数字双驱动的深度学习预测和适应性控制用于煤矿通风系统
Xijun Yang1, Hui Li2
1Ventilation Management Department, Huangyuchuan Coal Mine of Guoneng Yili Energy Co., Ltd, Ordos, 017209, Inner Mongolia, China.
Scientific reports
|December 4, 2025
概括
本研究介绍了煤矿通风的智能框架,整合了数字双胞胎和人工智能,以提高安全性和效率. 该系统在现场测试中实现了显著的能源节约和更快的响应.
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
- 网络物理系统 网络物理系统
背景情况:
- 煤矿通风系统需要实时监测和控制,尽管复杂的地下环境和不可预测的干扰.
- 传统方法难以应对动态条件并实现最佳性能.
研究的目的:
- 开发一个集成的框架,用于煤矿智能通风管理.
- 提高地下采矿业务的安全性,效率和可持续性.
主要方法:
- 构建了一个五维的数字双胞胎架构,用于双向同步.
- 开发了一个LSTM-Attention混合神经网络,用于预测通风参数 (MAPE 2.87%,R2 0.9612).
- 实施了适应模型预测控制策略,用于动态优化.
主要成果:
- 现场验证显示,对照准确率为97.3%.
- 在能源消耗方面降低了27%.
- 与传统方法相比,显示了66.4%的更快的系统响应.
结论:
- 拟议的框架为智能通风管理提供了一个实际的解决方案,克服了传统方法的局限性.
- 这项研究推动了采矿领域的网络物理集成,并验证了人工智能对安全关键的工业系统的有效性.
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