Case Studies Digital Transformation with Predictive Maintenance Drives Cost Savings

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	Digital Transformation with Predictive Maintenance Drives Cost Savings

Digital Transformation with Predictive Maintenance Drives Cost Savings

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics
Applicable Industries	Oil & Gas
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Services	Data Science Services
Challenge	The customer, a diversified energy company with operations in refining, marketing, midstream, chemicals and specialties, had experienced three previous failures of a hydrogen compressor resulting in millions in production losses and additional maintenance costs. The company had begun its own digital transformation initiative that uses big data, machine learning and artificial intelligence (AI) to drive cultural change in the organization. As part of the initiative, they were investigating predictive maintenance. The customer decided to organize a competitive bakeoff, trimming an initial list of ten predictive analytics vendors to a handful of finalists. Ultimately, AspenTech was chosen as the sole vendor to execute an online pilot project. Read More
About Customer	The customer is a diversified energy company with operations in refining, marketing, midstream, chemicals and specialties. They operate more than a dozen refineries in the U.S. and Europe with a total capacity of over 2 million barrels of crude oil per day. The company had begun its own digital transformation initiative that uses big data, machine learning and artificial intelligence (AI) to drive cultural change in the organization. As part of the initiative, they were investigating predictive maintenance. Read More
Solution	Aspen Mtell was implemented as the predictive maintenance solution. A hydrogen compressor in one of the refineries had multiple historical ring and piston failures costing over $250 million USD across just 3 events. Aspen Mtell was able to provide notification of pending failures over 35 days in advance. With that amount of warning, the plant could have scheduled the shutdown at a more opportune time within the 35-day window, reducing downtime by as much as 8 days. They would also have saved over 30 percent on the repair costs by planning work in advance. The combined production and maintenance savings from these three events alone would have been more than $75 million. Seven anomaly agents and four failure agents were created for the compressor. These agents watched for a range of failure types, including piston and piston ring failures (38 days lead time), valve failures (24 days lead time) and lubricator failures (32 days lead time). Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Applicable Industries

Oil & Gas

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Services

Data Science Services

Challenge

The customer, a diversified energy company with operations in refining, marketing, midstream, chemicals and specialties, had experienced three previous failures of a hydrogen compressor resulting in millions in production losses and additional maintenance costs. The company had begun its own digital transformation initiative that uses big data, machine learning and artificial intelligence (AI) to drive cultural change in the organization. As part of the initiative, they were investigating predictive maintenance. The customer decided to organize a competitive bakeoff, trimming an initial list of ten predictive analytics vendors to a handful of finalists. Ultimately, AspenTech was chosen as the sole vendor to execute an online pilot project.

About Customer

The customer is a diversified energy company with operations in refining, marketing, midstream, chemicals and specialties. They operate more than a dozen refineries in the U.S. and Europe with a total capacity of over 2 million barrels of crude oil per day. The company had begun its own digital transformation initiative that uses big data, machine learning and artificial intelligence (AI) to drive cultural change in the organization. As part of the initiative, they were investigating predictive maintenance.

Solution

Aspen Mtell was implemented as the predictive maintenance solution. A hydrogen compressor in one of the refineries had multiple historical ring and piston failures costing over $250 million USD across just 3 events. Aspen Mtell was able to provide notification of pending failures over 35 days in advance. With that amount of warning, the plant could have scheduled the shutdown at a more opportune time within the 35-day window, reducing downtime by as much as 8 days. They would also have saved over 30 percent on the repair costs by planning work in advance. The combined production and maintenance savings from these three events alone would have been more than $75 million. Seven anomaly agents and four failure agents were created for the compressor. These agents watched for a range of failure types, including piston and piston ring failures (38 days lead time), valve failures (24 days lead time) and lubricator failures (32 days lead time).

Impact #1	Aspen Mtell predicted a compressor failure 35 days in advance, allowing the company to avoid an emergency shutdown and meet production goals.
Impact #2	Having early failure predictions provided time to plan repairs and adjust scheduling and production.
Impact #3	The customer recognized the importance of Aspen Mtell’s ability to combine the mechanical view with the process view to find the earliest signs of failure.

Impact #1

Aspen Mtell predicted a compressor failure 35 days in advance, allowing the company to avoid an emergency shutdown and meet production goals.

Impact #2

Having early failure predictions provided time to plan repairs and adjust scheduling and production.

Impact #3

The customer recognized the importance of Aspen Mtell’s ability to combine the mechanical view with the process view to find the earliest signs of failure.

Benefit #1	Reduced maintenance costs: planned maintenance has a savings potential of 30 percent over emergency maintenance
Benefit #2	Minimized production losses by planning the shutdown: $30M USD potential savings

Benefit #1

Reduced maintenance costs: planned maintenance has a savings potential of 30 percent over emergency maintenance

Benefit #2

Minimized production losses by planning the shutdown: $30M USD potential savings

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Overview

Digital Transformation with Predictive Maintenance Drives Cost Savings

Operational Impact

Quantitative Benefit