Article
How Data Reveals the Culprits Impacting Food Production Quality
For food and beverage companies, quality mishaps during production can mean more than a literal poor taste in a customer's mouth. In addition to the potential impact on consumer safety, quality lapses can slow down production and result in money down the drain.
Article
New Paradigms of Sustainable Value in 2021
As energy companies shift to a new normal due to the pandemic, the energy transition continues to gain strong momentum. Industry leaders are focused on meeting aggressive sustainability targets and are making investments in digitalization to accelerate innovation.
Blog
The Five-Step AIoT Guide to OPTIMIZE 2021
Our AIoT team captured key highlights and takeaways from OPTIMIZE 2021 to help you navigate the recorded sessions to learn how to leverage Industrial AI in your organization.
Article
Reformer Model-Based Inferential Properties Embedded in APC
A major European refiner implemented Aspen Technology’s reformer inferred property package for control and optimization of their catalytic reformer in two of its refineries. Download this article to learn more about the company's methodology of execution and the benefits derived.
Article
Modelling for ULSD Optimisation
This article (published in PTQ Magazine) describes how the on-line coordination and optimisation of refinery process units led to a 10% increase in middle distillate production without investing in process units or equipment.
Article
Efficient Plant: Use Industry 4.0 To Elevate Sustainability
In this article, AspenTech's Paige Marie Morse delves into how digital-transformation technologies can help industrial organizations cut energy costs while reducing environmental impact. Download now to discover how AI and machine-learning technology combined can enable companies to predict asset malfunctions well in advance and have positive financial and sustainable results.
Article
Digitally Enabled Reliability: Beyond Predictive Maintenance in Mining
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.
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