使用NIRS-XRF聚变光谱和非线性残余校正测量大颗粒煤的热值:一个PLS-AE-RR模型框架
Yifan Qiao1,2, Ruonan Liu1,2, Bin Li3
1State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan 030006, China. qiaoyifan22@163.com.
Analytical methods : advancing methods and applications
|February 16, 2026
概括
这项研究引入了一个新的NIRS-XRF融合光谱框架,用于准确的在线煤炭热量预测,克服大型煤炭颗粒的挑战. PLS-AE-RR模型显著提高了煤和煤的预测准确度.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学方法.
- 机器学习 机器学习
背景情况:
- 在线预测煤炭的热量值对于发电厂的效率至关重要.
- 大型煤颗粒 (0-6毫米) 由于磨削困难,矩阵效应和非线性而带来挑战,降低了预测准确度.
- 现有的方法难以应对原煤样本的复杂性和变异性.
研究的目的:
- 开发一个强大而准确的框架,用于在线预测粗煤颗粒的热量值.
- 解决传统方法在处理煤炭样本中的矩阵效应和非线性方面的局限性.
- 提高在线煤炭质量评估的可靠性,以改善燃料管理.
主要方法:
- 提出了一个名为PLS-AE-RR (部分最小平方 - 自动编码器 - 回归) 的新型NIRS-XRF融合光谱框架.
- 采用了三层混合架构,结合了线性基线 (PLS),非线性特征提取 (AE) 和剩余校正 (RR).
- 在153个混合粒子煤样本上使用定制的NIRS-XRF双光谱系统验证了框架.
主要成果:
- 在粗煤样本中,PLS-AE-RR模型实现了高预测准确度的热量.
- 试验组的R2值为0.974的煤和0.938的煤.
- 与其他非线性校正方法 (PLS-AE-RF,PLS-AE-SVR) 相比,表现优越,MAE和RMSE最低.
结论:
- PLS-AE-RR框架为粗煤的在线热量预测提供了重大进展.
- 该方法为燃煤发电厂提供了高精度,高可靠性的工具,减少了手工样品预处理的需要.
- 通过精确的在线测量,支持优化燃料管理和煤炭混合战略.
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