基于NIRS的煤炭和岩石识别,使用卷积多视图广泛学习系统
Yuanbo Lv1, Shibo Wang1, En Yang2
1College of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
Heliyon
|October 22, 2024
概括
一个新的近红外光谱 (NIRS) 系统在采矿过程中准确地识别煤石混合物. 这项技术有助于确定停止屋顶洞穴的最佳时间,减少采矿损失并改善成本回收.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 从厚中有效地生产煤炭需要精确控制挖掘过程.
- 决定何时停止屋顶洞穴挖掘对于最小化采矿损失和最大化成本回收至关重要.
- 现有的方法与可变光谱数据质量扎,并且缺乏跨特征处理阶段的整合.
研究的目的:
- 开发一种创新的识别系统,用于在现场使用近红外光谱 (NIRS) 来识别煤石.
- 创建一个强大的煤岩识别方法,克服现有技术在光谱数据采集和处理的局限性.
- 改进决策过程,以阻止厚厚的煤层采矿的屋顶洞穴.
主要方法:
- 开发利用近红外光谱 (NIRS) 技术的现场识别系统.
- 实施一种煤岩识别方法,将卷积和多视图功能纳入广泛学习系统 (BLS) 模型.
- 该方法被设计为不变于采集因素,如颗粒度和传感器角度.
主要成果:
- 开发的BLS模型实现了99.78%的非凡的煤岩识别准确度.
- 在工作面上实验部署识别系统,证明了整个煤炭挖掘过程的有效识别.
- 该系统在现实采矿条件下实现了92.3%的实际识别精度.
结论:
- 基于NIRS的识别系统为长壁采矿的现场煤岩识别提供了可行的解决方案.
- 开发的方法有效地处理光谱数据质量的变化,并整合处理阶段的特征.
- 精确识别煤炭挖掘过程可以实现最佳的停止点,这对于高效的采矿操作至关重要.
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