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。
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.
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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