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使用机器学习预测固相组中的分布系数

Seok Min Hong¹, In-Ho Yoon², Kyung Hwa Cho³

¹Department of Civil, Urban, Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.

|February 16, 2024

概括

机器学习模型使用分布系数 (K) 准确预测-137 (Cs) 迁移. 这有助于核废物管理,通过评估各种条件下的污染物流动性和环境风险.

科学领域:

环境科学与工程环境科学与工程
核废物管理核废物管理
地质化学地质化学

背景情况:

放射性污染物的迁移,特别是,对核废物储存提出了重大挑战.
分布系数 (K_d) 对于评估污染物流动性至关重要,但对环境因素敏感.
准确的K_d预测对于在放射性废物处理中进行可靠的安全和风险评估至关重要.

研究的目的:

开发和评估机器学习模型,用于预测各种环境介质中的Cs分布系数 (K).
确定影响Cs吸收和移动性的关键环境和地化学变量.
为改善核废物管理中的环境风险评估和安全分析提供工具.

主要方法:

使用了日本原子能机构的 Sorption 数据库 (JAEA-SDB),其中包含 14 个输入变量.
开发和训练了三个机器学习模型:随机森林 (RF),人工神经网络 (ANN) 和卷积神经网络 (CNN).
使用规范化和日志转换预处理数据;使用R²和RMSE.评估模型性能.

主要成果:

射频,ANN和CNN模型实现了高预测准确性,R2值分别超过0.97,0.86和0.88.
变量重要性分析确定了环境介质,初始放射性核素度,固相特性和溶液条件作为重要的预测因素.
模型在各种环境条件下表现出强的性能,表明它们适用于K_d预测.

结论:

机器学习模型,特别是射频,可以准确预测Cs Kd值,这对于评估污染物流动性至关重要.
开发的模型可以加强核废物处理的安全分析和长期风险评估.
准确的K_d预测有助于防止环境中放射性物质的潜在危险和污染.

关键词:

是一种.分布系数分布系数 JAEA-SDB是美国国家航空航天局.机器学习是机器学习.吸附吸附方式吸附吸附方式重要性的变量.

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