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Presentation on a conceptual study of combining a small modular nuclear reactor (SMR) with a solid oxide fuel cell, solid oxide electrolysis cell, and a gas turbine as an integrated energy system with high operational flexibility and rapid load following. Virtually presented at the MILLENNIUM CLEAN and SUSTAINABLE POWER workshop 2025, University of Genoa, Italy.
The present report is intended to provide process flow diagrams (PFDs) and energy and mass balance data sheets for a U.S. industrial sector subset with which nuclear heat and power could be integrated—a subset that includes the oil refining, methanol and pulp and paper industries. Coupling options for integrating nuclear energy into these industries are quantitatively outlined for reference systems, and future work will extend this analysis in greater detail. Opportunities for integrating small modular nuclear reactors (SMNRs) were investigated for each of the industrial process configurations. Aspen HYSYS and Cycle-Tempo models for a high-temperature gas-cooled reactor were developed to evaluate the proposed integration. This introductory evaluation provides a general description and assessment of the operating principles, reactor coolant core outlet temperature, and reactor size to be integrated with industry. The industrial processes of oil refining and the production of methanol, pulp and paper were simulated by using Aspen HYSYS, Aspen Plus, and the PRELIM (Petroleum Refinery Life Cycle Inventory Model) tool to develop process details. Cycle-Tempo models then extend the process modeling results to obtain net energy demands (e.g., heat, steam, and electricity) when accounting for process steam and waste heat recovery. This information is intended to foster the analysis of integrating an SMNR to decarbonize industrial facilities. The SMNR would provide reliable, competitive, and sustainable clean energy while reducing carbon emissions and other environmental impacts, such as water withdrawals, consumption, and contamination. The refining industry, exhibited in Figure ES1, is a leading consumer of fossil -fuel-based heat, power, and hydrogen in the U.S. industrial sector, generating over 164 million metric tons (MMT) of CO 2 emissions in 2023. The overall mass and energy pertaining to a generalized complex refinery in the United States is reflected in Figure ES1, along with energy metrics regarding integration with a nuclear power plant (NPP). Data sheets were developed to indicate the energy requirements for the overall refinery and each refinery process. The data sheet for the overall refinery is shown in Table ES2.
This presentation presents the oil refinery requirements and conditions needed for the steam, the integration of the oil refinery with a small modular reactor light water reactor (LWR) and small modular reactor high temperature gas reactor (HTGR), and the carbon avoided by replacing the currently utilized natural gas with nuclear energy.
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Large Load Integration Tasks: Task 1 – Workshops Support stakeholder engagement across industry to promote collaboration and identify solutions to challenges that will guide other work Task 2 – Ancillary Services Characterize different types of large loads to assess under what conditions they may be utilized to provide grid stability services Task 3 – Communications Explore the cybersecurity and communications infrastructure required to enable large loads to interface with grid operations to provide ancillary services Task 4 – Nuclear Integration Explore risks and methods for supporting large load energy needs with SMRs and incorporating them into the wider power system Task 5 – Decision Support and TA Provide support to stakeholders through the creation of planning tools and direct technical assistance.
The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.
The industries examined in this report primarily rely on moderate-temperature heat provided by gas- or coal-fired boilers and combined heat and power (CHP) plants, delivered through standard process steam systems. High-temperature energy demands are often industry-specific and typically exceed the capabilities of high-temperature gas-cooled reactors (HTGRs). While it is technically feasible to replace process steam from fossil-based heat sources with nuclear energy, certain industries, such as methanol production and pulp and paper, face technoeconomic challenges in integrating nuclear energy without major changes or a technological shift. This is mainly due to the limited external energy demand remaining after the use of internal byproducts, waste heat recovery, and simple efficiency improvements. Achieving full decarbonization of these processes with nuclear energy would require significant technological advancements, involving experimental technology and substantial investments, making widespread adoption in existing industrial plants unlikely in the near term. This study reviews TES options in the context of enabling a flexible CHP supply while maintaining a steady nuclear heat input. Heat storage systems that interface between the reactor primary fluid and the CHP system offer superior performance and flexibility. Specifically, steam extraction downstream of the reheater with a two-tank molten-salt TES appears as the best solution regarding thermodynamic system benefits and system drawbacks. Using selected system configurations, a conceptual design of an industrial energy park was developed for industries with varying energy demands, such as steel production plants utilizing electric arc furnaces (EAFs) and chemical plants, as well as for those with constant energy demands, like petroleum refineries. This design highlights the capabilities of TES and explores its potential business cases. The study also conceptually develops the potential for integrating additional energy sources with nuclear systems through the implementation of TES. The potential of the HTGR-TES-CHP system was also evaluated considering key uncertainties such as industrial demand profiles, external grid access availability, and eligible tax credit levels, using the Holistic Energy Resource Optimization Network. Sensitivity of net present value to these uncertainties was analyzed to determine the optimal number of nuclear reactors (and CHP systems) and the suitable TES capacity. The results were interpreted from a decision-maker’s perspective, focusing on three key