Building Physically Plausible Digital Twins

Building physically plausible digital twins is the key to optimize power plants.

Read here how an expert consultancy used Visplore to reduce the time to validate digital twins by 20% – and saved around $10,000 of cost per project for data preparation and model adjustment.

Read User Success Story

Building physically plausible digital twins
Validate model data against physical conditions

Visplore features pre-configured cockpits for flexible self-service data exploration, data preparation and model validation.

  • Explore your sensor data interactively
  • Select and cleanse data intuitively
  • Detect data quality issues visually or automatically
  • Compare predictions to physical models and expert knowledge
  • Validate hundreds of models at a glance
  • Interactively zoom into problem cases
  • Visualize correlations for many operation parameters
  • Identify situations insufficiently represented in historical data

Quickly grasp the error distribution including trends and outliers of forecast models over time.
Click Image to enlarge

Violation of expected value from digital twin.
Click Image to enlarge

Book Live Demo

If you want to learn more, or discuss an application for your individual data, feel free to book a live demo!

Building physically plausible digital twins

An increasing number of power plants are operated with partial load, being tied to consumption or complementing renewables. However, since historical data is often not readily available for partial load, energy generation companies struggle to build reliable, realistic digital twins for optimizing their power plants. Challenges like missing representative data points, or data quality issues make purely data-driven machine learning approaches hard. Therefore, it is necessary that domain experts themselves are able to explore and validate a power plant’s technical data to find those time periods or situations where deviations of the models to the actual physical conditions are significant.

Visplore empowers domain experts to explore and clean hundreds of complex time series and accelerate the digital twins validation by 20%, on average.

Read User Success Story

Perfect for dirty sensor data. With Visplore, we give customers more insights for the same money.

When we need it fast, we use Visplore.

Visplore enabled us to gain significant new insights from massive data about the impact of production process parameters on product quality.

Thanks to its ease of use and high performance even with millions of measurements, Visplore has established itself as our standard tool for quality management in plants from China to South America.

Checking and cleaning data from energy systems used to be very time-consuming and kept us from our core tasks. With Visplore, data preparation has become much faster, and the quality of the resulting data has improved.

The good usability and the comprehensive visualizations enabled us to gain a deeper understanding of routinely collected data and their relationships. For example, this increased the quality of forecast models.

Visplore is recognised and in use as a central strategic software for data analytics throughout the company.

By analyzing process data from a large foundry with Visplore, complex dependencies were detected that we didn’t find with other software. As a result, our customer’s heat energy was reduced by around 5% and mold wear by around 4%.

For our thermodynamical simulations, Visplore helps us to identify suitable data, eliminate outliers, and analyze physical processes very effectively.

The visualization is fast and simple enough for live collaboration with the customers, on site. By this, Visplore supports a thorough understanding of correlations, outliers, and anomalies.

Visplore makes our analysis of sustainable energy systems much more efficient.
In addition to the performance for large data, its integration with scripting environments is a big plus.

Visplore allows for a much easier investigation of relevant patterns and structures. Our downstream analysis becomes more efficient and we gain more confidence in the results.

PreviousNext
 
illwerkevkw

Build physically plausible digital twins like never before.