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Customer Profile: Borealis is a leading provider of innovative solutions in the fields of polyolefins, base chemicals, and fertilizers. With its head office in Vienna, Austria, the company currently has around 6,600 employees and operates in over 120 countries. Borealis generated €7.5 billion in sales revenue and a net profit of € 1,095 million in 2017. Mubadala, through its holding company, owns 64 percent of the company, with the remaining 36 percent belonging to Austria-based OMV, an integrated, international oil and gas company. Borealis provides services and products to customers around the world in collaboration with Borouge, a joint venture with the Abu Dhabi National Oil Company (ADNOC).
The Company’s Objective: Borealis has embarked on a digital journey and the ability to bring transparency to all their operating processes is a priority for them. Companies have gone as far as they can with traditional preventive maintenance techniques, and are implementing machine learning and prescriptive analytics as part of their digital transformation initiatives. To achieve this objective, Borealis was looking for easy to implement predictive and prescriptive maintenance software to develop data analytics, including pattern recognition and early anomaly detection, in all operating functions; leading to increased performance in safety, quality, reliability and overall improved performance in manufacturing. Borealis wanted to transform asset maintenance into optimum reliability, extending the life of assets and maximizing the return on capital employed; and this can be done only through asset performance management, enabled by a blend of historical and real-time process, asset and enterprise data.
After carefully considering all these factors, Borealis zeroed in on advanced AspenTech solutions to realize its overall digital strategy. These technologies intelligently extract knowledge and value from the multiple large data sources available to end users in order to drive asset optimization. The scope of the first rollout is their production site in Stenungsund, Sweden.
Solution Implementation: Borealis implemented Aspen Mtell, part of the aspenONE Asset Performance Management (APM) software suite combining Big Data, machine learning and process knowledge expertise to maximize performance across the design, operations and maintenance asset lifecycle. The company’s selection of Aspen Mtell was based on its successful performance in a competitive predictive maintenance “proof of concept” project for a business-critical compressor on site. Predictive maintenance is a key initiative for Borealis, and this initial project effectively demonstrated the tangible benefit of automating such data analysis and knowledge work. The decision to move forward with Aspen Mtell was further supported by the demonstrated rapid speed of deployment, accurate early detection of asset degradation, and ability to scale the solution system-wide.
What Aspen Mtell Does: Aspen Mtell enables manufacturers to predict asset health, optimize maintenance and plant operations, increase production availability, and enable a predictive maintenance and operations strategy. The software 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. Salient features of Aspen Mtell:
- Faster
- Accurate
- Advanced
- Scalable
- Prescriptive
Proven Benefits: Early adopters of Aspen Mtell span multiple industries: energy, chemical, pharmaceutical, transportation, metals & mining, pulp & paper, and utilities. The software typically analyzes three sets of data:
- Process
- Condition
- Maintenance
The typical pilot project analyzes about 22 million sensor values of 135 million process data points to detect degradation; and the results are known in two and a half weeks. The cost savings across industries ranges in millions. With significantly earlier warning of asset degradation/failure provided by Aspen Mtell software, the company will have the time to work collaboratively to mitigate the losses of unplanned downtime and to minimize disruptions to their customers.
Deploying Aspen Mtell will give Borealis predictive and prescriptive maintenance capabilities that will drive optimum reliability, extending the life of assets, and maximizing the return on capital.