Nuclear Engineering and Technology最新文献

A comparative study on machine learning models for estimating cathode material compositions in black powders using prompt gamma-ray spectra 利用提示伽玛射线谱估计黑粉末正极材料成分的机器学习模型的比较研究

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-06 DOI: 10.1016/j.net.2025.104069

Yohan Lee , Byoungil Jeon , Soobin Lim

According to the rapid growth of the Lithium-ion battery (LIB) recycling industry, the demands for rapid and non-destructive analysis of black powder, the key product in this recycling process, has increased to enable effective recovery of valuable materials and the formulation of optimal recycling strategies. This study aims to evaluate the feasibility of a deep learning application that estimates the composition of valuable materials in black powder in prompt gamma-ray activation analysis (PGAA). Reference data for major target materials, Nickel, Cobalt, Manganese, and Iron, were acquired using the Four-circle single-crystal diffractometer at the HANARO research reactor, and synthetic datasets with varying counting statistics were generated for model training. To identify the most suitable model for PGAA-based composition estimation, we conducted a comparative analysis of six machine learning algorithms, including machine learning (Lasso, Decision Tree, XGBoost) and deep learning architectures (MLP, CNN, Transformer). Among them, the Transformer architecture demonstrated best performance, achieving an r² score of over 0.99 for all components. Furthermore, uncertainty quantification using deep ensembles confirmed that the model provides reliable confidence intervals, essential for industrial decision-making. These results demonstrate the promising potential of the deep learning method for effective and high-throughput battery recycling applications.

随着锂离子电池（LIB）回收行业的快速增长，对黑粉这一回收过程中的关键产品进行快速、无损分析的需求日益增加，以实现有价值材料的有效回收和优化回收策略的制定。本研究旨在评估在快速伽马射线激活分析（PGAA）中估计黑火药中有价值物质组成的深度学习应用的可行性。主要目标材料镍、钴、锰和铁的参考数据使用HANARO研究堆的四圆单晶衍射仪获取，并生成具有不同计数统计量的合成数据集用于模型训练。为了确定最适合基于pgaa的成分估计的模型，我们对六种机器学习算法进行了比较分析，包括机器学习（Lasso, Decision Tree, XGBoost）和深度学习架构（MLP， CNN, Transformer）。其中，Transformer体系结构表现出最好的性能，所有组件的r2得分都超过0.99。此外，使用深度集成的不确定性量化证实了该模型提供可靠的置信区间，这对工业决策至关重要。这些结果证明了深度学习方法在有效和高通量电池回收应用中的巨大潜力。

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引用次数: 0

Enhancing SEU tolerance efficacy in advanced FinFET FPGA devices using system-level fine-grained spatial redundancy techniques 利用系统级细粒度空间冗余技术增强先进FinFET FPGA器件的SEU容差效率

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-04 DOI: 10.1016/j.net.2025.104047

Chang Cai , Hong-Jie Zeng , Ze-Qi Huang , Xue-Zhi Zheng , Yi Sun , Jing Zhang , Min-Chi Hu , Han-Tao Jing , Zhi-Xin Tan , Rui-Rui Fan , Jun Ge , Shu-Sheng Pan

This paper investigates the Single Event Upset (SEU) sensitivity, system-level hardening effectiveness, and potential applications of high-performance 16 nm Field Programmable Gate Arrays (FPGAs) in radiation environments. Representative circuits incorporating flip-flops and configured arithmetic logic units were specifically designed using FPGA internal resources. This integration introduces extra upset errors due to the additional utilization of arithmetic logics, contributing to a better understanding of SEU sensitivity in FPGA-based circuits within actual application designs. The irradiation tests were conducted to evaluate the SEU sensitivity of D flip-flops (DFFs) and Configuration Memories (CRAMs) under various application conditions. The results indicate that Fine-Grained (FG) Triple Modular Redundancy (TMR) circuits play a critical role in achieving high SEU tolerance, whereas general TMR hardening circuits often prove ineffective in most experiments, even with triplicated flip-flops. FG TMR circuits were developed to address the limitations of general TMR circuits, achieving SEU tolerance improvements by three orders of magnitude for output protection. Notably, FG TMR circuits showed no global signal-induced failures during proton irradiation tests. Furthermore, these system-level radiation tolerance designs present promising applications for Commercial Off-The-Shelf (COTS) devices in spaceflight and ground accelerator facility.

