Tag: CAP

Powering the Future: Amazon and Google’s Investment in SMRs and How NuclearN is Driving AI Integration for a Carbon-Free Tomorrow

Posted on by sondra

NuclearN applauds the groundbreaking investments from Amazon and Google in Small Modular Reactors (SMRs), recognizing how these developments align with the future of clean, sustainable energy. Amazon’s $500 million partnership with Energy Northwest and Google’s collaboration with Kairos Power are significant milestones in the integration of nuclear energy with advanced technology. These partnerships emphasize that nuclear energy, combined with cutting-edge technologies like artificial intelligence (AI), is crucial to powering the infrastructure required for future technological advancements.

As Phil Zeringue, VP of Strategic Partnerships at NuclearN, points out, “These partnerships are critical to powering the future technologies we rely on, and the synergy between nuclear and AI is key to a sustainable, energy-secure future.” Zeringue’s perspective reflects the evolving role of nuclear energy as a foundational pillar for the world’s energy needs, especially as global tech giants like Amazon and Google take action to secure carbon-free power sources for their growing infrastructure.

NuclearN is well-positioned at the intersection of nuclear energy and technology. With over 48 nuclear reactors across North America and Europe currently utilizing our AI-driven tools to enhance their operations, we understand the immense potential that AI holds in transforming how nuclear energy is deployed. We also recognize that Amazon’s and Google’s investment in SMRs goes beyond simply meeting their energy needs; it represents a commitment to long-term sustainability and a clear acknowledgment of the need for innovation to address the global energy crisis.

The Intersection of AI and Nuclear Energy

Nuclear energy has long been regarded as a critical component of achieving a carbon-free future. However, the rise of SMRs provides a more flexible, scalable option for energy generation. Unlike traditional, larger nuclear reactors, SMRs can be deployed in a wider range of locations, require less upfront capital, and offer shorter construction timelines. Yet, despite these advantages, challenges such as human error, design changes, and logistical issues remain. This is where AI comes into play.

At NuclearN, we see AI as a critical enabler in the SMR deployment process. By integrating AI into SMR projects, we can significantly reduce human error, streamline design changes, and improve operational efficiency. Our AI-driven tools are already being used at nuclear sites to automate planning, streamline documentation processes, and enhance safety protocols. These tools allow engineers and operators to focus on building and maintaining SMRs efficiently while minimizing risks.

Phil Zeringue underscores the importance of these technologies, stating, “The integration of AI with SMRs is crucial for enhancing safety, reducing risks, and increasing the overall efficiency of these projects. These technologies not only align with our social impact goals but are also necessary to ensure the reliable, secure, and sustainable energy needed to power the future.”

By leveraging AI, we can address the two largest drivers of delays in SMR construction: human error and design changes. AI solutions can automate repetitive tasks, provide real-time insights for decision-making, and ensure that complex data is handled with precision. This not only improves the safety and efficiency of SMR projects but also reduces costs and keeps projects on schedule.

Amazon and Google: Leading the Charge in SMR Development

The recent announcements from Amazon and Google demonstrate that some of the world’s most innovative companies are betting on nuclear energy to meet their future energy needs. Amazon’s $500 million investment in partnership with Energy Northwest, along with Google’s collaboration with Kairos Power, showcases a growing recognition of SMRs’ potential to provide reliable, carbon-free energy. These companies are not just investing in their own infrastructure—they are signaling to the world that nuclear energy, paired with advanced technology, is a critical solution to the energy and environmental challenges we face.

Zeringue notes, “As more technology companies like Amazon and Google invest in nuclear technologies to power their infrastructure requirements, new nuclear power is needed more than ever. Which mean new workforce, new manufacturing and new tools to shrink the timelines to bring these crucial assets online”

These tech giants rely on vast amounts of energy to power their global operations, data centers, and the technologies that billions of people use every day. As they continue to expand their reach, the demand for secure and sustainable energy sources grows. Nuclear energy, particularly SMRs, offers a viable path forward. By investing in these solutions, Amazon and Google are not only securing their own energy futures but also helping to pave the way for the wider adoption of SMRs.

