Case Study

Multivariate Statistical Analysis Finds the Bad Actors in Out-of-Spec Batches

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.

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.

Case Study

Multivariate Statistical Analysis Finds the Bad Actors in Light Component Losses

This petrochemical company launched an Aspen ProMV™ pilot project to investigate light component losses that go to the bottom of a fractionation column and pressurize the downstream column. Using Aspen ProMV for continuous processes, a model was developed, and bad actors that are highly correlated to the light product loss were identified. Aspen ProMV’s optimization tool was also utilized to provide better operating conditions to reduce losses.

Case Study

Multivariate Statistical Analysis Finds Cause of Quench Oil High-Viscosity Issue

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.

Case Study

Detección de fugas en el rehervidor con meses de anticipación

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.

Case Study

Dos fallas inminentes se detuvieron a las dos semanas del monitoreo

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.

Case Study

Análisis estadístico multivariable encuentra la causa de un problema de alta viscosidad en el aceite de enfriamiento

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.

Case Study

规范性维护软件帮助Saras 提升经营绩效并推动卓越运营

Saras拥有地中海最复杂的炼油厂,每天的炼油产能为30万桶。作为数字化项目的一部分,他们正在评估如何提高资本和资产密集型炼油厂运营的可靠性。他们选择了AspenMtell,基于一个竞争性试点项目选择过程,最初的重点集中在关键炼油设备上,比如大型压缩机和水泵。 Aspen Mtell通过挖掘历史和实时操作以及维护数据来发现资产性能下降和故障发生之前的精确特征,预测未来故障并制定详细的行动以缓解或解决问题。

Case Study

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.

Case Study

Data-Driven Maintenance Planning Saves $1.8 Million USD Per Year in Shutdown Costs

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.

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