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一种基于图像处理和深度学习的新型粒子大小分布校正方法,用于使用NIRS-XRF进行煤炭质量分析.

Rui Gao1, Jiaxin Yin1, Ruonan Liu1

  • 1State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Laser Spectroscopy, Shanxi University, Taiyuan, 030006, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030006, China.

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概括

这项研究引入了一种使用图像处理和深度学习的新型颗粒大小校正方法,以改善近红外 (NIRS) 和X射线光 (XRF) 光谱技术的煤炭质量分析. 该方法通过减轻粒子大小对光谱测量的影响,显著提高了准确性和可重复性.

关键词:
在美国,CNN是CNN.护士们的护士们颗粒大小分布 颗粒大小分布萨姆·萨姆·萨姆·萨姆是什么意思标签: 美国 STN STN这是XRFXRF.

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科学领域:

  • 分析化学 分析化学
  • 频谱学是一种光谱学.
  • 数据科学数据科学数据科学

背景情况:

  • 结合近红外 (NIRS) 和X射线光 (XRF) 光谱学,提供了可靠的煤炭质量分析.
  • 粒子大小的变化会对NIRS和XRF的准确性和可重复性产生负面影响.
  • 现有的方法缺乏有效的减轻在光谱煤炭分析中的粒子大小影响的策略.

研究的目的:

  • 开发和验证用于NIRS-XRF煤炭分析的创新颗粒大小校正方法.
  • 通过解决颗粒大小变化,提高煤炭质量预测的准确性和可重复性.
  • 提高对不同煤炭颗粒大小的光谱分析模型的适应性.

主要方法:

  • 整合图像处理和深度学习技术用于粒子大小分布分析.
  • 微观图像捕获和分段任何模型 (SAM) 用于二元化和粒子表示.
  • 空间变压器网络 (STN) 用于几何校正和卷积神经网络 (CNN) 用于特征提取和错误相关模型.

主要成果:

  • 预测误差显著减少:标准偏差 (SD) 从0.321%降至0.229%,平均绝对误差 (MAE) 从0.317%降至0.225%,预测的根平均平方误差 (RMSEP) 从0.335%降至0.257%.
  • 与校正前值相比,SD的错误减少百分比为64.06%,MAE为50%,RMSEP为60.80%.
  • 证明了模型可重复性和准确性的改进,有效地减轻了亚毫米颗粒大小的影响.

结论:

  • 拟议的深度学习和图像分析方法有效地纠正NIRS-XRF煤炭分析中的粒子大小变化.
  • 这种方法显著提高了光谱质量检测模型的准确性,可重复性和适应性.
  • 自动化分析和实时校正对于散装材料的在线质量检测技术具有前景.