Descargue este caso de estudio para conocer cómo uno de los complejos más grandes y completamente integrados de fundición y refinería de zinc y plomo deseaba mejorar sus operaciones metalúrgicas. Queriendo hacer un mejor uso de los datos que su sistema de gestión de mantenimiento registraba, la compañía realizó un piloto de Aspen Mtell para evaluar la efectividad del mantenimiento basado en monitoreo de condiciones de equipos.

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.

A global pulp and paper manufacturer set out to predict risk and improve capacity using the power of automation.

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.

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.

A specialty plastics plant needed a solution to reduce downtime on a problematic hypercompressor in their LDPE production process.

When one of the world’s largest fully integrated zinc and lead smelting and refining complexes wanted to improve their metallurgical operations, the company turned to Aspen Mtell® prescriptive maintenance.

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.

A global provider of knowledge-based maintenance, modifications and asset integrity services wanted to take a more data-driven approach to planned maintenance and reduce unplanned downtime to optimize lifecycle costs.

Conozca cómo se utilizó la tecnología de mantenimiento predictivo de Aspen Mtell para saber con 27 días de anticipación sobre fallas de la válvula central de una planta de plásticos especializada.