What if you could understand the implications before executing?
The combination of physics-based models and data science approaches and cloud scalability lends operators to streamline and scale testing of hypothetical scenarios. This allows for improved prediction of impact, options comparison, and increased quality in decisions making. Leading to overall improved performance and productivity, whilst upholding safety levels, and lowering energy consumption in energy facility operations.
Leveraging more than 35 years of experience implementing physics-based models for energy facilities, KONGSBERG delivers innovative Hybrid ML technology that combines data science and physics-based simulation. This enables expedited, accurate, and explainable predictions that drive production optimization, automation, and enhanced monitoring capabilities.
The 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 the incorporation of simulation 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.
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.
Analytics and physics-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 the processing time.
While the data-driven modelling 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 has 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 modelling approach and data-driven approach into one solution
Machine Learning, Artificial Intelligence, Industry 4.0, Physics Models… How can the energy industry go beyond these buzzwords? And most important, how can you get real value out of it?
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 simulated world provides unlimited sets of synthetic test data, yet still with high accuracy.
When data-driven models are properly trained, outcomes include 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 a 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.
In industrial operations, decisions at various levels of complexity must be taken every day. Out-comes from such decisions can result in a negative impact on the business. In many cases, such outcomes become known only after actions are executed. This is often too late.
Facilities teams might wonder:
What if you could test the scenarios before taking action?
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.
With Hybrid Machine Learning we enhance & constrain data-driven models using knowledge about the physical world through high fidelity simulator. Join this Webinar and listen to Eivind Roson Eide and Shane McArdle from Kongsberg Digital talk about how the Kognitwin platform addresses the limiting factors for developing and deploying machine learning models to real industrial assets, by using our well-proven simulators and easy and secure access to contextualized data.
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 (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 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.