areas: deployment strategy (oversized units vs. undersized units with TES support), industrial process characteristics (thermal-intensive single profiles vs. electricity-intensive combined profiles), and operational goals (maximizing profits vs. minimizing natural gas (NG) consumption or external grid dependence). The optimization results indicate that the HTGR-TES-CHP system significantly reduces reliance on NG boilers for individual industrial processes by 9-60% (in NG capacity factor), with an average reduction of 38%, compared to standalone NG boiler operation case (Business As Usual [BAU]). For combined industrial processes, the reduction ranges from 37-77%, with an average of 60%. Additionally, the system greatly reduces dependence on external grids. In meeting industrial electrical demands, a 33-100% self-sufficient internal electricity supply is achieved for single industrial process, with an average of 74%, compared to the BAU scenario, where 100% of electricity is imported. For combined processes, 35-100% of internal electricity demands are met by the reactor, with an average of 73%. At last, the relative NG price levels at which the proposed HTGR-TES-CHP system can cost-effectively enter the market currently dominated by existing NG boilers were estimated. For a moderate HTGR CAPEX level ($\$$2500/kWth, $\$$6329/kWe), the analysis suggests that NG prices must be 2.5 to 7 times higher than HTGR variable operating and maintenance costs for single industrial process, and 5.5 to 9.5 times higher for a combined process scenario. Tax credit modeling shows that the Investment Tax Credit significantly reduces the price threshold needed to break even, making the system competitive with NG boilers in certain cases.
The rapid digitalization of nuclear power plants (NPPs) and the deployment of advanced and small modular reactors (A/SMRs) have expanded the cybersecurity attack surface within the nuclear sector. This evolution introduces unique challenges beyond those faced in general information technology (IT), operational technology (OT) and industrial control system (ICS) security, due to nuclear power’s regulatory rigor, safety-critical nature, and operational needs. A pressing workforce gap persists; cybersecurity graduates typically lack nuclear-specific context and retraining them for industry readiness requires 12–18 months, creating a significant burden. This paper addresses this gap by defining the domains of knowledge that nuclear cybersecurity specialists must master, spanning cybersecurity, nuclear engineering, OT/ICS security, and regulatory governance. We propose a curricular framework integrating technical, regulatory, and applied learning components to accelerate workforce readiness. Our approach builds on existing findings that current curricula inadequately integrate nuclear engineering and cybersecurity, shifting the discourse from why specialization is needed to what knowledge must be taught. The recommendations have implications for workforce development and long-term resilience of the nuclear energy sector.
A nuclear assisted carbon negative hybrid energy process that enables production of synthetic bio-crude oil and biochar from Eastern Idaho waste biomass is proposed. The process integrates nuclear powered electricity with high temperature steam electrolysis and biomass hydropyrolysis. The bio-crude oil is of sufficient composition and blended with traditional crude oil at a refinery. Hydrogen from the electrolyzer is pressurized and inserted into the pyrolyzer. Non condensable gases generated in the hydropyrolysis process are burned with oxygen from the electrolyzer to produce heat for the electrolyzer, biomass dryer, and pyrolyzer. The biochar is returned to the soil via fertilizer application and remains there for thousands of years. Since the total process uses nuclear generated electricity, the carbon in the biochar is ultimately sequestered from the atmosphere, thus making the process carbon negative. Using Eastern Idaho wheat or barley straw, this hybrid energy process has the potential to provide an alternative petroleum source. Two options exist for the system design: 1) send electricity from the nuclear plant and straw to a chemical processing plant to produce the bio-crude and biochar, 2) construct the biomass processing facility near the nuclear plant to allow use of nuclear-generated process heat to drive the chemical. Process model description and results are discussed. The process is sized to produce gasoline and diesel at the rate that the INL uses every day for fleet usage.
Verification of containment integrity is required for spent nuclear fuel (SNF) managed by the commercial nuclear industry and U.S. Department of Energy (DOE), especially after extended storage. Certain SNF storage systems, such as the DOE road-ready dry storage system, hold several packaged containments within a welded over-canister. These packaged containments are called Department of Energy Standard Canisters (DOESCs). DOESC leakage identification is challenging because their containment boundary cannot be accessed for testing and their contents (i.e., SNF and fill gas) are often similar. There are concerns that this could result in costly characterization and repackaging operations of DOE road-ready dry storage systems if compromised DOESCs are suspected. Here, to address these concerns, this paper presents an approach for applying a gas tagging process using xenon to uniquely identify compromised inaccessible containments following extended storage. The containments considered for this application are seven DOESCs, each packaged within a single over-canister. Two different SNF loading configurations from the Advanced Test Reactor and Fort Saint Vrain nuclear power plant are considered. These configurations are used to represent research reactor aluminum-clad spent nuclear fuel (ASNF) and TRi-structural ISOtropic (TRISO) SNF types. Results for this application show that for ASNF and TRISO type fuels for which the selected fuels are representative, the volume of taggant required at loading is determined primarily by the lower detection limit and leak rate of taggant from a compromised DOESC, rather than the amount of fission-generated xenon in the loaded fuel. While the application presented is suited for larger leaks, smaller leaks could be detected by modifying certain design parameters. This gas tagging approach can also be applied to other DOE containments and advanced reactor SNF storage systems.
