Meta will finance Oklo's purchase of uranium for its reactors. It's a massive vote of confidence for both the startup and nuclear power, but challenges remain. There are two ways for tech companies to invest in nuclear power right now. One is to buy power from traditional reactors that are already built, either by purchasing electricity from the plants directly or financing the reconstruction of decommissioned units. The other is to invest in one of the dozens of reactor startups promising to commercialize designs and technologies never before used in the American market to generate electricity.

US support for nuclear energy is soaring. Meanwhile, coal plants are on their way out and electricity-sucking data centers are meeting huge pushback. Welcome to the next front in the energy battle. Take yourself back to 2017. Get Out and The Shape of Water were playing in theaters, Zohran Mamdani was still known as rapper Young Cardamom, and the Trump administration, freshly in power, was eager to prop up its favored energy sources. That year, the administration introduced a series of subsidies for struggling coal-fired power plants and nuclear power plants, which were facing increasing price pressures from gas and cheap renewables.

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional-integral-derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL's strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance -- an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with less control effort.

Australia's new chief scientist has said he is open to the prospect of nuclear power playing a role in the country's energy mix, but remained focused on forms of energy that were "available to help us right now". On his first day in the job, Prof Tony Haymet said new energy-intensive technologies like artificial intelligence could be powered by renewables, but that he thought serious discussions about nuclear in Australia were likely to be years away. "If you go back and look at Chernobyl and Three Mile Island and so on, there wasn't enough transparency and openness. I think the nuclear industry has accepted the fact that they have to rebuild their social licence to operate," Haymet told a press conference when asked about small modular reactors (SMRs). "You know, for the next chief scientist in 2030 or 2040, I think you can re-ask your question."

As the global demand for clean energy intensifies to achieve sustainability and net-zero carbon emission goals, nuclear energy stands out as a reliable solution. However, fully harnessing its potential requires overcoming key challenges, such as the high capital costs associated with nuclear power plants (NPPs). One promising strategy to mitigate these costs involves repurposing sites with existing infrastructure, including coal power plant (CPP) locations, which offer pre-built facilities and utilities. Additionally, brownfield sites - previously developed or underutilized lands often impacted by industrial activity - present another compelling alternative. These sites typically feature valuable infrastructure that can significantly reduce the costs of NPP development. This study introduces a novel multi-objective optimization methodology, leveraging combinatorial search to evaluate over 30,000 potential NPP sites in the United States. Our approach addresses gaps in the current practice of assigning pre-determined weights to each site attribute that could lead to bias in the ranking. Each site is assigned a performance-based score, derived from a detailed combinatorial analysis of its site attributes. The methodology generates a comprehensive database comprising site locations (inputs), attributes (outputs), site score (outputs), and the contribution of each attribute to the site score (outputs). We then use this database to train a machine learning neural network model, enabling rapid predictions of nuclear siting suitability across any location in the contiguous United States.

Global carbon emissions would be 6% lower than today if not for the "inaccurate narrative" against nuclear power since the Chornobyl disaster that has created "unfounded public concern", according to Tony Blair's thinktank. A report from the Tony Blair Institute (TBI) has found that if the nuclear power industry had continued to grow at the same pace as before the 1986 nuclear disaster, the carbon savings would be the equivalent of removing the emissions of Canada, South Korea, Australia and Mexico combined. The world's emissions are higher than they might have been because of a sharp slowdown in the number of nuclear reactors opened since the 1980s, said the report, released on Monday. It found that more than 400 reactors started up in the 30 years before the Chornobyl disaster, but fewer than 200 had been commissioned in the almost 30 years since. "The result is that nuclear energy has never become the ubiquitous power source many had projected, with countries instead turning towards alternatives such as coal and gas," the report said. The thinktank has predicted a "new nuclear age" in the years ahead, driven by a surge in demand for low-carbon electricity from the power-thirsty datacentres needed to power artificial intelligence.

Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan.

Early fault detection and timely maintenance scheduling can significantly mitigate operational risks in NPPs and enhance the reliability of operator decision-making. Therefore, it is necessary to develop an efficient Prognostics and Health Management (PHM) multi-step prediction model for predicting of system health status and prompt execution of maintenance operations. In this study, we propose a novel predictive model that integrates reinforcement learning with Long Short-Term Memory (LSTM) neural networks and the Expert Fuzzy Evaluation Method. The model is validated using parameter data for 20 different breach sizes in the Main Steam Line Break (MSLB) accident condition of the CPR1000 pressurized water reactor simulation model and it demonstrates a remarkable capability in accurately forecasting NPP parameter changes up to 128 steps ahead (with a time interval of 10 seconds per step, i.e., 1280 seconds), thereby satisfying the temporal advance requirement for fault prognostics in NPPs. Furthermore, this method provides an effective reference solution for PHM applications such as anomaly detection and remaining useful life prediction.

Google is officially putting its weight behind advanced "mini" nuclear reactors in an effort to produce new clean to meet growing AI energy demands. On Tuesday, the company announced an agreement with California-based small nuclear reactor (SMR) startup Kairos Power to commission the development of six or seven reactors that could add 500 megawatts of clean energy to the US electrical grid within the next decade. Google's buy-in represents the biggest investment for the experimental new reactor type from a tech company and could play a key in making so-called next-generation nuclear commercially viable. The deal is part of a broader embrace of nuclear power by tech giants who are frantically searching for ways to fuel their increasing energy consumption while attempting to stick to their climate goals. In a blog post, Google said it expects the first of Kairos reactors to come online as early as 2030, with the other five six operational by 2035.

When the Three Mile Island power plant in Pennsylvania was decommissioned in 2019, it heralded the symbolic end of America's nuclear industry. In 1979, the facility was the site of the worst nuclear disaster in the nation's history: a partial reactor meltdown that didn't release enough radiation to cause detectable harm to people nearby, but still turned Americans against nuclear power and prompted a host of regulations that functionally killed most nuclear build-out for decades. Many existing plants stayed online, but 40 years later, Three Mile Island joined a wave of facilities that shut down because of financial hurdles and competition from cheap natural gas, closures that cast doubt over the future of nuclear power in the United States. Now Three Mile Island is coming back, this time as part of efforts to meet the enormous electricity demands of generative AI. The plant's owner, Constellation Energy, announced yesterday that it is reopening the facility.