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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, thereby, avoiding risks and ensuring production optimization. 


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.  


Hybrid ML is the answer to this challenge. Based on physics constrained data model approachsimulator, Hybrid ML allows you to explore untested configurations without risk to people, environment or the equipment. 

A verifiedvalidated machine learning model is trained to provide real time data support and warn the user of potential savings. Through the modelsdifferent scenarios can be tested, and optimization proposals can be proved. All to ensure the users have a full overview and take the most favorable action.


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, operatoreasily 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 a virtual replica of energy facilities, Kognitwin Energy offers easy access to dynamic monitoring, simulation, and high-quality predictions in real time.

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