基于机器学习的全周期评估方法,用于煤矿煤气开采性能
Jianhui Wu1, Lizhen Zhao2,3, Yang Du3
1Henan Energy Chemical Construction Group Co., Ltd., Zhengzhou, Henan 454000, China.
ACS omega
|December 22, 2025
概括
这项研究引入了评估煤矿天然气开采的新框架,提高了安全性和效率. 先进的模型准确地预测气体压力,帮助智能矿山管理.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
- 计算科学 计算科学
背景情况:
- 煤炭开采深度增加,导致关键的天然气相关灾害.
- 目前的天然气开采缺乏科学评估,动态监测和及时的有效性评估.
- 这些限制阻碍了安全和高效的煤炭资源开采.
研究的目的:
- 建立一个全面的,全周期的煤矿天然气开采评估框架.
- 开发用于初步方案评估和动态监测的预测模型.
- 为加强煤矿安全和智能管理提供科学决策支持.
主要方法:
- 基于贝叶斯优化的随机森林回归 (BO-RFR) 用于初步方案评估.
- 深度神经网络和卷积自编码器 (DNN-CAE) 用于残气压力场的动态预测和重建.
- 将这些模型整合到从设计到实施的全周期评估框架中.
主要成果:
- 在BO-RFR模型中,预测余气压的误差低于0.02 MPa.
- DNN-CAE动态评估模型表现出高准确度,MSE为2.73 × 10 ^ - 5和MAE为0.00493.
- 现场测试证实了在实际应用中提出的方法的准确性和可靠性.
结论:
- 开发的框架和模型为煤矿天然气控制挑战提供了有效的解决方案.
- 能够精确控制和智能管理天然气提取过程.
- 显著的实用价值被证明是提高煤矿生产安全.
更多相关视频
相关概念视频
01:14Mechanical Efficiency of Real Machines
1.2K
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
However, in reality, no machine can be truly ideal, and all of them experience some...
1.2K
00:56Extraction: Advanced Methods
1.0K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.0K