Start-Up and Ramp-up Optimization

Optimizing start-up and ramp-up operations of caloric plants plays an increasingly important role in utilities’ performance optimizations.

Read here how performance engineers used Visplore to reduce the start-up and ramp-up analysis effort by a factor of 10 and made turbine start-ups 30 minutes faster with 20% less fuel consumption.

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Watch this short demo and learn how simple it is to extract and compare hundreds of start-up and ramp-up operations within minutes

Optimize start-up & ramp-up operations
Anayze operations with no effort

Visplore features ready-to-use solutions for the intuitive analysis and optimization of start-up or ramp-up operations.

  • Import current data easily
  • Select and cleanse data intuitively
  • Compare plant operations in detail
  • Extract time periods with start-up operations efficiently through pattern search
  • Intuitively calculate and analyze suitable KPIs
  • Identify conditions for optimal operations
  • Save analysis as standard template
  • Expand understanding of prerequisites and time sequences

Identifying two normal and one anomalous start-up procedure.
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Long term overview of anomaly score in context of other parameters.
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If you want to learn more, or discuss an application for your individual data, feel free to book a live demo!

Optimizing start-up and ramp-up operations

Substantially increased volatility in the power grid requires utility companies to cycle their thermal power plants output more frequently and rapidly. Optimizing these start-up, ramp-up and down operations is crucial for performance optimization, helping save fuel while staying within the plant’s technological capabilities.

However, optimizing start-up procedures is inherently complex, traditionally relying on the experience and intuition of individual operators. The existing process for analyzing start-up operations was time-consuming and tedious, involving manual data extraction and comparison using tools like Excel. This task required in-depth knowledge of critical parameters, which meant it could not be automated or easily outsourced to data scientists.

Visplore empowers performance engineers to intuitively analyze start-up and ramp-up operations with minimal effort.

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Why our customers
prefer Visplore

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.

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Optimize start-up and ramp-up operations like never before.