NuclearN’s Role in Supporting the Future of Energy

NuclearN’s mission is to drive forward the advancement of AI technologies alongside scalable energy solutions like SMRs. With our expertise in AI and nuclear energy, we are uniquely positioned to support the growth of SMRs and help meet the world’s increasing energy demands. Our work with 48 nuclear sites across North America and Europe has provided us with valuable insights into how AI can transform nuclear operations. By automating critical processes, improving safety, and reducing human error, we help nuclear facilities operate more efficiently and safely.

Our alignment with companies like Amazon and Google goes beyond shared goals of carbon neutrality. We recognize the importance of building partnerships that drive innovation and ensure a sustainable future. As SMRs become an increasingly important part of the energy landscape, the role of AI will only continue to grow. By integrating AI into SMR projects, we can accelerate the deployment of these reactors and ensure they operate safely and efficiently.

As Phil Zeringue emphasizes, “AI is a major force driving the need more carbon free energy, it is not lost on us that we are providing AI solution to make nuclear power the responsible, reliable and affordable choice which can power more AI which can further improve nuclear, it’s a virtuous cycle we are proud to be at the forefront of.”

Looking Ahead: A Carbon-Free, Energy-Secure Future

The investments from Amazon and Google mark an exciting chapter in the future of nuclear energy. Their leadership in this space highlights the importance of sustainable energy solutions to meet the demands of the modern world. NuclearN remains committed to advancing AI technologies that support the deployment and operation of SMRs, ensuring that the energy needs of the future are met with innovation, safety, and sustainability.

As the energy landscape evolves, it’s clear that partnerships between technology companies and nuclear energy providers are key to driving the development of scalable, carbon-free energy. NuclearN is proud to be part of this movement and looks forward to collaborating with industry leaders to create a cleaner, safer, and more secure energy future for all.



Nuclearn v1.8 – Neural Search and Easier Automation

Posted on by Courteney_Fox

Nuclearn recently released version 1.8 of its analytics and automation platform, bringing major upgrades like neural search for intuitive queries, configurable automation routines, expanded analytics outputs, and enhanced ETL data integration. Together these features, some of them AI-driven, aim to optimize workflows and performance.

Neural Search

The neural search upgrade allows searching based on intent rather than keywords, even with ambiguous queries. Neural algorithms understand semantics, context, synonyms, and data formats. This saves time compared to traditional keyword searches, and provides significant advantages when context-sensitive information retrieval is crucial.

Some of the benefits of neural search include:
Precision of Search Results: Traditional keyword-based searches often yield an overwhelming number of irrelevant results, making it difficult for plant personnel to find the specific information they need. Neural search engines deliver results with ranked relevance. This means results are not just based on keyword match but on the basis of how closely the content of the document matches the intent of the search query.  

Contextual Understanding: Boolean queries, which are typically used in traditional search engines, lack the ability to understand the contextual nuances of complex technical language often found in engineering and compliance documentation. Neural search algorithms have a kind of “semantic understanding” that can understand the context behind a query, providing more relevant results. In addition, Neural search understands synonyms and related terms, crucial when dealing with the specialized lexicon in nuclear, thus making searches more robust.

Multiple Data Formats: Nuclear plants often store data in different formats, such as PDFs, Word documents, sensor logs, and older, legacy systems. A neural search engine can be trained to understand and index different types of data, providing a unified search experience across multiple data formats. 

Selective Classification for Unmatched Automation Accuracy

AutoCAP Screener also saw major improvements in v1.8. You can now set desired overall accuracy levels for automation templates. The Nuclearn platform then controls the confidence thresholds using a statistical technique called “selective classification” that enables theoretically guaranteed risk controls. This enables the system to ensure it operates above a user-defined automation accuracy level.