The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform
The rising interest in nuclear microreactors has highlighted the need for comprehensive technoeconomic assessments. However, the scarcity of publicly available designs and cost data has posed significant challenges. To address this issue, the Microreactor Optimization Using Simulation and Economics (MOUSE) tool is developed. MOUSE is a tool that integrates nuclear microreactor design with reactor economics. The design calculations encompass core simulations using the OpenMC Monte Carlo Particle Transport Code [romano2015], along with simplified balance of plant calculations. On the economic side, MOUSE provides detailed bottom-up cost estimates, calculating both the total capital cost and the levelized cost of energy for first-of-a-kind and nth-of-a-kind microreactors. The cost estimation correlations are developed using data from the MARVEL project and additional literature sources. MOUSE has released as an open-source tool on GitHub (MOUSE Tool). By combining design calculations with cost equations, MOUSE enables users to evaluate the impact of various technological consideration, advanced moderators, design changes, material/fuel changes, and geometry modifications—as well as economic parameters like interest rates and construction duration. This comprehensive framework can guide stakeholders towards technological solutions that enhance microreactor competitiveness. Additionally, powered by the WATTS toolkit [romano2022], MOUSE supports optimization studies, parametric analyses, and uncertainty calculations/propagation. Currently, preconceptual designs of three microreactor types are included in MOUSE: a liquid metal thermal microreactor (LTMR), gas cooled TRISO-fueled microreactor (GCMR) and heat-pipe TRISO fueled microreactor (HPMR). To showcase its ability, MOUSE was used to conduct detailed bottom-up cost estimates for the first of a kind (FOAK) and Nth of a kind (NOAK) of the following microreactors • A 20MWt LTMR that is built on the ongoing MARVEL demonstration at Idaho National Laboratory (INL) • A 15 MWt GCMR that was designed to be more representative of the typical commercial microreactor • A 7 MWt HPMR that was built on previous work (Choi 2024) The The reader should note that these three designs and corresponding cost estimates are examples to demonstrate the MOUSE capability. The designs are pre-conceptual, the reactor designs were not optimized, and the cost estimates were developed with incomplete information. Additionally, stakeholders might be interested in a variety of designs that may differ from the examples provided in this report. The MOUSE tool can also be used to study how design choices affect economics. To demonstrate its capability, MOUSE was used conduct parametric studies such as examining the economic impact of the reflector's material and thickness, the moderator's booster material and dimensions, fuel composition and enrichment, core size, and power level. Several insights were gained from these parametric studies.
The total charm-quark production cross section per unit of rapidity dσ($\mathbf {c\overline{c}}$)/dy, and the fragmentation fractions of charm quarks to different charm-hadron species f(c → h c ), are measured for the first time in p–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV at midrapidity (–0.96 < y < 0.04 in the centre-of-mass frame) using data collected by ALICE at the CERN LHC. The results are obtained based on all the available measurements of prompt production of ground-state charm-hadron species: D 0 , D + , D$^{+}_{s}$, and J/ψ mesons, and Λ$^{+}_{c}$ and $Ξ$$^{0}_{c}$ baryons. The resulting cross section is dσ($\mathbf {c\overline{c}}$)/dy = 219.6 ± 6.3 (stat.) $^{+10.5}_{–11.8}$ (syst.) $^{+8.3}_{–2.9}$ (extr.) ± 5.4 (BR) ± 4.6 (lumi.) ± 19.5 (rapidity shape) + 15.0 (Ω$^{0}_{c}$) mb, which is consistent with a binary scaling of pQCD calculations from pp collisions. The measured fragmentation fractions are compatible with those measured in pp collisions at $\sqrt{s_{NN}}$ = 5.02 and 13 TeV, showing an increase in the relative production rates of charm baryons with respect to charm mesons in pp and p–Pb collisions compared with e + e – and e – p collisions. The p T -integrated nuclear modification factor of charm quarks, R pPb ($\mathbf {c\overline{c}}$) = 0.91 ± 0.04 (stat.) $^{+0.08}_{–0.09}$ (syst.) $^{+0.05}_{–0.03}$ (extr.) ± 0.03 (lumi.), is found to be consistent with unity and with theoretical predictions including nuclear modifications of the parton distribution functions.
