在PUREX过程中,机器学习预测酸提取行为的PUREX过程
Sankar V Harilal1,2, Matilda I Duffy1, Eva Brayfindley1
1Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
ACS omega
|January 1, 2025
概括
机器学习模型准确地预测了缩提取 (PUREX) 系统中的酸行为. 这有助于优化核燃料再加工的实验,并了解活性化物提取.
科学领域:
- 核化学 核化学 核化学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 减提取 (PUREX) 过程对于从废核燃料中回收和至关重要.
- 由于复杂的水性料成分,预测PUREX中的提取参数具有挑战性.
- 了解酸的行为至关重要,因为它会影响PUREX过程中酸提取的过程.
研究的目的:
- 开发PUREX系统中酸分布的预测模型.
- 利用机器学习来克服传统热力学模型的局限性.
- 为优化 PUREX 工艺设计和实验提供一个 in silico 工具.
主要方法:
- 编制了关于酸行为的广泛的溶剂提取文献数据.
- 开发机器学习模型来预测有机阶段酸度.
- 研究了初始酸和酸和稀释剂度的影响.
主要成果:
- 机器学习模型在预测酸平衡度方面表现出很高的准确性.
- 这些模型成功地将酸和TBP度与不同稀释剂的酸提取相关联.
- 生成的ML辅助响应表面提供了新的预测能力.
结论:
- 机器学习为模拟PUREX.EX等复杂化学过程提供了一种强大的方法.
- 开发的ML模型是PUREX流程优化中的一个有价值的in silico工具.
- 这项工作在预测和优化核燃料再加工中的活性化物分离方面取得了重大进展.
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