基于ITOC优化的深度学习框架,用于预测煤炭自燃温度:一个合的CNN-BiGRU-CBAM模型
Xuming Shao1, Wenhao Liu2, Gang Bai3,4,5
1Safety Science and Engineering College, Liaoning Technical University, Huludao, 125105, Liaoning , China. shaoxuming66@163.com.
Scientific reports
|July 22, 2025
概括
本研究引入了一种先进的深度学习模型,用于使用关键气体指标预测煤炭自燃温度. 新的ITOC-CNN-BiGRU-CBAM框架通过在煤矿中实现精确的早期预警系统来提高安全性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
背景情况:
- 煤炭自燃 (CSC) 是矿山的主要安全风险,需要准确的温度预测,用于预警系统.
- 了解煤炭氧化和热解对于评估燃烧风险和制定有效的控制策略至关重要.
研究的目的:
- 开发和验证一种新的深度学习框架,用于在煤炭自燃中准确预测温度.
- 确定与CSC温度高度相关的关键气体指标,以改进预测建模.
主要方法:
- 对编程加热实验数据和煤氧化-热解合反应机制的分析.
- 皮尔森相关性分析以确定六个关键气体指标 (O2,CO,C2H4,CO/ΔO2,C2H4/C2H6,C2H6).
- 开发一个改进的龙卷风优化与科里奥利斯力 (ITOC) 战略,与CNN-BiGRU-CBAM深度学习模型集成,用于超参数优化和预测.
主要成果:
- 与五个启发式算法相比,ITOC算法显示出更高的准确性和合稳定性.
- 拟议的ITOC-CNN-BiGRU-CBAM模型在测试组中实现了高精度,R2为0.9738,MAPE为4.1254%,MAE为6.2740和RMSE为12.4735.
- 在多个煤矿的现场验证证实了该模型的强烈概括性和工程适应性,预测温度与测量密切匹配.
结论:
- ITOC-CNN-BiGRU-CBAM模型提供了一种强大而准确的方法来预测煤炭自燃温度.
- 这种智能框架提供了一个有前途的理论和实践解决方案,用于早期预警和精确预防矿业操作中的CSC危险.
- 确定的关键气体指标可以作为评估煤炭氧化阶段和燃烧风险的可靠预测指标.
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