Tag: Process

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.



Enhance Your Outage Management with NuclearN

Posted on by sondra

Managing nuclear plant outages is a critical task that demands meticulous planning, precise execution, and rapid responses to unforeseen challenges. At NuclearN, our platform, designed by nuclear engineers for nuclear engineers, offers a comprehensive solution to transform your outage management process, ensuring safety, compliance, and cost-effectiveness.

Comprehensive Outage Schedule Support

NuclearN provides robust schedule support to help you manage and mitigate risks effectively:

  • Identify Risks: Quickly identify high-schedule risk activities by department to prioritize and mitigate potential delays.
  • Accurate Predictions: Obtain precise outage duration predictions to plan effectively.
  • Flexibility in Planning: Run “what-if” scenarios to understand the impact of scope changes and adjust plans proactively.

Financial Support for Optimal Budget Management

Efficient financial management is crucial during outages. NuclearN offers tools to optimize your budget and ensure financial accuracy:

  • Budget Optimization: Strategically reduce online and spring outage O&M charges to accommodate outage expenses.
  • Asset Validation: Ensure all planned outage work orders are correctly classified as fixed assets, preventing financial discrepancies.

Engineering Support for Efficient Operations

NuclearN enhances engineering support by speeding up processes and ensuring compliance:

  • Expedite Changes: Speed up the process for DCNs and temporary alterations to keep projects on track.
  • Compliance Checks: Efficient 50.59 screening for replacing obsolete parts, ensuring regulatory compliance.
  • Scope Review: Accelerate the engineering review of scope, ensuring all aspects are covered in time.

Outage Readiness: Preparing for Success

With NuclearN, your facility is well-prepared before the outage begins, setting the stage for success with fewer resources:

  • Daily Monitoring: See changes to schedule risks daily as tasks are complete, allowing quick adjustments.
  • Impact Analysis: Understand each activity’s percentage impact on the critical path to prioritize effectively.
  • Scenario Planning: Continuously run “what-if” scenarios to adapt to new challenges and changes.
  • Risk Identification: View high-schedule risk activities by group to allocate resources where needed most.

Observation Program for Continuous Improvement

NuclearN supports an efficient observation program to drive continuous improvement:

  • Efficient Observations: Supervisors can conduct faster, lower-friction observations.
  • Real-Time Analysis: Benefit from real-time trending and analysis to detect and address issues promptly.
  • Enhanced Quality: Ensure higher quality and more meaningful observations.

Issue Resolution and Enhanced Operations

NuclearN enables your team to react swiftly and efficiently during outages:

  • Expedite Changes: Speed up DCNs and temporary alterations.
  • Compliance Checks: Efficient 50.59 screening for obsolete parts.
  • Scope Review: Accelerate the engineering review of scope.

Optimize Financial and Engineering Efficiency

NuclearN helps you optimize budgets and ensure accurate financial management:

  • Expense Management: Reduce unnecessary O&M charges.
  • Asset Classification: Ensure accurate classification of planned outage work orders.

Stay ahead with NuclearN and transform your outage management strategy today!

DARSA: The Guide to Full Process Automation Using AI

Posted on by Courteney_Fox

You don’t automate right away…

Process automation using Artificial Intelligence is a complex endeavor. To successfully automate a process, automation systems and their implementers need to effectively incorporate complex technologies, a deep understanding of the business processes, risk-based decision making, and organizational change management all at once! This challenge can feel insurmountable to many organizations looking to start adopting AI-driven process automation. And unfortunately for some, it has proven to be so.

Luckily, there are battle-tested methods for bringing an automation system to life and avoiding the potential pitfalls. Here at Nuclearn, we have developed a project implementation process we call DARSA that helps guide us through automation projects. DARSA helps us deliver maximum value with minimal risk by leveraging an iterative, agile approach to AI-driven automation.

So what is DARSA? DARSA is a five-step linear process that stands for Decisions-Data-Direction, Assess, Recommend, Semi-Automate, and Automate. Each step in DARSA is a distinct phase with distinct characteristics, and transitions between these phases are planned explicitly and usually require system changes. To learn more about DARSA, we must dive into the first phase: “Decisions, Data and Direction”.

