Case Study

Cómo Braskem Idesa incrementó su tiempo útil en más de 20% utilizando datos y recursos existentes

En este caso de estudio, conozca cómo Braskem Idesa utilizó Aspen ProMV ™ para identificar y corregir de manera proactiva y de acuerdo a las condiciones históricas que llevaron a una mayor velocidad de ensuciamiento.

Case Study

Como a Braskem Idesa Aumentou o Tempo Operativo do Reator em mais de 20% Usando Datos e Recursos Existentes

Neste estudo de caso, saiba como a Braskem Idesa usou o Aspen ProMV™ para identificar e corregir em forma proativa condições que históricamente levaram ao entumpimento dos reatores altos.

Case Study

How Braskem Idesa Increased Reactor Uptime by Over 20% Using Existing Data and Resources

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.

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

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

Energy Company Drives Innovation in Predictive Maintenance via Digital Transformation Program

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.

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.

Case Study

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.

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

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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