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Aspen Fidelis Reliability Software Quantifies Financial Benefits Across the Plant Asset Lifecycle
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Process Analytics
Applicable Industries
- Chemicals
- Oil & Gas
Applicable Functions
- Discrete Manufacturing
- Process Manufacturing
Use Cases
- Predictive Maintenance
- Process Control & Optimization
Services
- Data Science Services
- System Integration
The Challenge
In the chemical, oil, and gas and other process industries, EPC firms are tasked with designing and developing increasingly large and complex installations. EPCs face the challenges of highly competitive bid situations and then delivering those projects within tight budgets and timeframes. After start up, plant owner-operators face challenges related to ensuring efficient operations and maintaining or renewing assets over a period that can stretch into decades. In both cases, solutions such as Aspen Fidelis Reliability can provide significant value through discrete event simulation. The AspenTech solution constructs plant models and runs multiple scenarios to analyze the impact of variables such as equipment capacities, operating logic, and production schedules on system-wide performance over time.
About The Customer
AspenTech is a leading software supplier for optimizing asset performance. Their products thrive in complex, industrial environments where it is critical to optimize the asset design, operation and maintenance lifecycle. AspenTech uniquely combines decades of process modelling expertise with machine learning. Their purpose-built software platform automates knowledge work and builds sustainable competitive advantage by delivering high returns over the entire asset lifecycle. As a result, companies in capital-intensive industries can maximize uptime and push the limits of performance, running their assets safer, greener, longer and faster.
The Solution
Aspen Fidelis Reliability is a discrete event simulation tool that constructs plant models and runs multiple scenarios to analyze the impact of variables such as equipment capacities, operating logic, and production schedules on system-wide performance over time. This helps EPCs and owner-operators gain confidence in their designs and improvement efforts and realize significant financial benefits across all phases of the plant lifecycle. The software uses the Monte Carlo simulation technique, which is based on repeated random sampling to obtain numerical results. This assigns random values to model variables – for example, process downtimes and equipment repair times – based on probability distributions. According to the company, since this approach considers the uncertainty inherent in complex systems, it can provide better solutions than approaches based on deterministic analysis in which fixed values are used for model variables.
Operational Impact
Quantitative Benefit
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