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Focus Energia e Oil&Gas Focus Energia e Oil&Gas INFORMAZIONE PUBBLICITARIA

AVEVA

AI-Driven Framework to drive

autonomous operations and achieve

operational excellence

The Energy Industry is operating in a challenging parameters which weakens fault prediction and
business environment with the need to balancie all struggle to perform “what-if” analyses. They are
the dimensions of the energy trilemma (maximize also often not integrated requiring still a lot of man-
uptime and efficiency, ensure energy security and ual and time-consuming inputs.
affordability, accelerate energy transition) while em- As a result, operators are left with incomplete in-
powering a changing workforce. sights and cannot assess fault impact and optimize
Digital technologies such as IT/OT data platform, operations under constraints. The final decision is
digital twin, AI/ML, cloud are seen as one of the typically left to the user who must interpret the out-
key enablers to respond to these challenges and puts without sufficient decision-support tools.
achieve operational excellence building an “auton- These limitations highlight the need for a more ad-
omous plant” that is connected, smart and green. vanced predictive maintenance solution – one that
An “autonomous plant” operates and maintains anticipates issues, provides prescriptive actions,
production with minimum direct human involve- and offers context, optimization, and decision sup-
ment improving performance, reliability, safety and port capabilities. This comprehensive approach is
sustainability while reducing personnel onsite and what AVEVA defines as proactive asset optimiza-
exposed to hazardous situations. tion.
Most of the energy companies have already started It is an integrated framework that addresses the
their journey towards autonomous operations tran- limitations of traditional approaches by incorporat-
sitioning from reactive, time-based maintenance to ing AI, predictive analytics, simulation, optimization,
predictive and proactive strategies and implement- and decision support tools providing the following
ing traditional predictive maintenance programs on components:
top of their data infrastructure layer. Data Acquisition, Historization and Contextu-
However, these solutions lack the ability to monitor alization
the asset’s physical behaviour leaving out critical This layer provides operational data management
Maximize uptime and efficiency capabilities, and it is the foundation for autono-
mous operations. With advanced functionalities
such as asset hierarchy, event management it cre-
ates a data infrastructure layer it collects, historizes
and contextualizes real-time data coming from the
Cost and DCS. It is used for target setting, monitoring asset
Profitability
health and condition-based monitoring.
Empower a Ensure energy
changing Efficiency Reliability security and Physics-based Simulation & Optimization
workforce Digital Twin affordability This layer provides physics-based models for com-
prehensive understanding of assets performance.
HSE It operates in three different modes:
• Reconciliation mode: run online simulation
online to reconcile system measurements, en-
suring mass and heat balance closure while
detecting faulty instruments.
• Simulation mode: run simulation scenarios
Accelerate energy transition
and sustainability that are used to train predictive models to en-

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