从煤炭分析中预测总热量,使用基于决策树的包装和增强技术
Tanveer Alam Munshi1, Labiba Nusrat Jahan1, M Farhad Howladar1
1Department of Petroleum and Mining Engineering, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh.
Heliyon
|January 3, 2024
概括
这项研究引入了先进的机器学习模型,可以在没有炸弹热量计的情况下预测煤炭的总热量 (GCV). XGBoost表现出卓越的准确性,为煤炭质量评估提供了更快,更有效的替代方案.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 在能源资源中的机器学习应用.
- 分析化学和燃料的特性分析.
背景情况:
- 炸弹热量计是确定煤炭总热量 (GCV) 的标准,但它耗时且劳动密集.
- 开发使用近距离和最终分析的替代方法对于高效的煤炭质量评估至关重要.
- 现有的经验模型往往缺乏准确性,因为它们的简单性和特定位置的约束.
研究的目的:
- 开发和评估用于预测煤炭GCV的新型机器学习模型.
- 为了比较新的基于树的算法 (额外的树,包装,决策树,自适应增强) 与已建立的算法 (随机森林,梯度增强,XGBoost) 的性能.
- 为准确的GCV建模确定最重要的煤炭特征.
主要方法:
- 利用了来自美国地质调查局煤炭质量 (COALQUAL) 数据库的7430个数据点,包括近距离和最终分析.
- 开发并测试了用于GCV预测的额外树,包装,决策树和自适应提升模型.
- 研究了随机森林,梯度增强和XGBoost,并将它们与实证模型进行比较 (Schuster,Mazumdar,Channiwala和Parikh,Parikh等,CFRI).
- 使用详尽的网格搜索调整模型,并使用统计索引 (R2,MSE,MAE,MAPE) 评估性能.
主要成果:
- 包装和提升技术实现了超过0.97.9的确定系数 (R2).
- XGBoost的表现优于其他所有模型,R2值为0.9974,误差指标最低 (MSE: 14703.3,MAE: 89.2).
- 氧气和碳含量被确定为GCV预测最重要的特征,而挥发性物质和硫是最不显著的.
结论:
- 机器学习模型,特别是XGBoost,为GCV确定提供了高精度和高效的炸弹热量计替代方案.
- 开发的模型可以显著减少与传统实验室分析相关的时间,成本和复杂性.
- 这种方法可以快速准确地评估煤炭的质量,帮助工程师和运营商在燃料分级和利用方面.
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