In this case study learn how a global oil and gas company was able to detect and predict a variety of pending equipment failures. Download today to uncover how Aspen Mtell enabled the company to correctly identify all reported events – as well as unknown problems.

When developing its highly successful digital transformation program, a leading European energy company turned to Aspen Mtell® for predictive maintenance. After deploying Aspen Mtell on more than 50 large assets in a refinery, the company began testing the technology at a wind farm and had five early successes in predicting gear box failures.

Aspen HYSYS has been updated and improved with additional features over the last few years. From concurrent engineering to integrated gas plant modeling and refinery-wide modeling to streamlined process safety analysis, experience the latest innovations in chemical process simulation. This checklist shows the addition of features and incremental improvements by version.

Aspen Plus has been updated and improved with additional features over the last few years. From concurrent engineering to integrated solids modeling and batch modeling, experience the latest innovations in chemical process simulation. This checklist shows the addition of features and incremental improvements by version.

This article, featured in The Chemical Engineer, discusses why operators often struggle to know where to begin to digitally transform. AspenTech's Paige Morse outlines how many operations, particularly in specialties, have become increasingly complex as customers demand higher levels of performance and quality and a greater range of options from producers. Operators realize they need to diversify product lines to meet these increasingly sophisticated needs. Download this article to learn more.

Industrial organizations are sitting on a decades-old, massive double-edged sword: their data. Until now.

In the Pharma Times article “Working in Silos” AspenTech’s Justin Eames discusses the pitfalls of working in silos in the pharmaceutical industry and explains how AspenTech’s solutions can make an impact at speed using predictive analytics. Discover how to predict asset degradation and failure well in advance of an impending breakdown or disruption and gain the ability to make decisions that can not only minimize cost and disruption, but that can also protect public health by ensuring continuity and resulting quality of drug supply.

As all companies have been challenged by the current economic and health crisis, the successful recovery requires a new view toward future development and growth with metrics that consider broad and more long-term goals. Featured in Hydrocarbon Processing, AspenTech’s Paige Morse shares considerations for building future resiliency that center around digitalization.

Real-time condition-based monitoring of assets is an attractive solution for mining companies to minimize their unscheduled downtime and improve reliability of critical equipment. The success of an asset monitoring program depends on how well this data is analyzed, filtered and categorized to enable accurate predictions of impending failures. Focusing on the behavior of an asset only tells a small portion of the story. To fully predict failures, the trends of the entire process must be considered as a whole.

The era of the intelligent mine is here and the maintenance department of the future will use insight to improve its decision-making ability based on data.