Aspen Mtell Predictive Maintenance Solution
Improving efficiency, reliability, and productivity through data-driven maintenance.
Achieving operational and profitability goals in capital-intensive enterprises is largely influenced by the ability to avoid (or minimize) asset failures and the resulting unplanned downtime. Aspen Mtell is characterized by its comprehensive solution that combines rule-based and condition-based monitoring, first-principles modeling, AI machine learning, and custom models developed by data science teams to monitor asset health and performance. Aspen Mtell meticulously tracks various operational parameters and captures subtle changes in behavior to quickly identify signs of anomalies, effectively and rapidly assessing asset risks. This enhances asset reliability, reduces operational costs, and strengthens commitments to safety and sustainability.
basic information
**What can be done with Aspen Mtell** - Achieve maximization of lead time and improved accuracy of failure prediction through AI/ML algorithms. - Combine various monitoring technologies to achieve strategic asset coverage and reduce the time required for value realization. - Accelerate the creation, deployment, and scaling of agents by incorporating KPIs and industry-specific expertise. - Simplify the creation of agents and data preparation through automatic identification of critical sensors and optimal input parameters. - Share specialized skills between data science teams and maintenance/process engineers to enhance collaboration. - Enable efficient and effective actions by production and operations departments by classifying and prioritizing all active alerts by severity and importance. - Centralize and visualize key performance indicators for plants and businesses in real-time to expedite decision-making, optimize resource allocation, and extend asset lifespan. - Streamline operations through easy integration with third-party systems and other AspenTech solutions. Improve overall business efficiency and performance by utilizing cloud-based solutions.
Price range
Delivery Time
Applications/Examples of results
For more details, please refer to the related links or feel free to contact us.
catalog（5）
Download All Catalogs
News about this product（7）
-
Case Study: Argentina's Largest Oil and Gas Company Improves Reliability and Efficiency with Aspen Mtell
YPF, Argentina's largest oil and gas company with a 100-year history, has begun its transformation efforts in collaboration with AspenTech. As a leader in the regional energy industry, the company has set ambitious goals, including venturing into crude oil and LNG exports and quadrupling its corporate value over the next four years by enhancing the production efficiency of its refineries. [Challenges] - To achieve these goals, it was necessary to significantly reduce operating costs while ensuring the reliability of equipment. [Results Achieved with Aspen Mtell] - Early detection of failures prevented significant damage to critical compressors. - Avoided an estimated 10 days of opportunity loss due to increased vibrations leading to failures, as well as preventing uncontrolled vents that could cause environmental pollution. - The implementation of the system led to an improvement in safety culture, streamlined operations, and promoted proactive problem-solving. For more details, please refer to the related catalog.
-
Case Study: New Listing - Sardeolica: Digitizing Wind Farm Maintenance to Achieve Cost Reduction, Improved Uptime, and Enhanced Safety Culture.
Sardeolica operates a 126 MW wind farm consisting of 57 wind turbines located in Urasai and Perdaxius, Italy, with an annual generation of 250 GWh. Since 2017, the company has embarked on a digitalization process aimed at optimizing plant control, planning maintenance during low wind periods, continuous improvement of productivity and machine uptime, and realizing a "digital" maintenance culture. 【Challenges】 • Extending the lifespan of the wind farm (a total of 57 turbines) and reducing maintenance costs • Optimizing scheduling through predictive maintenance requirements 【Achievements with Aspen Mtell】 • Proactively managing the wind farm to avoid catastrophic damage • Predicting potential issues up to six months in advance • Planning maintenance work during low-impact low wind periods • Improving plant uptime while reducing annual maintenance costs by up to 10% For more details, please refer to the related catalog.
-
Case Study: New Listing OCP Ecuador: Accurately Identifying Asset Failures Using Prescriptive Maintenance to Reduce Maintenance Costs
OCP Ecuador is a midstream oil and gas company that transports, stores, and ships crude oil. They showcase a case where they have implemented AspenTech's predictive maintenance solution, Aspen Mtell, to continuously monitor critical assets using both process data and mechanical data. 【Value Created by Adopting AspenTech Products】 • Accurately detected imminent asset failures, such as combustion issues and valve and injector calibration problems, 20 days before they occurred. • Rapidly applied the solution to 22 main pumps, 5 booster pumps, and 4 generators. • Generated three times the initial investment in less than five months. For more details, please refer to the related catalog.
-
[Webinar Information] 5/23 "Conservation Strategies and Process Improvement Solutions Based on Digital Platforms"
Focusing on digital transformation (DX) in the process industry, we will introduce maintenance strategies and process improvement solutions based on digital platforms. This webinar will provide valuable information for professionals and stakeholders involved in the process industry, as well as those interested in DX. Don't miss the opportunity to discover keys and insights for success on the journey of digital transformation! --------------------------------------- Event Name: "Maintenance Strategies and Process Improvement Solutions Based on Digital Platforms" Date: May 23, 2024 (Thursday) 1:30 p.m. - 4:00 p.m. Co-hosted by: Emerson Japan Co., Ltd. --------------------------------------- 【Agenda】 ● Opening ● Improving the performance of existing equipment through Asset Performance Management ● Innovations in OT data integration: New developments in DX brought by AspenTech Inmation ● Commitment to Boundless Automation
-
[New Listing] Aspen Mtell Enhances Efficiency, Reliability, and Productivity with Data-Driven Maintenance
Minimizing asset failures and unplanned operational shutdowns Achieving operational and profitability goals in the process industry is greatly influenced by the ability to avoid (or minimize) asset failures and the resulting unplanned operational shutdowns. However, there are limitations to implementing this with traditional maintenance programs, which typically result in reactive responses after failures occur. Other new maintenance methods have been tested, but Aspen Mtell stands out by combining "rule-based and condition-based monitoring," "first-principle modeling," "AI machine learning," and "custom models developed by data science teams" to create a comprehensive solution for monitoring asset health and performance. Aspen Mtell meticulously tracks various operational parameters and captures subtle changes in behavior to quickly identify signs of anomalies and effectively and rapidly assess asset risks. For more details, please refer to the related catalog.
