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使用混合机器学习模型和物理数量的相关性,预测 bentonite 中的离子扩散

Tao Wu¹, Junlei Tian², Xiaoqiong Shi²

¹School of Engineering, Huzhou University, Huzhou 313000, China; Huzhou Key Laboratory of Environmental Functional Materials and Pollution Control, Huzhou University, Huzhou 313000, China.

The Science of the total environment

|July 3, 2024

概括

这项研究使用机器学习来预测本顿石中的放射性核素扩散,将微观结构与扩散行为联系起来,以便更好地了解污染物运输.

科学领域:

地质化学和材料科学材料科学
环境科学与工程环境科学与工程
计算科学计算科学

背景情况:

放射性核酸在本托尼特中的扩散对于核废物处理安全至关重要.
了解本托尼特微观结构和扩散之间的联系是准确建模的关键.
现有的模型往往缺乏微观和中视镜特征的全面整合.

研究的目的:

开发和验证一个机器学习模型,预测本顿石中的放射性核素扩散系数.
为了阐明本托尼特的微型/介面结构和放射性核酸扩散之间的相关性.
通过使用先进的分析技术,确定影响放射性核素扩散的关键因素.

主要方法:

使用光梯度提升机 (LightGBM) 进行预测建模.
通过粒子集群优化 (PSO) 算法优化LightGBM超参数.
集成的微型 (离子半径,蒙莫里隆石堆叠) 和介光学 (孔隙性,导电性) 特性.
通过 HCrO4-,I- 和 CoEDTA2-. 的透射扩散实验验验证的预测.

主要成果:

该PSO-LightGBM模型准确预测了有效的扩散系数.
压缩干密度,水中的离子扩散系数,离子半径和总孔隙被确定为主要影响因素.
沙普利的附加解释和部分依赖图提供了对因素影响和关系的见解.
实验验证证证实了模型的可靠性和预测准确性.

结论:

机器学习,整合多个尺度的特征,增强对托尼特中放射性核素扩散机制的理解.
开发的模型有效地将本托尼特的微观结构与放射性核素运输行为联系起来.
这种方法提供了一种可靠的工具,用于评估地质构成中的污染物迁移.

关键词:

有效扩散系数的有效扩散系数机器学习是机器学习.部分依赖的图表部分依赖图表沙普利添加剂的解释通过扩散实验通过扩散实验.

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