用白盒方法来实现储存与金属有机框架的强大相关性
Arefeh Naghizadeh1, Fahimeh Hadavimoghaddam2,3, Saeid Atashrouz4
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Scientific reports
|February 10, 2025
概括
金属有机框架 (MOFs) 显示出对清洁储存的承诺. 这项研究使用先进的计算模型准确预测MOF中的储能容量,识别关键影响因素.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 是一种关键的清洁能源载体.
- 金属有机框架 (MOF) 对储存具有前景.
- 准确预测储能容量至关重要.
研究的目的:
- 用软计算方法估计MOF中的储量.
- 开发可靠的相关性来预测储能容量.
- 确定影响MOF中储存的关键参数.
主要方法:
- 使用的白盒方法:数据处理的组方法 (GMDH),遗传编程 (GP) 和基因表达编程 (GEP).
- 使用温度,压力,孔积和表面积作为输入参数.
- 进行统计和图形错误评估和灵敏度分析.
主要成果:
- 该GMDH模型实现了最高的准确性 (RMSE:0.410,MAE:0.307),与GEP显示可比性能.
- 孔腔体积和压力分别被确定为最强的线性和非线性预测指标.
- 温度对储存产生了最小的负面影响.
结论:
- 开发了准确的预测模型,用于MOF中的储存.
- 孔径体积和压力是优化基于MOF的储量的关键因素.
- 这些模型显示出高的统计可靠性,96%的数据在可接受的区域内.
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