本文研究了高性能16nm现场可编程门阵列（fpga）在辐射环境中的单事件扰动（SEU）灵敏度、系统级硬化效率和潜在应用。利用FPGA内部资源，设计了包含触发器和配置算术逻辑单元的代表性电路。由于算术逻辑的额外利用，这种集成引入了额外的干扰误差，有助于在实际应用设计中更好地理解基于fpga的电路中的SEU灵敏度。通过辐照试验，评价了D触发器（dff）和组态存储器（cram）在不同应用条件下的SEU敏感性。结果表明，细粒度（FG）三模冗余（TMR）电路在实现高SEU容限方面发挥了关键作用，而一般的TMR硬化电路在大多数实验中往往被证明是无效的，即使是三次触发器。FG TMR电路的开发是为了解决一般TMR电路的局限性，实现了输出保护的SEU容差提高了三个数量级。值得注意的是，在质子辐照测试中，FG TMR电路显示没有全局信号诱导故障。此外，这些系统级辐射容限设计为航天和地面加速器设施中的商用现货（COTS）设备提供了有前途的应用。

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引用次数: 0

Improving hydration resistance of gadolinium-based burnable absorbers via cerium oxide additions 添加氧化铈提高钆基可燃吸收剂的抗水化性能

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-04 DOI: 10.1016/j.net.2025.104067

Hyeong Jin Kim, Hyeongju Bae, Ho Jin Ryu

The hydration behavior of gadolinium oxide (Gd₂O₃) under high-temperature water conditions presents a significant challenge for the development of soluble-boron-free small modular reactor (SMR) fuels. To overcome this issue, we investigated cerium oxide (CeO₂) additions to improve the hydration resistance of Gd₂O₃. Pellets containing 0–25 at.% Ce were exposed to pressurized water at 200 °C, 1.5 MPa, for 48 h. Gd₂O₃ pellets are originally monoclinic under 1600 °C sintering temperature, whereas CeO₂ additions progressively suppress the phase transformation; at 15 at.% or higher Ce doping, a single cubic phase forms. XRD showed that undoped Gd₂O₃ fully hydrates, whereas specimens with 15 at.% or higher Ce doping retain the cubic structure with no detectable hydroxides. FT-IR results also provide evidence of hydration resistance of Ce-doped Gd₂O₃, by vanishing Gd-O-H bands with the Ce addition. XPS indicated that Ce ions are predominantly Ce⁴⁺ in 15 at.% or higher Ce-doped Gd₂O₃, suggesting that Ce⁴⁺ substitution on Gd³⁺ sites prevents oxygen-vacancy-assisted hydroxylation. Conversely, a lower Ce⁴⁺ fraction in pellets sintered in an Ar atmosphere correlated with an increased hydration tendency. These results indicate that 15 at.% or higher Ce doping stabilizes the cubic phase of Gd₂O₃ and completely suppresses hydration under the tested conditions, as a result of a reduced oxygen vacancy concentration.

高温水条件下氧化钆（Gd2O3）的水化行为是开发可溶无硼小型模块化反应堆（SMR）燃料的一个重大挑战。为了克服这一问题，我们研究了添加氧化铈（CeO2）来提高Gd2O3的抗水化性能。含有0-25 at的弹丸。% Ce在200℃，1.5 MPa的高压水中暴露48 h。在1600℃烧结温度下，Gd2O3球团最初是单斜的，而CeO2的加入逐渐抑制了相变；15点。%或更高的Ce掺杂，形成单立方相。XRD结果表明，未掺杂的Gd2O3完全水化；%或更高的Ce掺杂保留了立方结构，没有检测到氢氧化物。FT-IR结果还通过Ce的加入使Gd-O-H带消失，提供了Ce掺杂Gd2O3的水化阻力的证据。XPS表明，15at中Ce离子以Ce4+为主。%或更高的ce掺杂Gd2O3，表明在Gd3+位点上的Ce4+取代阻止了氧空位辅助羟基化。相反，在Ar气氛中烧结的球团中Ce4+分数越低，其水化倾向越强。这些结果表明15 at。%或更高的Ce掺杂稳定了Gd2O3的立方相，在测试条件下，由于氧空位浓度降低，完全抑制了水化作用。

