基于监督机器学习的预测储存类,利用二二甲作为有机载体
Ahsan Ali1, Muhammad Adnan Khan2,3,4, Hoimyung Choi1
1Department of Mechanical Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Molecules (Basel, Switzerland)
|March 28, 2024
概括
双乙烯 (H0-DBT) 是一种安全的液态有机载体. 机器学习准确地预测储存类,持久验证方法实现97%的整体准确性以获得最佳结果.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 液态有机载体 (LOHC),如二氧化 (H0-DBT) 提供安全的缩储存.
- 准确预测储能容量对于LOHC应用至关重要.
- 机器学习 (ML) 对复杂的材料属性预测越来越重要.
研究的目的:
- 根据容量,将储存数据分为低,中,高等级.
- 开发和评估一种机器学习模型,用于预测H0-DBT中的这些储存类.
- 为了确定最佳的验证技术,准确的储存预测.
主要方法:
- 开发一个支持矢量机 (SVM) 模型,称为HSP-SVM,用于储存的分类.
- 使用5倍交叉验证 (5-FCV),再置换验证 (RV) 和持久验证 (HV) 的性能评估.
- 对每个验证方法的分类准确度和误解率的分析.
主要成果:
- 持久验证 (HV) 方法表现出卓越的性能.
- HV实现了高类特定精度:98.5% (低),97% (中等) 和98.5% (高).
- HV的整体准确率为97%,误解率为3%,优于5-FCV和RV (93.9%的准确率,6.1%的误解率).
结论:
- 持久验证 (HV) 方法是准确预测H0-DBT中的储存类的最佳方法.
- 通过HV验证的HSP-SVM模型提供了一个可靠的工具,用于评估LOHC中的储存潜力.
- 精确的ML驱动的预测增强了用于能系统的LOHC技术的开发和应用.
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