通过使用机器学习和密度函数理论的煤炭飞灰基地聚合物对重金属固定的新洞察力
Kaizhi Yang1, Bo Yang1, Kezhou Yan2
1Institute of Resources and Environmental Engineering, Shanxi University, State Environmental Protection Key Laboratory of Efficient Utilization of Waste Resources, Taiyuan 030006, China.
Waste management (New York, N.Y.)
|September 21, 2025
概括
机器学习和DFT预测了基于煤 (CFA) 的地质聚合物中的重金属固定. 这种方法提高了对可持续废物管理的固化机制的理解.
科学领域:
- 材料科学 材料科学 材料科学
- 环境工程 环境工程
- 计算化学的计算化学
背景情况:
- 基于煤炭飞灰 (CFA) 的地质聚合物为重金属固定和废物利用提供可持续的低碳解决方案.
- 优化地质聚合物合成和评估重金属固定传统上涉及昂贵和耗时的方法缺乏机械洞察力.
研究的目的:
- 开发一种结合机器学习 (ML) 和密度函数理论 (DFT) 的方法,用于预测和理解基于CFA的地质聚合物中的重金属固定.
- 确定影响重金属固定效率的关键因素.
主要方法:
- 评估了八个ML模型,确定了梯度增强回归 (GB) 作为表现最好 (R2 = 0.9284).
- 利用DFT计算来阐明重金属固定的原子层机制.
- 进行特征重要性分析,以对影响因素进行排名.
主要成果:
- GB模型准确地预测了固定性能.
- 特性重要性表示重金属特性 > 地质聚合物原材料特性 > 固化条件 > 活性剂特性.
- DFT揭示了将大半径的水合重金属离子,低Si/Al比率和高含量结合起来可以增强固定性.
结论:
- 集成的ML + DFT方法为预测复杂废物系统中的重金属固定提供了强大的工具.
- 这种方法为固化过程提供了有价值的机械洞察力,改善了环境应用的地质聚合物设计.
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