实例探究 > Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence

Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence

公司规模

1,000+

地区

Europe

国家

Italy

产品

Aspen Mtell

技术栈

Machine Learning
Predictive Analytics

实施规模

Enterprise-wide Deployment

影响指标

Cost Savings
Productivity Improvements

技术

分析与建模 - 机器学习
分析与建模 - 预测分析

适用行业

石油和天然气

适用功能

离散制造
维护

用例

机器状态监测
预测性维护

服务

数据科学服务
系统集成

关于客户

Saras is the owner of the most complex refinery in the Mediterranean, with 300,000 barrels per day of refining capacity. As part of their digitization program, they were evaluating ways to drive greater reliability in their capital- and asset-intensive refinery operations. They selected Aspen Mtell based on a competitive pilot project selection process which initially focused on critical refinery equipment, such as large compressors and pumps. Saras plans to use its sister engineering company, industrial automation specialist Sartec, to roll out Aspen Mtell refinery-wide.

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挑战

Saras, the owner of the most complex refinery in the Mediterranean, was looking for ways to improve reliability in their capital- and asset-intensive refinery operations. They had a strategic objective to improve uptime and decrease maintenance costs. The challenge was to ensure reliable operation of a 300,000 BPD refinery and a 575-megawatt integrated gasification combined cycle (IGCC) power generation plant. The initial project focused on four pieces of equipment: a feed pump, a wash oil pump, a makeup H2 compressor, and a recycle compressor. The desired outcomes of the pilot project were an accurate solution that detects precise patterns of normal behavior, failures, and anomalies, a solution that indicates early warning, with significant lead time from point of detection to actual failure, and the ability to capture a failure signature and use it to detect failures in unseen data on the same assets and/or similar assets.

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解决方案

Saras selected Aspen Mtell for their digitization program. Aspen Mtell mines historical and real-time operational and maintenance data to discover the precise failure signatures that precede asset degradation and breakdowns, predict future failures and prescribe detailed actions to mitigate or solve problems. The initial project, conducted in just a couple of weeks, covered the work to build Aspen Mtell agents to identify the failures for a subset of equipment. The data for these agents included condition data and process data. The team reviewed 163 quality issues (such as bad values and missing values) and cross-referenced the work order history for the four assets, including 340 prior work orders. The maintenance history spanned 17 problem classification codes.

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运营影响

Aspen Mtell was able to execute this pilot project within weeks, impressing Saras with its speed of deployment, accurate early detection of asset failures, avoidance of false alarms and ability to scale the solution system-wide.
The project achieved all objectives, and the Aspen Mtell agents were able to predict failures with significant lead time.
The agents accurately identified the specific failure mode — and did so without false positives.

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数量效益