asset performance as that 20-year operator, Aspen Mtell continuously learns from masses of data and each event. This enables the software to detect future events and their underlying causes, weeks or even months in advance of what even the most experienced individual can accomplish.
Innovative solutions like Aspen Mtell and Aspen Fidelis™ have already proven their value in improving reliability for systems ranging from freight locomotives to the largest compressors, and from drill strings to process pumps.
Today, the leading global process manufacturers are already benefitting from Aspen Mtell machine learning to get concrete financial returns from increased uptime. These include organizations such as SARAS, Borealis, SABIC and DowDupont.
To learn more, see:
AspenTech white paper: Beyond Oil Digitalization
Video: Aspen Mtell
Case Study: Prescriptive Maintenance Software Helps SARAS Improve Business Performance and Drive Operational Excellence
For more information on visiting Hudson Bay and the Nelson River, visit Clint’s website.