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Beyond the Visual

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

Leveraging more than 35 years of experience implementing physics-based models for energy facilities, KONGSBERG delivers innovative Hybrid ML technology that combine data science and physics-based simulation. This enables expedited, accurate, and explainable predictions that drive production optimization, automation, and enhanced monitoring capabilities.

A portfolio of services to address operators' needs

Digital twin has become synonymous with 3D representation and structured datasets and documentation that represent various asset types. However, visualization and documentation represent only a limited part of a wider range of functionalities that are required to accurately describe the facilities and their behaviour in their respective operating environments.

Kognitwin® Energy, KONGSBERG’s dynamic digital twin solution, offers a complete portfolio of scalable services that can be adapted to customer needs, that through incorporation of simulation an analytics provide value significantly beyond the visualization and documentation access. It is a vehicle for enhanced collaboration, broad-reaching innovation underpinned by IoT, analytics and simulation. It drives improvements across the fundamental operating model across portfolios of assets, providing a single source for information in the industry.

FROM VISUALIZATION TO AUTONOMOUS OPERATIONS, KOGNITWIN ENERGY ENABLES operators TO embrace the value of digitaL.

  • Actual state

    • Asset data and transactional data: Offering a digital twin for that collects information, documentation.
    • Real time data: Enabling the measurement and observation of the current status of the asset.
  • Modelled state

    • Simulated data: Augments and enhances physical measurements with simulation, allowing for accurate hypothetical scenarios testing and delivers synthetic training data for machine learning models.
    • Physics-based and mathematical models: Enabling the digital twin to reflect planning, operation, and maintenance through the lens of models.
  • Predicted state

    • Data driven ML (Machine Learning) models: Adding real time optimization capabilities verified by physics-based models to enable users in accessing key asset performance data across the organization; thereby driving  efficiency across teams and business processes.
    • Automation: Enabling higher levels of autonomy and closing the loop.

The combination of these three dimensions is powerful and supports a range of use cases with high business value.

This is Kognitwin Energy

Beyond being a virtual replica of your industrial facility, Kognitwin Energy, our dynamic digital twin delivers a rich framework for advanced digitalization and analytics, including a range of solutions that can be customized to attend your needs.

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Data science and advanced knowledge about the physical world combined into Hybrid ML

Analytics and physic-based simulation are familiar tools in the Energy industry, known to provide accurate results. However, a downside in the way these technologies have been applied is their resource-intensive nature, leading to longer processing time, resulting in limited real-time use, especially when considering complex dynamic applications.

As a response, operators are now looking to data-driven approaches as a means to speed up processing time.

While the data-driven modeling approach presents clear advantages in processing time, it is also challenged with accuracy, its dependency on high data quality, and correct interpretation and training, where the combination of these have led to challenges in implementation for the heavy-asset industries.

Hybrid ML (Machine Learning) provides a solution for this challenge, as it combines the strengths of the physics-based modeling approach and data-driven approach into one solution.

As a category of machine learning, Hybrid ML is defined as an approach to train and improve the precision of data-driven through training it with synthetic data from physics-based simulation. Unlike the physical world, the synthetic simulation “world” provides unlimited sets of test data, however with high accuracy. The result is, when properly trained, increased speed and accuracy provided through quicker data-driven algorithms.

With Kognitwin you can configure, orchestrate and run Hybrid ML, and we use this to enable more complete real-time view of the facility performance, even in parts of the facility with less or lower grade instrumentation. As a result, operators get closer to the goal of achieving a suitable digital data foundation; not only for real-time insight, but also for future innovation.

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  • Artificial Intelligence

    Artificial Intelligence (AI) refers to computer systems that are designed to think and perform actions like humans. The application areas for AI is broad and can range from simple game bots, following some predefined rules (such as the ghosts in Pacman), to more advanced language translation models (such as google translate).

  • Machine Learning

    Machine learning (ML) is an application of AI and a category of algorithms that allow models to become more accurate in predicting outcomes without being explicitly programmed. By finding statistical relationships in the data, the models can find good predictors despite the programmer not knowing in advance what these will be.  

  • Deep Learning

    Deep Learning is a part of a broader family of machine learning methods. These are often used for modern image and text analysis as the relationships in the underlying data are extremely hard to define upfront. By using a vast amount of data and computing resources, these relationships can be learned.  

Combining the best of both worlds with Hybrid ML

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Test What-if scenarios and always be one step ahead

In industrial operations, decisions with various levels of complexity need to be taken every day.  Outcomes of these decisions can result in a negative impact for the business, that in many cases are known just after the action is executed. And that is too late.

The teams working on onshore and offshore assets might wonder:        

  • What if I change the parameters of this equipment?
  • What if I schedule this maintenance for next week instead of today?
  • What if I adjust the pressure?

What if you could test scenarios before performing?

The combination of simulators, physical, and data-driven models offers the opportunity to test different hypothetical scenarios, predict their impact, compare options, and make accurate decisions. It means improved performance and productivity, increased safety, and energy savings for energy operations.

  • USE CASE: HOW ENERGY COMPANIES CAN USE HYBRID ML TO OPTIMIZE ASSET PERFORMANCE  LEARN MORE

  • USE CASE: FIND OUT HOW KOGNITWIN ENERGY CAN HELP YOU TO PERFORM CONDITION MONITORING AND AVOID COSTLY SHUTDOWNS  LEARN MORE

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Why choose Kongsberg Digital

With 200 years of determination, KONGSBERG has a long and proud history. Through our foresight and ability to adapt, we have survived through changing times, always boosting innovation and pushing new technologies to better serve the market.

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