采矿道空气体积的间隔组合预测,考虑多种影响因素
Zhen Wang1,2, Erkan Topal2, Liangshan Shao1
1School of Business Administration, Liaoning Technical University, Huludao, Liaoning, China.
PloS one
|February 7, 2025
概括
精确的地雷道空气体积测量对于安全至关重要. 本研究引入了一种使用先进信号处理和神经网络的新型间隔预测模型,显著提高了地下环境中空气体积预测的准确性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
- 信号处理 信号处理
背景情况:
- 矿山安全非常依赖于持续和准确的空气体积监测.
- 由于空气体积波动和测量不稳定性,地下环境存在挑战.
研究的目的:
- 开发一种准确的方法来测量矿山道的空气体积.
- 为了提高空气体积和相关通风参数的预测准确度.
主要方法:
- 使用间隔类型的完整集体实证模式分解与自适应噪声 (In-CEEMDAN) 进行数据预处理.
- 阶段空间重建以确定影响因素.
- 基于神经网络的预测结合了模糊的数字和一般化诱导的有序加权平均运算符.
- 使用灰色相关性和L2范式进行优化,用于全面的间隔预测模型.
主要成果:
- 拟议的模型有效地减少了联合加权平均绝对误差 (CWMAE),联合加权平均平方根误差 (CWRMSE) 和联合加权平均绝对百分比误差 (CWMAPE).
- 来自煤矿的实验结果显示,预测准确度有了显著的改善.
结论:
- 开发的间隔组合预测模型准确地预测了矿井道空气体积.
- 该方法提供了一个强大的解决方案,通过可靠的通风监控来提高安全性.
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