1) Decisions – Data – Direction

Before starting AI-driven automation, it is important to specify several key factors that will guide the project. These items are Decisions, Data, and Direction.

Decisions are the first, and most critical item to define early in an AI-driven automation project. At the end of the day, if you are embarking on an AI-driven automation project, you are doing so because you need to automate challenging decisions that currently require a human. If there is no decision to be made, then AI will not help automation in any meaningful way. If the decisions are trivial or based on simple rules, there is no need for AI. These processes should be automated by traditional software. So the first, essential part of an AI-driven automation project is identifying and defining the decisions that are planned to be automated.

Data is the next item that must be specified. An AI-driven automation project needs to identify two key sets of data: the input data being used to influence decisions, and the data created as a result of those decisions. These are critical, as AI-driven automation relies heavily on machine learning. To learn how to make decisions automatically, machine learning requires historical data. For each decision, there should be a detailed log of all data used at decision time, and a log of all historical human decisions.

Direction. AI projects must begin with clear direction, but unlike large traditional software projects, AI-driven automation projects cannot begin with a detailed project plan laying out detailed requirements gathering and design. AI and machine learning are notoriously unpredictable – even the most experienced practitioners have challenges predicting how well models will perform on new datasets and new challenges. Automation systems often have to evolve around the unpredictable strengths and weaknesses of the AI. As a result, it is important to specify a clear direction for the project. All members of the project should be aligned with this direction, and use it to guide their iterations and decisions. For example, in an AI-driven automation system for helping generate Pre-Job Brief forms, the direction for the project might be “Reduce the amount of time required to assemble Pre-Job Briefs while maintaining or improving the quality of Pre-Job Briefs”. This simple statement of direction goes a long way towards bounding the project scope, ruling out system design decisions that are unacceptable, and fostering potential innovation.

2) Assess

Once the Decisions, Data and Direction are specified, the most important factors for success in an AI-Driven automation project are fast iterations and good feedback. That is why the second phase in DARSA is “Assess”. During this stage of the project, nothing is actually automated! The “automation” results are being shared with subject matter experts, so they can assess the results and provide feedback. Automation system designers and Data Scientists are already quite familiar with testing how well their system works via various traditional methods. While these can help with generating accuracy metrics (the model is 95% accurate!), these methods are quite poor at evaluating exactly where the automations will fail or be inaccurate, why they are that way, and what the impacts are. The Assess phase is often where important risks and caveats are identified, and where additional considerations for the project are discovered.

Let’s take for example my experience with a project attempting to automate the screening of Condition Reports (CR) at a Nuclear Power Plant. One of the key decisions in screening a CR is determining whether the documented issue has the potential to affect the safe operation of the plant, often referred to as a “Condition Adverse to Quality”. Before even showing our AI model to users, my team had produced some highly accurate models, north of 95% accurate! We knew at the time that the human benchmark for screening was 98%, and we figured we were very close to that number, surely close enough to have successful automation. It was only after going through the “Assess” phase that we learned from our subject matter experts that we had missed a key part of the automation.

We learned during the Assess phase that not all Condition Reports are the same. In fact, there was a drastically asynchronous cost associated with wrong predictions. Overall accuracy was important, but what would make or break the project was the percentage of “Conditions Adverse to Quality” that we incorrectly classified as Not Conditions Adverse to Quality. The reverse error (classifying Not Adverse to Quality as Adverse to Quality) had a cost associated with it – we might end up performing some unnecessary paperwork. But get a few high-profile errors the other way, and we would potentially miss safety-impacting conditions, undermining regulatory trust in the automation system and the CAP Program as a whole.

As a result, we made some fundamental changes to the AI models, as well as the automation system that would eventually be implemented. The AI models were trained to be more conscious of higher-profile errors, and the automation system would take into consideration “confidence” levels of predictions, with a more conservative bias. A thorough Assessment phase reduced the risk of adverse consequences and ensured any pitfalls were detected and mitigated prior to implementation.

