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How energy companies can use Hybrid ML to optimize asset performance

The O&G industry aims to reduce its large consumption of energy and reduce carbon emissions. Combining simulators and Hybrid ML, Kognitwin Energy can help you to test configurations before executing actions, enabling proactive modelling that focus on outcomes such as lowered energy consumption, flare or vent avoidance and other environmentally sensitive actions.

Challenge 

The asset-heavy industry consumes a substantial amount of energy. This contributes to carbon emissions and reduces profit margins. The operating space is rarely explored as risk is larger than potential savings. 

Solution 

  • Hybrid ML is the answer to this challenge.
  • A verified machine learning model is trained to provide real-time data support and warn the user of potential savings.
  • It enables exploring untested configurations without risk to people, environment or the equipment. 
  • Different scenarios can be tested, and optimization proposals can be proved.
  • Users can have a full overview and take the most favorable action.
  • Operators easily get access to key insights and metrics they need to put the new configuration into practice.

Impact 

Even small improvements to the operating conditions scaled across an asset can provide substantial energy savings without affecting the production point.

With Kognitwin Energy, simulators and the Hybrid ML technology in the loop, operators easily get access to key insights and metrics they need to put the new configuration into practice.

Estimated results: 

  • $2M yearly saving on reduced energy consumption of the entire process 
  • Reduced 0,5% electricity consumption without affecting production 
  • Production optimization 
  • Integrity control 
  • Reduced carbon emission 

Beyond the visuals

Aside from providing virtual replicas of energy facilities, Kognitwin® Energy offers easy access to monitoring, dynamic simulation, and high-quality predictions in real time.

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