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引用次数: 0

Extension of the formula between reactivity and concentrations of fissile and non-fissile nuclide in the fuel salt for thermal neutron spectrum molten salt reactors 热中子谱熔盐堆燃料盐中可裂变和不可裂变核素浓度与反应性关系式的推广

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-04 DOI: 10.1016/j.net.2025.104068

Changqing Yu , Guifeng Zhu , Jintong Cao , Yang Zou , Rui Yan , Xiaohan Yu

Due to the unique properties of liquid fuel in molten salt reactors (MSRs), reactor reactivity is directly correlated with the fuel salt composition. Previous studies have established and verified a linear relationship between reactivity and the inverse of fissile nuclide concentration in thermal spectrum MSRs. This paper aims to expand the relationship to non-fissile nuclides. Non-fissile nuclides, including carrier salts, and fission products, are widely distributed within MSRs. Unlike fissile nuclides, increasing the concentration of these non-fissile isotopes reduces reactivity. By applying the "1/v law" for neutron absorption cross-sections in the thermal neutron energy range, we demonstrate that reactivity is linearly related to the concentration of these non-fissile isotopes, in contrast to fissile ones. Furthermore, a generalized relationship expressing reactivity as a function of both fissile and non-fissile nuclide concentrations is developed and validated. This formula enables the calculation of reactor reactivity for any fuel salt composition without requiring repeated neutron transport calculations, offering significant potential for criticality and burnup calculations in MSRs.

由于熔盐堆中液体燃料的独特性质，反应堆的反应性与燃料盐组成直接相关。以往的研究已经在热谱MSRs中建立并验证了反应性与可裂变核素浓度反比之间的线性关系。本文的目的是将这种关系扩展到不可裂变的核素。非裂变核素，包括载体盐和裂变产物，广泛分布在msr内。与易裂变核素不同，增加这些非易裂变同位素的浓度会降低反应性。通过对热中子能量范围内的中子吸收截面应用“1/v定律”，我们证明了反应性与这些不可裂变同位素的浓度呈线性关系，而与可裂变同位素的浓度相反。此外，一个广义的关系表示反应性作为一个函数的可裂变和不可裂变核素浓度被开发和验证。该公式可以计算任何燃料盐成分的反应堆反应性，而不需要重复的中子输运计算，为msr中的临界和燃耗计算提供了重要的潜力。

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引用次数: 0

Potential applications of artificial intelligence in spent nuclear fuel reprocessing research: A review 人工智能在乏燃料后处理研究中的潜在应用综述

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-04 DOI: 10.1016/j.net.2025.104060

Tianchi Li, Qi Chen, Jia Zhou, Yandong Sun, Yanhong Jia, Yang Bai, Jianhua Guo, Qiuyue Zhang, Xiaojin Wang, Fangliang Yao, Zhi Cao, Wentao Wang, Fang Liu, Taihong Yan, Weifang Zheng

Spent nuclear fuel reprocessing plays a pivotal role in achieving a closed nuclear fuel cycle. However, traditional research approaches face significant obstacles due to the highly radioactive environment, limited availability of experimental data, and the inherent complexity of the underlying mechanisms. Artificial intelligence (AI), with its strength in extracting intricate patterns and making predictions from sparse data, offers promising solutions to these longstanding challenges. Recent advancements have demonstrated the potential of AI in diverse domains, including image recognition, process modeling, materials discovery, process control, data analytics, and safety assessment. By addressing the limitations of conventional methods in data handling and mechanistic modeling, AI enhances system safety and operational efficiency under extreme conditions. Furthermore, it complements traditional physics-based approaches, paving the way for transformative research paradigms. As the volume of accessible data grows and algorithms continue to evolve, AI applications in spent fuel reprocessing are expected to expand—enabling fully integrated, self-optimizing control systems and proactive safety monitoring. These developments promise to significantly reshape the landscape of nuclear fuel reprocessing technologies.

