Predictive asset optimization: Improving asset performance through simulation

A successful maintenance strategy is all about timing. It’s a classic dilemma: perform maintenance too early, and the result is unnecessary downtime. Too late, and you risk failures and even greater setbacks to production. With predictive asset optimization, process engineers can empower operations decision-makers by taking the fundamental engineering knowledge from conceptual design and deploying it for real-time insight. Predictive asset optimization can remove guesswork and dramatically reduce operational risk.

By incorporating dynamic simulation tools with predictive analytics and advanced visualization, predictive asset optimization amounts to more than the sum of its parts: a hybrid digital twin. It provides users with a true 360-degree view of operational risks. The solution takes a full-circle approach to failure prevention by helping you identify problems earlier, determine their causes with greater accuracy, and forecast remaining useful asset life to determine the best strategy and timing to address the issue. The result is maximized uptime, availability, and, ultimately, profitability. But the usefulness of the solution doesn’t stop at operations; as you come to understand your assets with greater insight, you can incorporate these insights back into the design of future assets, kicking off a cycle of continuous improvement.

Predictive asset optimization empowers you with a much more holistic view into the health and performance of your assets so that you can identify deviations that would otherwise be very difficult to isolate. For example, take the optimization of the maintenance cycles of a gas turbine. What makes turbines especially tricky to maintain is their sensitivity to ambient conditions, like temperature and load point. Because these ambient conditions may vary greatly from one day to the next, it’s difficult to accurately measure the underlying degradation of the asset. That’s where predictive asset optimization makes all the difference.

Using first-principles simulation, predictive asset optimization allows us to calculate how the turbine should be performing so that we can compare that theoretical performance against the asset’s actual performance. This comparison allows us to isolate the underlying degradation of the turbine from the normal day-to-day and hour-to-hour deviations caused by ambient conditions. Once the user can measure that degradation, then they can employ the predictive analytics component of the solution to determine the cause of that degradation, the best way to address it, and how much that degradation is impacting fuel efficiency and costs over time.

The result serves as a valuable reminder: profitability and sustainability are not opposing forces. By maximizing asset efficiency, we use less fuel, and when we use less fuel, we spend less and emit less at the same time. In addition to performance degradation, predictive asset optimization can also accurately predict greenhouse gas emissions. Industry leaders have been predicting emissions now for some time using predictive analytics, but by incorporating a hybrid digital twin into those predictions, the results are far more accurate, and the analysis far more in-depth.

Predictive asset optimization is designed to optimize entire systems or subsystems, but more targeted, smaller-scale capabilities are on their way. In our next release, the solution will include an individual component-based calculation as an add-on to the asset framework of AVEVA™ PI System™. This addition will allow users to drill down even deeper into the health of an asset, component by component. New users will be able to start here, start small, and scale up on demand to give their process engineers the full power of AVEVA’s cloud-based simulation and predictive analytics tools.

Learn more about predictive asset optimization by checking out our free webinar. To see more ways process simulation enables innovation, read our white paper.

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