研究一个深度集成模型的结构优化在煤炭自发燃烧温度预测的研究
Xiaojun Zhang1, Xiaobin Yang2, Tianyu Fu1
1School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing).
Journal of visualized experiments : JoVE
|January 6, 2026
概括
这项研究引入了一种优化的深度学习模型,用于预测煤炭自燃温度. 先进的框架提高了实时煤矿安全监测的准确性和适应性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 煤炭自燃在采矿操作中构成重大安全风险.
- 传统的温度预测方法在一般化和适应性方面扎.
- 煤矿需要强大的实时监控系统.
研究的目的:
- 开发一项用于预测煤炭自燃温度的先进协议.
- 克服固定架构的局限性和现有方法的可转移性差.
- 提高煤矿早期预警系统的准确性和可靠性.
主要方法:
- 使用了一种精灵搜索算法 (SSA) 优化的卷积神经网络 (CNN) - 长期短期记忆 (LSTM) - 注意力框架.
- 采用CNN用于空间特征提取和LSTM用于时间依赖性捕获.
- 集成了一个注意力机制来突出关键温度阶段和特征.
- SSA优化了网络深度和超参数,以实现动态数据适应.
主要成果:
- 拟议的模型在同质数据集上显示出明显更高的预测准确性.
- 在不同采矿条件的异质数据集中实现了强大的概括性能.
- 该框架被证明非常适合实时监控煤矿温度.
- 与传统预测方法相比,显示出更高的性能.
结论:
- SSA-CNN-LSTM-Attention框架为煤炭自燃温度预测提供了一种卓越的方法.
- 该协议为各种采矿环境提供了一个动态和适应性的解决方案.
- 这一进步对于提高煤矿安全性和实施有效的早期预警系统至关重要.
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