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.
Mine Moves from Calendar-based to Prescriptive Maintenance with Aspen Mtell
Download this case study to learn how one of the world’s largest fully integrated zinc and lead smelting and refining complexes wanted to improve their metallurgical operations. Seeking to make more use of the data they had in hand as well as the utility of interfaces, the company conducted a pilot of Aspen Mtell to evaluate the effectiveness of prescriptive maintenance.
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.
Data-Driven Maintenance Planning Saves $1.8 Million USD Per Year in Shutdown Costs
For service providers, project execution is critical to customer confidence and overall success. Download this case study to learn how a global provider of knowledge-based maintenance, modifications and asset integrity services saved $1.8 million in shutdown costs using Aspen Mtell.
Global Energy Company Improves Safety and Asset Integrity with Machine Learning
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.
Refinery Gets Asset Failure Predictions with Nearly a Month of Lead Time
Because traditional diagnostic methods weren’t preventing equipment failures or identifying root causes of historic failures, a U.S. refinery turned to Aspen Mtell prescriptive maintenance to improve internal data science resources. Download this case study to learn how this refinery's pilot program with Aspen Mtell was able to predict failures with nearly one month of lead time, enabling planning for maintenance and rescheduling production.
Ahorre costos con mantenimiento predictivo durante la transformación digital
Lea cómo Aspen Mtell predijo una falla en el compresor con 35 días de anticipación, permitiendo que la empresa de energía evitara una parada de emergencia y cumpliera con las metas de producción. Minimizó las pérdidas de producción planificando la parada de la planta: ahorro potencial de 30 millones de dólares
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.
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.
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.