乏燃料后处理在实现密闭核燃料循环中起着关键作用。然而，由于高放射性环境、实验数据的有限可用性以及潜在机制的内在复杂性，传统的研究方法面临着重大障碍。人工智能（AI）凭借其在提取复杂模式和从稀疏数据中进行预测方面的优势，为这些长期挑战提供了有希望的解决方案。最近的进展已经证明了人工智能在不同领域的潜力，包括图像识别、过程建模、材料发现、过程控制、数据分析和安全评估。通过解决传统方法在数据处理和机械建模方面的局限性，人工智能提高了系统在极端条件下的安全性和运行效率。此外，它补充了传统的基于物理的方法，为变革性研究范式铺平了道路。随着可访问数据量的增长和算法的不断发展，人工智能在乏燃料后处理中的应用有望扩大——实现完全集成、自我优化的控制系统和主动安全监测。这些发展有望显著重塑核燃料后处理技术的格局。

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引用次数: 0

Research on depletion chain simplification method based on pseudo-decay nuclides 基于伪衰变核素的耗尽链简化方法研究

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-03 DOI: 10.1016/j.net.2025.104066

Bin Zhang , Xingmin Tang , Lianjie Wang , Xiaojing Liu , Kai Huang , Tengfei Zhang

The applicability of the depletion database determines the accuracy of burnup and power calculations during core simulation. The information of depletion chains in evaluated nuclear databases is unnecessarily complex for most applications in reactor physics, leading to large, rigid, and inefficient burnup matrix. This paper introduces a method for simplifying the depletion chains, which considering the impact of various nuclides and their corresponding transmutation pathways on the calculation accuracy of reactivity rates and nuclide densities. The impact of each reaction channel and decay nuclides is evaluated by depletion calculations of typical problems. Since fission products are intended to be reduced from depletion chains, pseudo-decay nuclides are introduced to substitute the role of fission products to maintain the accuracy of decay heat calculations. The verification results show that the number of nuclides tracked to be reduced from 1500 to 200 or fewer nuclides. No significant deviations are introduced in the calculation of effective multiplication factors and nuclide number densities by the simplified depletion chains. And the pseudo-decay nuclides significantly maintain the accuracy of decay heat calculations, with the deviation of decay heat contribution to total power being less than 0.5 %.

耗尽数据库的适用性决定了堆芯模拟过程中燃耗和功率计算的准确性。在大多数反应堆物理应用中，评估核数据库中的耗尽链信息过于复杂，导致燃耗矩阵大、刚性和低效。本文介绍了一种简化耗尽链的方法，该方法考虑了各种核素及其相应的嬗变途径对反应率和核素密度计算精度的影响。每个反应通道和衰变核素的影响通过典型问题的耗尽计算来评估。由于裂变产物打算从耗尽链中还原，因此引入了伪衰变核素来代替裂变产物的作用，以保持衰变热计算的准确性。验证结果表明，跟踪到的核素数量从1500个减少到200个或更少。简化耗尽链在计算有效增殖因子和核素数密度时没有引入明显的偏差。伪衰变核素保持了衰变热计算的准确性，衰变热对总功率贡献的偏差小于0.5%。

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引用次数: 0

Physics-guided multi-task learning for predicting thermophysical properties of Ag-In-Cd absorber alloys with extremely small data 以极小数据预测Ag-In-Cd吸收合金热物理性质的物理引导多任务学习

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-02 DOI: 10.1016/j.net.2025.104061

Yi-Rui Hu , Xiaoya Wang , Zhaohe Gao , Hongxing Xiao , Hongsheng Chen , Jie Xiong

Machine learning (ML) techniques have recently shown great promise in advancing nuclear materials research. However, the application of ML in this field is often limited by the scarcity of experimental data due to the high cost and complexity of irradiation experiments. In this study, we propose a physics-guided multi-task neural network (PGMTNN) framework to accurately predict the thermophysical properties of Ag-In-Cd control rod alloys under neutron irradiation, using extremely limited datasets. The PGMTNN model incorporates fundamental physical laws as soft constraints within a multi-task learning architecture, enabling the simultaneous prediction of density, specific heat capacity, thermal diffusivity, and thermal conductivity. This approach effectively captures the intrinsic correlations among different material properties and ensures physical consistency of the predictions. Compared with traditional ML methods and the transformer-based foundation model, the PGMTNN demonstrates superior performance, achieving an average coefficient of determination exceeding 0.92 on the testing set. These results underscore the potential of physics-guided, multi-task ML frameworks for accurate property prediction and reliable design of advanced absorber materials under small-data conditions.

