The nuclear power industry must find innovative ways to become more cost-effective.
With AI and Machine Learning solutions, you can reduce costs by accurately predicting component failures and Eigenvalue projections for hot k-effective.
Blue Wave's solutions are more reactive than current methods and require less human interaction, which leads to improved efficiencies, a lower risk of incident or accidents, and mitigates potential risks to the environment.
Scaling up your AI competencies reduces costs by creating virtual sensors and calibrations, providing early warning of impending failures, and developing useful remaining life component models.
Our AI and Machine Learning models are able to extract all of the variables of data and understand the connections between the different information, thus learning how to do or assess a specific task. This makes it possible to more accurately:
- Predict when components will fail (RUL/EOL)
- Monitor and manage MCO
- Project Eigenvalue for hot k-effective
- Devise and examine alternate fuel loading plans
- Assess other critical systems
It is our goal to aid in re-establishing the U.S. as a leader in nuclear energy.
AI and Machine Learning methods are utilized to review past and real-time data to create fast-running models that will analyze data sets, predict and prevent catastrophic operational delays, and drive better protocols for regulation compliance, efficiency, and safety.
Select a topic to learn more about our capabilities.
Our team integrates decades of expertise to help you:
- Improve operational effectiveness with routine diagnostics and prognostics
- Implement model-based fault detection
- Review and monitor advanced control systems and performance such as MCO and neutronics as it is applied in all stages of nuclear fuel cycle.
To avoid this, we’ve built custom Machine Learning software that lets you discover more faithful degradation and performance indicators through clustering and classifications, develop surrograte/proxy models for components, reduce catering events, and predict component failures.
We utilize Neural Net architecture which is superior to polynomial fits at finding complex relationships between inputs to help you solve complex problems that increase efficiency, predictability, and overall performance.
You can use our predictive AI algorithms to set up these virtual sensors to monitor plant assets and operations.