在自发燃烧和氧化过程中由多种指标气体驱动的温度间隔的预测
Tong Zhang1,2, Baoshan Jia1,2,3, Di Zhang1,2
1Safety Science and Engineering College, Liaoning Technical University, Huludao 125105, Liaoning, China.
ACS omega
|February 23, 2026
概括
这项研究开发了一种新的气体驱动模型,用于预测煤炭自燃 (CSC) 温度. 灰狼优化 - 卷积神经网络 - 双向长期短期记忆 - 量子回归 (GWO-CNN-BiLSTM-QR) 模型准确预测CSC风险,提高矿山安全.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
背景情况:
- 煤炭自燃 (CSC) 是地下煤矿的一个重大危险,需要准确的温度预测才能有效预防.
- 现有的方法往往缺乏及时风险评估和缓解策略所需的精度.
研究的目的:
- 开发和验证一种新的,多指标的,燃气驱动的煤炭自燃温度间隔预测模型.
- 将先进的机器学习算法与气体演变数据集成,以提高预测准确度.
主要方法:
- 使用煤样本进行CSC加热实验,并通过自主开发的系统监测气体演变 (O2,CO,CH4,CO2,C2H6,C2H4).
- 开发了一个GWO-CNN-BiLSTM-QR模型,使用200个数据集,输入指示气体数据和输出煤炭温度.
- 使用点 (R2,RMSE) 和间隔 (CWC,MPICD) 预测指标评估模型性能,与基准模型进行比较.
主要成果:
- 该GWO-CNN-BiLSTM-QR模型实现了高精度,R2为0.9554和RMSE为16.1455,用于点预测.
- 该模型在95%和80%的置信度级别下表现出优异的间隔预测性能,CWC和MPICD值较低.
- 对独立数据的验证证实了该模型强大的概括能力和预测性能.
结论:
- 拟议的GWO-CNN-BiLSTM-QR模型为CSC的温度间隔预测提供了一种强大而准确的方法.
- 这种气体驱动的方法为CSC预测和煤矿风险减轻提供了宝贵的理论见解和实际指导.
- 该框架显示了煤矿安全管理中大规模工程应用的巨大潜力.
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