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With selective classification, plants can improve automation rates and efficiency without compromising the quality of critical decisions. Risk is minimized by abstaining from acting in uncertain cases. The outcome is automation that consistently aligns with nuclear-grade precision and trustworthiness. By giving you accuracy configuration control, we ensure our AI technology conforms to your reliability needs. 

Additionally, multiple quality of life enhancements were added to the AutoCAP audit pages. Users can now sort the audit page results, add filters, integrate PowerBI dashboards with audit results, and even export the automation results to csv. These enhancements make it easier and more flexible for users to assess, evaluate, and monitor the automation system.

Analytics & Reporting Enhancements

On the analytics front, our customers wanted more customizations. v1.8 answers their request with the ability to upload their own custom report templates. In addition, customers can change date aggregations in reports to tailor the visualizations for specific audiences and uses. Enhanced dataset filtering and exporting also allows sending analyzed data to PowerBI or Excel for further manipulation or presentation.

Buckets

Editing analytics buckets is now more flexible too, with overwrite and save-as options. We added the ability to exclude and filter buckets from the visualization more easily and make changes to existing buckets, including their name.  

Data Integration

Behind the scenes, ETL workflows (meaning “extract, transform, load” data) were upgraded to more seamlessly ingest plant data into the Nuclearn platform. Users can now schedule recurring ETL jobs and share workflows between sites. With smooth data onboarding, you can focus your time on analytics and automation rather than manually uploading data. 

With advanced search, configurable automation, expanded analytics, and optimized data integration in v1.8, the Nuclearn Platform is better equipped to drive operational optimization using AI-powered technology. This release highlights Nuclearn’s commitment to meaningful innovation that solves real-world needs.

5 Reasons AI is the Promising Future of Nuclear CAP

Posted on by Courteney_Fox

In the near future, the Nuclear Corrective Action Program (CAP) will be sleek, streamlined, and highly efficient; where occasionally humans participants are required to review and deliberate over only the most complicated issues requiring their vast experience and wisdom. For everything else, a trained army of CAP AI agents invisibly process issues, review and alert on trends, assign corrective actions, and take feedback from human coaches via purpose-designed human/AI interfaces.

No longer will a team of humans be subject to hours upon days of analysis for trend detection, a Senior Reactor Operator forced to process another condition report about a cracked sidewalk, or an Engineer left waiting for a corrective action item to be issued to her inbox. These functions will have been largely automated with the focused application of AI-based technology. Here are the five reasons this future is highly probable, based on both the current state of the Nuclear Industry and leading-edge AI technology.

Cost Savings and Improved Quality

It comes as no surprise to anyone that has worked in the Nuclear Industry that running an effective CAP program is expensive. CAP demands a significant investment into human resources that have adequate experience to effectively diagnose and resolve the problems experienced in an operating power plant. In practice, this requires either dedicated staffing or rotating employees out of primary roles to fulfill a CAP function.

By applying intelligent automation to the Screening, Work Generation, and Issue Trending processes, a resource reduction of approximately 45% is expected.

Beyond reducing the number of resources required, AI reduces the total amount of time required to execute portions of the CAP process. While a human screening team may only be able to process conditions on a daily basis, an AI system can review and screen conditions and issue work items immediately. More quickly getting workable tasks into the hands of employees saves money and improves CAP quality.

For those issues that may be too complex for AI to effectively handle, a human-in-the-loop system can be employed, where AI knows when it is unsure and can reach out for human assistance. By using human-in-the-loop the cost of the CAP program is reduced while keeping quality the same or better.

Additionally, AI can lower the threshold for issue documentation. Deployment of an information extraction AI lets employees more naturally capture issues using natural language, without filling out specialized forms. When issues become easier to document, they are documented more often, the overall information input into the CAP program increases, and the chance an issue is corrected becomes greater. AI that immediately evaluates the quality and completeness of the submitted report enables automated dialogue with the submitter. This can encourage behaviors such as adding information, clarify issues, correcting spelling, or otherwise encourage behaviors that promote report quality, increasing the effectiveness of the overall CAP program.

