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 a large producer of synthetic rubber used Aspen ProMV to identify the cause of ongoing quality issues with its batch products. Download the case study to read how Aspen ProMV uncovered the variables that correlated most with batch quality, resolving production problems faster to limit losses.

Lea cómo esta compañía minera utilizó Aspen Mtell para predecir dos posibles fallas cuando se implementó en 12 activos durante un breve piloto en línea. Permitiendo así, un tiempo de inactividad planificado de equipos críticos, ahorrando dinero por cortes inesperados.

Una de las empresas más grandes a nivel mundial para productos químicos, plásticos y de refinación utilizó Aspen ProMV para entender y resolver sus problemas de producción causado por un problema de alta viscosidad en el aceite de enfriamiento. En este caso de estudio conozca cómo Aspen ProMV permitió a la empresa a destacar las principales variables de proceso que están altamente correlacionadas con problemas de viscosidad y que guio rápidamente a los ingenieros de proceso al problema subyacente para limitar las pérdidas.

In this case study, learn how Braskem Idesa used Aspen ProMV™ to proactively identify and correct for conditions that historically led to high reactor fouling.

Descubra cómo un fabricante de termoplásticos de ingeniería descubrió la causa raíz de fallas recurrentes en sus calderas de tubos y calderas utilizando el mantenimiento prescriptivo de Aspen Mtell.

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.

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.

When developing its highly successful digital transformation program, a leading European energy company turned to Aspen Mtell® for predictive maintenance. After deploying Aspen Mtell on more than 50 large assets in a refinery, the company began testing the technology at a wind farm and had five early successes in predicting gear box failures.

One of the world's largest chemical, plastic and refining companies used Aspen ProMV to understand and resolve production problems caused by an ongoing quench oil high-viscosity issue. In this case study, learn how Aspen ProMV enabled the company to highlight the top process variables highly correlated with viscosity issues, and quickly guided process engineers to the underlying issue to limit losses.