Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence
Company Size
1,000+
Region
- Europe
Country
- Italy
Product
- Aspen Mtell
Tech Stack
- Machine Learning
- Predictive Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Oil & Gas
Applicable Functions
- Discrete Manufacturing
- Maintenance
Use Cases
- Machine Condition Monitoring
- Predictive Maintenance
Services
- Data Science Services
- System Integration
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
Quantitative Benefit
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