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

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

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

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.

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