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

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

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

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	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.
Impact #2	The project achieved all objectives, and the Aspen Mtell agents were able to predict failures with significant lead time.
Impact #3	The agents accurately identified the specific failure mode — and did so without false positives.

Impact #1

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.

Impact #2

The project achieved all objectives, and the Aspen Mtell agents were able to predict failures with significant lead time.

Impact #3

The agents accurately identified the specific failure mode — and did so without false positives.

Benefit #1	Detection accuracy of 91% with 30 days of lead time
Benefit #2	Valve high outlet temperature failure event, with a lead time of 39 days
Benefit #3	Valve replacement due to an instrument failure, with a lead time of 25 days

Benefit #1

Detection accuracy of 91% with 30 days of lead time

Benefit #2

Valve high outlet temperature failure event, with a lead time of 39 days

Benefit #3

Valve replacement due to an instrument failure, with a lead time of 25 days

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Overview

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

Operational Impact

Quantitative Benefit