This is a dataset of hydrated metal complexes and metal–nitrate hydrated complexes intended for public use, reproducibility, and downstream structural analysis. A key feature is coverage across the full lanthanide(III) series (La–Lu), enabling systematic comparisons of coordination motifs and bonding trends across the entire lanthanide sequence. In addition to the lanthanides, the dataset also includes other metal ions such as UO2(VI), Fe(II), and Fe(III). The dataset provides optimized geometries for hydrated and nitrate-containing hydrated complexes, together with representative ab initio molecular dynamics (AIMD) trajectories saved in standard XYZ formats. The accompanying NWChem input decks enable reproduction of the reported calculations and provide a starting point for extending the simulations to related coordination environments. Computationally, DFT calculations employ the B3LYP functional with DFT-D3BJ dispersion corrections and a COSMO continuum solvent model (dielectric constant 78.4) to represent solvation beyond the explicitly treated first hydration shell. AIMD simulations are performed with the NWChem qmd module at 298 K, integrating nuclear motion with the velocity-Verlet algorithm and controlling temperature using a Nosé–Hoover thermostat. Trajectories are approximately 4.8 ps in length and are used primarily to assess short-time stability of candidate coordination motifs, including (for lanthanides) differences between 8- versus 9-water coordination and comparisons between nitrate-bound and nitrate-free hydrated complexes.
This study focuses on post-combustion capture and oxy-fuel combustion for the boilers at the mill, as well as steam integration with the nuclear power plant. The primary goal of the research outlined in this report is to design, analyze, and document the integration of industrial-scale HTGR with a reference Kraft Pulp Mill. The purpose is to deliver reliable, cost-effective, and sustainable clean energy alternatives while reducing CO2 emissions. Specifically, this study focuses on 6 different scenarios that include carbon capture equipment and some of them use nuclear power to meet the heat and electricity needs of the reference plant. Also, 2 of these scenarios are created while also producing clean hydrogen through integrated High-Temperature Steam Electrolysis (HTSE). This report offers a detailed techno-economic assessment of different scenarios for a Kraft Pulp Mill, including an analysis of tax credits (section 45V, 45Q, and 48E) provided by the Inflation Reduction Act (IRA) of 2022. The evaluation explores the potential economic benefits and challenges of incorporating different configurations, including nuclear energy, into Kraft Pulp Mill operations, with particular attention to energy efficiency, economic implications, and environmental impact. By assessing both the technical feasibility and economic viability, this analysis aims to identify existing gaps and propose solutions for the successful implementation of nuclear integration. The findings are intended to provide valuable insights for stakeholders considering the adoption of advanced nuclear reactors in the pulp and paper industries.
Employing integrated energy systems (IESs) with nuclear power plants (NPPs) can improve NPP utilization by leveraging dedicated thermal and electric power delivery, but it may also increase operational safety risks. This paper presents a framework to identify and quantify hazards and risks for such IESs. The framework combines accidentology to review past industrial accidents with failure modes and effects analysis (FMEA) to identify potential future incidents. Hydrogen explosion and toxic chemical release hazards are of particular concern. Explosion consequences are quantified using the Bauwens-Dorofeev (Bauwens) and trinitrotoluene equivalent mass (TNT-EM) methods, while chemical release consequences are computed using the Gaussian atmospheric dispersion method. Operational disturbances from direct electrical and thermal integration that may affect NPP safety are modeled using probabilistic risk analysis (PRA). Hazards and risks are then evaluated for regulatory compliance. The framework is applied to IESs comprising pressurized or boiling water reactors supplying three levels of thermal and electrical power to industrial customers. Case studies include high-temperature steam electrolysis hydrogen plants of varying capacities and a synthetic fuel production plant. Sensitivity analysis examines piping component failures in the PRA model as a precursor to cost estimation for thermal extraction line design. Additionally, Fussel-Vessely (FV) and risk increase importance (RII) measures identify risk-informed design improvements for the thermal extraction system. FMEA highlights hazards such as loss of offsite power, prompt loss of electrical load, loss of thermal output, and immediate steam diversion, in addition to hydrogen explosions and toxic chemical releases. Both Bauwens and TNT-EM methods suggest maintaining several hundred meters of separation between the NPP and hydrogen facility to mitigate explosion risks. PRA results show a maximum initiating event frequency increase of 1.15% and an overall risk increase of 0.28%. Importance measure analysis identifies upstream pipe leak isolation components as critical. Evaluating the results against safety regulations, it is concluded that hazards and risks can be managed to comply with regulations through risk-informed thermal and electrical connection designs, component selection, maintenance programs, and safe separation distances between NPPs and integrated industrial facilities.
Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.