使用机器学习模型对放射性核酸溶解度的实验分析和预测:有机复合剂的影响
Bolam Kim1, Amaranadha Reddy Manchuri1, Gi-Taek Oh2
1Department of Environmental Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.
Journal of hazardous materials
|March 16, 2024
概括
放射性废物中的有机复合剂增加了放射性核素的可溶性和可移动性. 机器学习,特别是高斯过程回归 (GPR),准确地预测了这种可溶性,有助于库存安全评估.
科学领域:
- 环境科学 环境科学
- 放射化学 放射化学是指辐射化学.
- 计算科学 计算科学
背景情况:
- 放射性废物含有有机复合剂,增强放射性核素的溶解性和移动性.
- 了解放射性核素-溶性相互作用对于安全的废物处理至关重要.
研究的目的:
- 在不同的环境条件下评估,,和的可溶性.
- 开发和验证用于预测放射性核素溶解性的机器学习模型.
主要方法:
- 进行了四种放射性核素和三种有机复合剂的可溶性批量实验.
- 不同的pH值,温度和复合剂度.
- 使用贝叶斯优化开发和优化了四种监督机器学习模型 (GPR,增强树,ANN,SVM).
主要成果:
- 从可溶性实验中生成了720个数据集.
- 高斯过程回归 (GPR) 证明了强大而准确的放射性核酸溶解度预测.
- 在95%的不确定性水平下,GPR预测与实验结果有很高的相关性.
结论:
- 在复杂的环境条件下,GPR是预测放射性核酸溶解度的合适模型.
- 准确的可溶性预测可以提高从废物储存库中评估放射性核素的流动性.
- 这种方法支持改善放射性废物管理的安全评估.
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