机器学习（ML）技术最近在推进核材料研究方面显示出巨大的希望。然而，由于辐照实验的高成本和复杂性，机器学习在这一领域的应用往往受到实验数据稀缺的限制。在这项研究中，我们提出了一个物理引导的多任务神经网络（PGMTNN）框架，以准确预测Ag-In-Cd控制棒合金在中子辐照下的热物理性质，使用极其有限的数据集。PGMTNN模型将基本物理定律作为多任务学习架构中的软约束，能够同时预测密度、比热容、热扩散率和导热系数。这种方法有效地捕获了不同材料特性之间的内在相关性，并确保了预测的物理一致性。与传统的机器学习方法和基于变压器的基础模型相比，PGMTNN表现出更好的性能，在测试集上的平均确定系数超过0.92。这些结果强调了物理指导的多任务机器学习框架在小数据条件下准确预测性能和可靠设计先进吸收材料的潜力。

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引用次数: 0

Risk concerns and sustainable development goals influencing the adoption of tiny nuclear batteries: Findings from public perceptions 影响微型核电池采用的风险关切和可持续发展目标：来自公众看法的调查结果

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-02 DOI: 10.1016/j.net.2025.104062

Jin-Wei Wang , Jiaojiao Li

Tiny nuclear batteries (TNBs) are one of the cutting-edge technologies to meet the future sustainable energy needs of residents, and will play an important role in the future modern low-carbon energy system. However, the public's adoption intention of this new technology and its influencing mechanism are still unclear. This study expanded the technology acceptance model of TNBs from the perspective of risk and sustainable development, analyzed 387 questionnaires collected in Beijing in depth, and revealed the key transmission path affecting TNBs' adoption intention using structural equation modeling and the bootstrap technique. In the context of Beijing, the results indicate that Hazardous Materials Risk (HMR), Performance Risk (PR), and Sustainable Development Goals (SDGs) have only indirect effects on the Behavioral Intention (BI) of TNBs, and Green Perceived Usefulness (GPU) is an important mediating variable. In addition, this study found that "ever heard of TNBs" (EH) is an important moderating variable. If one had been aware of TNBs in advance, the total indirect effect of PR would have been significantly increased by 0.212.

微型核电池是满足未来居民可持续能源需求的前沿技术之一，将在未来现代低碳能源体系中发挥重要作用。然而，公众对这项新技术的采用意愿及其影响机制尚不清楚。本研究从风险和可持续发展的角度拓展了tnb的技术接受模型，并对北京地区收集的387份问卷进行了深入分析，运用结构方程模型和bootstrap技术揭示了影响tnb采用意愿的关键传播路径。在北京市背景下，研究结果表明，有害物质风险（HMR）、绩效风险（PR）和可持续发展目标（sdg）对tnb的行为意向（BI）只有间接影响，绿色感知有用性（GPU）是一个重要的中介变量。此外，本研究还发现“是否听说过tnb”（EH）是一个重要的调节变量。如果事先知道tnb， PR的总间接效应将显著增加0.212。

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引用次数: 0

Research on the regulation of welding residual stress in 316L austenitic stainless steel thick-section based on ultrasonic impact and vibration 基于超声冲击和振动的316L奥氏体不锈钢厚截面焊接残余应力调节研究

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-02 DOI: 10.1016/j.net.2025.104063

Ya-Qi Zhong , Zhi-Hong Liu , Hai-Biao Ji , Rui Wang , Jian-Guo Ma , Yu-Dong Su , Hua Zhai

In nuclear fusion engineering, high residual tensile stresses post-weld critically undermine structural integrity by reducing fatigue strength, accelerating crack propagation, and increasing the risk of failure. Alleviating residual tensile stresses in thick-section 316L austenitic stainless steel continues to be a significant scientific and engineering issue. This study investigated the effects of ultrasonic vibratory stress relief (UVSR) and ultrasonic impact treatment (UIT) through 4 customized process schemes: layer-by-layer UVSR, post-weld UVSR, and the UIT-UVSR composite process. The distribution of residual stress, microstructure, and mechanical properties of welded joints in fusion reactor vacuum vessel components were thoroughly assessed. All four schemes diminished welding residual stresses, enhanced the microstructure, and elevated local mechanical characteristics. Significantly, post-weld UVSR attained optimal stress relief, diminishing longitudinal and transverse residual stresses by 51.2 % and 25 %, respectively, while facilitating a more uniform stress distribution. The UIT-UVSR composite processes could not demonstrate synergy beyond individual UVSR, and layer-by-layer UVSR proved less effective than post-weld UVSR. In summary, post-weld UVSR is an efficient technique for regulating residual stresses in thick-section 316L welds, offering practical insights for components of fusion-reactor vacuum vessels.

