Leading Pulp and Paper Manufacturer Detects and Avoids Major Fire with Aspen Mtell
Aspen Mtell provided a nine-day advance warning of imminent kiln overheating, allowing the plant to change operating conditions and avoid an operational shutdown. Download this case study to learn more.
Optimizing Smelting and Refining Equipment Reliability with Prescriptive Analytics
In this case study, you’ll learn about how one of the world’s largest fully integrated zinc and lead smelting and refining complexes sought to improve their metallurgical operations. Download this case study today to learn how your organization can use Aspen Mtell® to benefit from cost avoidance, as well as improve safety and environmental performance.
Aspen Mtell® Machine Learning Finds Cause of Compressor Failures at LNG Facility
Read how this LNG facility used Aspen Mtell prescriptive maintenance to provide up to 61 days advance notice of catastrophic compressor failures, preventing an economic loss of more than $40M USD per occurrence. Quick to implement and readily scalable, the solution provided key insights into the root cause of the failures.
US Railway Saves Millions by Preventing Line of Road Failures
A major transportation company responsible for delivering customer goods on time, safely and reliably, was plagued by catastrophic failures of locomotives that had gone undetected by its current reliability techniques. Using Aspen Mtell to examine data from engine lube oil samples, a leading U.S. railway was able to save millions of dollars.
Leaks in Reboiler Detected With Months of Advance Notice
Learn how a manufacturer of engineering thermoplastics discovered the root cause of recurring failures in their shell-and-tube reboilers using Aspen Mtell prescriptive maintenance.
Case Study: Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence
As part of an effort to drive reliability in its refinery operations, Saras turned to Aspen Mtell prescriptive maintenance to improve equipment uptime and decrease maintenance costs. Learn how Saras executed a project within weeks that predicted equipment failures up to 45 days in advance using prescriptive analytics, and enabled the company to increase revenue and cut operating expenses.
From Reactive to Proactive: Machine Learning Drives Better Business Outcomes
In this case study, learn how Aspen Mtell's predictive maintenance technology was used to deliver 27 days of advance warning of a central valve failure at a specialty plastics plant.
Digital Transformation with Predictive Maintenance Drives Cost Savings
Read how this large energy company used Aspen Mtell to get notification of pending failures in a hydrogen compressor more then 35 days in advance—enabling as much as $30M USD in potential savings by planning the shutdown to minimize production losses.