3) Recommend

After the Assess phase, Recommendation begins. The Recommend phase typically involves providing the AI results to the manual task performers in real-time, but not automating any of their decisions. This stage is often very low risk – if the AI system is wrong or incorrect there is someone manually reviewing and correcting the errors, preventing any major inaccuracies. This is also the first stage that realizes delivered value of an AI-driven automation system.

Increased manual efficiency is often recognized as a benefit in the Recommend phase. In the majority of cases, it is physically faster for someone to perform a review of the AI’s output and make small corrections versus working the decision task from start to finish. Paired with a proper Human/AI interface, the cognitive load, manual data entry, and the number of keystrokes/mouse clicks are drastically reduced. This helps drive human efficiencies that translate to cost savings.

The Recommendation phase also permits the capture of metrics tracking how well the automation system is performing under real-world use. This is absolutely critical if partial or complete automation is desired. By running in a recommendation setup, exact data about performance can be gathered and analyzed to help improve system performance and gain a deeper objective understanding of your automation risk. This is important for deciding how to proceed with any partial automation while providing evidence to help convince those skeptical of automation.

This stage may last as short as a few weeks, or as long as several years. If the automation system needs additional training data and tweaking, the recommendation phase provides a long runway for doing so in a safe manner. Since the system is already delivering value, there is relieved pressure to reach additional levels of automation. On some projects, this phase may provide enough ROI on its own that stakeholders no longer feel the need to take on additional risk with partial or complete automation.

4) Semi-Automate

The next step in DARSA is “Semi-Automate”. This is the first stage to both fully realize the benefits and risks of automation. This phase is characterized by true automation of a task – but only for a subset of the total tasks performed.

The metrics gathered in the Recommend phase play a key role here, as they can inform which parts of the task are acceptable for automation. As the system encounters different inputs and situations, total system confidence will vary. Based on this confidence, among other metrics, automation can be implemented as a graded approach. Low-risk, high-confidence tasks are usually automated first, and after the system continues to learn, and stakeholder confidence is improved, higher-risk automations can be turned on.

For example, take a system intended to automate the planning and scheduling functions required for Nuclear Work Management. Such a system would begin to partially automate the scheduling of work activities that have low safety and operational impacts, and the planning of repetitive activities that have little historical deviation in the execution work steps. These activities are low-risk (if something goes wrong there are minimal consequences) and high-confidence (the AI has lots of previous examples with defined conditions).

During semi-automation, it is prudent to still have a manual review of a portion of automated tasks to monitor model performance, as well as provide additional training data. Without manual review, there is no longer a “ground truth” for items that have been automated. This makes it challenging to know whether the system is working well! Additionally, AI performance may begin to stagnate without the inclusion of new training examples, similar to how human performance may stagnate without new learnings and experiences.

5) Automate

The final phase that every automation system aims to achieve: complete automation. This phase is characterized by the complete automation of all tasks planned in the scope of the project. The system has been running for long enough and has gathered enough data to prove that there is no human involvement necessary. From this point forward, the only costs associated with the task are the costs associated with running and maintaining the system. Complete automation is more common with tasks that have a lower overall level of risk, yet require a lot of manual effort without automation. The most common example of this in the Nuclear Industry today is automated Corrective Action Program trend coding.

It is expected to jump back and forth between partial and complete automation at some frequency. A common case where this can occur is when the automated task or decision is changed, and the automation system hasn’t learned what those changes are. There will need to be some amount of manual intervention until the system learns the new changes, and full automation can be turned back on. An example of this would be in a “trend-coding” automation system when the “codes” or “tags” applied to data are altered.

Start Using DARSA

DARSA provides a proven roadmap to designing, building, implementing, and iterating an AI-driven automation system. Using this process, organizations embarking on the development of new automation systems can deliver maximum value with minimal risk, using a methodology appropriate for modern AI in practical automation applications.

Visit https://nuclearn.ai to learn more about how Nuclearn uses DARSA to help Nuclear Power Plants achieve AI-driven automation.