Scale

The most valuable problems to solve are frequently the largest. CAP and associated activities are one of the largest opportunities in Nuclear. CAP lies at the heart of the Nuclear Industry, and requires participation from almost every trade and profession at each site. The ubiquity of CAP combined with the savings potential provides an immense incentive for plant operators, industry vendors, and industry research groups to discover and implement ways to make these programs run more sustainably and efficiently. Specialized AI that can automate various tasks are at the top of mind of industry groups such as the Electric Power Research Institute, the Idaho National Laboratories, and various utility in-house teams.

A fortunate side effect of the CAP program is the production of large quantities of high-quality data – data ideal for training the AI systems that will be used to automate the same functions. Most of this data is captured in free-form text as natural language. Language with a specific Nuclear vocabulary and dialect, but natural language nonetheless. The scale of this data puts it on par with the datasets utilized by the large technology vendors and academic institutions to develop and train the most effective AI systems. Thanks to the scale of Nuclear CAP data, these large AI systems can be specialized to operate in the Nuclear domain – increasing performance and effectiveness for the tasks at hand.

Transportability

The most notable advancements in AI technology of the late 2010s were around the development of advanced natural language-based AI. This AI has the ability to understand human language more naturally and effectively than previously thought possible. Breakthroughs in this area are characterized by the ability of AI to transfer learning from one problem to another. An AI good at classifying condition report quality will be better at identifying equipment tags vs one specifically trained just to identify equipment tags.

The benefit for the nuclear industry is that an AI system trained at Plant X will be able to transfer its learning to Plant Y and be more performant than one trained at just Plant Y. This is similar to how a working professional at Diablo Canyon would more easily adapt and apply their knowledge when transferring to Turkey Point than someone not having worked in the nuclear industry at all. Similar to a human employee, an AI system will benefit from the variety of different knowledge obtained from general industry data. Learning specifics for any one plant will be faster, cheaper, and easier for any plant wishing to specialize the AI system for use in automation once trained on general industry data.

As a result, solutions developed at one site will be able to be shared. With commonly applicable training and similar problems, the industry can work to solve the big problems once with ‘large’ or ‘hard’ AI, and transport the solution from plant to plant for the benefit of the entire industry.

Automated Screening

One of the more specific solutions apparent when applying AI to the CAP process is the automation of the condition screening process. Condition screening is the process of reviewing a received report of a non-standard condition in or around the plant, then applying certain tags, codes, or classifications, assigning an owner, and generating the appropriate work items that address the condition. For some plants, this process involves dedicated groups of senior employees that work daily to manually perform this process. For others, this involves dispersed resources periodically gathering together to complete screening. In either case, the resources are usually senior-level and experienced, and thus expensive. The following estimation of resources spent by the industry for this process each year illustrates just how large an opportunity there is:

The screening process has certain properties: repeatability and complexity of task, quality of data, scale, cost, etc. that make it extremely promising to apply AI-powered automation — discussion worthy of a separate blog post…coming soon!

Automated Trending

Automated trending is the sequel to Automated Screening – it’s what comes after the conditions have been identified and actions issued. Normally done ‘cognitively’ or via brute force search of the condition data, trending is resource-intensive and largely manual. Read Nuclearn’s Nuclear CAP Coding AI – Better Performance at a Lower Cost to find out more about how AI can help automate and simplify the trending task.

Bonus: The Rapid Progress of AI Technology

The five points above are only achievable due to the explosion in the progress of various technologies that underpin how AI learns and behaves. The speed in recent years with which new AI tools achieve human-level performance on various vision and language tasks is unprecedented. As seen in the chart below, developing AI that can recognize simple numerical digits at human-level performance took over 10 years; to recognize cats, dogs, cars and other everyday objects in images took about 5 years. More recently, developing AI that can recognize and manipulate human language took only about 2 years.

The accelerating pace of AI advancements shows no sign of stopping anytime soon. This type of rapid advancement, combined with the scale, transportability, and savings of CAP, allows Nuclearn to confidently say AI is the future of Nuclear CAP.