在核聚变工程中，高残余拉伸应力会降低疲劳强度，加速裂纹扩展，增加失效风险，从而严重破坏结构的完整性。减轻厚截面316L奥氏体不锈钢的残余拉伸应力仍然是一个重大的科学和工程问题。本研究通过4种定制工艺方案：逐层UVSR、焊后UVSR、UVSR -UVSR复合工艺，研究了超声振动应力消除（UVSR）和超声冲击处理（UIT）的效果。对核聚变反应堆真空容器部件焊接接头的残余应力分布、显微组织和力学性能进行了全面评估。四种方案均能降低焊接残余应力，改善微观组织，提高局部力学特性。值得注意的是，焊后UVSR达到了最佳的应力消除效果，纵向和横向残余应力分别减少了51.2%和25%，同时使应力分布更加均匀。单元-UVSR复合工艺在单独的UVSR之外不能表现出协同作用，并且逐层的UVSR被证明不如焊接后的UVSR有效。综上所述，焊后UVSR是一种有效的316L厚截面焊缝残余应力调节技术，为聚变反应堆真空容器部件提供了实用的见解。

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引用次数: 0

Artificial neural network modeling and response surface methodology as efficient tools for predicting the effective parameters of uranium extraction from chloride media 人工神经网络建模和响应面方法是预测氯化物介质中提铀有效参数的有效工具

IF 2.6 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Engineering and Technology

Pub Date : 2025-12-02 DOI: 10.1016/j.net.2025.104065

Fereshte Khanramaki, Fazel Zahakifar

This study focuses on optimizing solvent extraction parameters to efficiently recover uranium from chloride solutions, a process with significant potential for the nuclear industry. Initially, various extractants were evaluated using a single-variable method, which identified di(2-ethylhexyl) phosphoric acid (D2EHPA) in kerosene as having high potential for extracting uranium from chloride media. Subsequently, other significant process parameters were assessed: chloride ion concentration (0.05–2 mol/L), extractant concentration (0.01–0.2 mol/L), pH of the aqueous phase (0.1–2), and contact time (5–60 min). Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) were employed to model the data and compare the results of the extraction process. The RSM model a second-degree polynomial equation with an R² value of 0.9846, fitted the experimental data excellently. The ANN model with two hidden layers predicted the extraction efficiency with an R² of 0.9911, indicating a strong fit. Under the optimum conditions, the extraction efficiency was 98.87 % according to the RSM and 99.63 % according to the ANN, both of which are very close to the experimental value of 99.43 %. This research contributes to optimizing uranium extraction processes, enhancing efficiency, and potentially reducing the environmental impact of the nuclear industry.

本研究的重点是优化溶剂萃取参数，以有效地从氯化物溶液中回收铀，这一过程在核工业中具有重要的潜力。最初，使用单变量方法评估了各种萃取剂，确定煤油中的二（2-乙基己基）磷酸（D2EHPA）具有从氯化物介质中提取铀的高潜力。随后，评估了其他重要工艺参数：氯离子浓度（0.05-2 mol/L）、萃取剂浓度（0.01-0.2 mol/L）、水相pH（0.1-2）和接触时间（5-60 min）。采用响应面法（RSM）和人工神经网络（ANN）对数据进行建模，并对提取过程的结果进行比较。RSM模型为二阶多项式方程，R2值为0.9846，与实验数据拟合良好。具有两个隐藏层的ANN模型预测提取效率的R2为0.9911，表明拟合较强。在最佳提取条件下，RSM提取效率为98.87%，ANN提取效率为99.63%，均与实验值99.43%非常接近。这项研究有助于优化铀提取过程，提高效率，并可能减少核工业对环境的影响。

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引用次数: 0