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.

In this article, Robert Golightly explains the limitations of traditional maintenance practices and how new advancements in predictive maintenance are pushing assets and equipment to the limits of performance. Learn how today's industry leaders are using low-touch machine learning to extract value from design and operations data to predict when and where failures will occur.

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

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.

In this article, Transform and Grow – Manufacturing Excellence in Specialty Chemicals, Aspen experts discuss what is required to drive transformation in specialty chemicals through technology-enabled best practices.

After an electrical storm stopped gas production at one of its plants, SABIC executives and reliability engineers turned to Aspen Fidelis Reliability to calculate the probability of it—or a similarly debilitating event—happening again. Read this article from Plant Services to learn more.

To be a leading 'player' today's specialty chemicals environment, overcoming these challenges is crucial to staying competitive and ensuring the entire supply chain is optimized.

Leaders often wonder, where do we apply digital technologies to the challenges in our business? How can we chart our success? This uncertainty is understandable considering the technology’s complexity and rapid evolution, but understanding and integrating artificial intelligence (AI) is increasingly critical to competitiveness in the chemical industry.

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.

For metals and mining, a prescriptive maintenance solution marries the inherent knowledge of operations with existing process data to automatically create an alert when a failure will occur - reducing the time spent searching for root causes.