在深层煤矿中提高气体度预测和通风效率:混合DL-Koopman和Fuzzy-PID框架
Keyi Yuan1,2, Ke Gao3,4,5, Yujiao Liu1,2
1College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning Province, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了深层煤矿的智能通风框架,使用深度学习和DL-Koopman理论进行准确的气体预测和Fuzzy-PID控制以优化空气流量,提高安全性和可持续性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 传统的通风系统在深层,动态的煤矿中面临安全和运营方面的挑战.
- 越来越多的深度和复杂性需要先进的环境性能和可持续性的解决方案.
研究的目的:
- 开发一种用于深层煤矿智能通风的创新框架.
- 提高气体度预测的准确性和优化空气流调节,以提高安全性和能源效率.
主要方法:
- 深度学习与DL-Koopman运算子理论的整合用于预测建模.
- 实施模糊自适应PID (模糊-PID) 控制策略,用于实时调节空气流.
- 分析历史气体度和风速数据以识别模式.
主要成果:
- 在波动的通风条件下,DL-Koopman模型显著提高了气体度预测的准确性.
- 模糊PID控制策略证明了动态参数调整,增强系统稳定性和减轻干扰.
- 双重方法确保快速适应地下环境变化.
结论:
- 拟议的框架为煤矿采矿中智能通风系统提供了一条实用途径.
- 这项研究通过提高安全性和能源效率,为更清洁,更可持续的采矿实践做出了贡献.
- 该研究与全球能源转型目标保持一致,通过减少煤炭开采的环境足迹.
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