基于WOA-LSTM算法的煤矿瓦斯智能化防治及管控研究  

Research on Intelligent Prevention Management,and Control of Coal Mine Gas Based on WOA-LSTM Algorithm

作　　者：刘虎 王新驭 王永峰 张德弦 史昀棠 LIU Hu;WANG Xinyu;WANG Yongfeng;ZHANG Dexian;SHI Yuntang

机构地区：[1]内蒙古鸿远煤炭集团有限公司孙三沟煤矿,内蒙古鄂尔多斯010300

出　　处：《化工设计通讯》2025年第4期138-140,共3页Chemical Engineering Design Communications

摘　　要：随着煤炭开采的深入,煤矿瓦斯智能化防治及管控变得尤为关键。为解决传统瓦斯管理在效率和准确上的不足,研究提出了一种集成鲸鱼优化算法和长短期记忆网络的智能化预测模型。该模型通过鲸鱼优化算法调整长短期记忆网络的网络参数,有效提升了瓦斯涌出量的预测准确性。实验结果显示,该模型在多个评估指标上均优于传统的门控循环单元算法和双向长短期记忆网络模型,其不仅能更快收敛,同时平均误差仅为0.14%,显著降低了瓦斯涌出量的预测误差。此次研究为煤矿瓦斯的实时监测和智能化管理提供了技术支撑,有助于提高采矿作业的安全性。With the deepening of coal mining,the intelligent prevention and control of coal mine gas has become particularly critical.In order to solve the shortage of effi ciency and accuracy of traditional gas management,an intelligent prediction model integrating whale optimization algorithm and long and short term memory network is proposed.The model adjusts the network parameters of the long and short-term memory network by whale optimization algorithm,and effectively improves the prediction accuracy of gas emission.The experimental results show that the model is superior to the traditional gated cyclic unit algorithm and bidirectional long short-term memory network model in many evaluation indexes.It not only converges faster,but also has an average error of 0.14%,which signifi cantly reduces the prediction error of gas emission.This research provides technical support for the real-time monitoring and intelligent management of coal mine gas,and helps to improve the safety of mining operations.

关 键 词：煤矿工程 鲸鱼优化算法 长短期记忆网络 瓦斯预测 智能化防治 

分 类 号：TD324[矿业工程—矿井建设]