摘要
煤矿通风机是保证煤矿安全生产的重要大型设备,提高其自动化、智能化水平意义重大。当前多数煤矿井下通风机工作时长期处于恒速运转模式,能耗消耗巨大。为了克服通风机工作中一风吹的状态,使其根据实际需要智能调风、按需供风、节能减耗,根据煤矿井下温湿度、瓦斯浓度及煤尘浓度实际情况,运用遗传算法与BP神经网络相结合的控制方法,构建了预测井下需风量的网络模型;使用MATLAB软件对遗传算法优化的BP神经网络风量预测效果进行了测试。结果显示,系统预测准确率高,达到理想效果。通过工控机、PLC、变频器及各类传感器等相关硬件以及软件技术,设计完成了通风机智能监控系统,使通风机智能按需调节风量,提高了通风机自动化、智能化控制水平。
The coal mine ventilation fans are important large-scale equipment to ensure coal mine safety production,and improving their automation and intelligence level is of great significance.The current underground ventilation fan in coal mines operates at a constant speed mode for a long time,with huge energy consumption.In order to overcome the state of one wind blowing during the operation of the ventilation fan,and to intelligently adjust and supply air according to actual needs,energy conservation and consumption reduction,a control method combining genetic algorithm and BP neural network was used to construct a network model for predicting the underground air demand based on the actual situation of temperature and humidity,gas concentration,and coal dust concentration in the coal mine.MATLAB software was used to test the prediction effect of the BP neural network optimized by genetic algorithm,and the results showed high prediction accuracy and ideal effect.The intelligent monitoring system for ventilation fans has been designed through industrial computer,PLC,frequency converter,various sensors and other related hardware and software technologies,achieving the effect of intelligent on-demand adjustment of air volume for ventilation fans,and improving the automation and intelligent control level of ventilation fans.
作者
方志伟
FANG Zhiwei(College of Artificial Intelligence and Information Engineering,Jinken College,Jiangsu Nanjing 210000,China)
出处
《工业仪表与自动化装置》
2025年第3期45-50,共6页
Industrial Instrumentation & Automation
基金
江苏地下空间智慧运维工程技术研究开发中心“地下空间Mesh无线自组网通信网络技术及应用”(课题编号jsdxkjzh-2023-03)
2023年江苏省高校“青蓝工程”优秀青年骨干教师资助项目。
关键词
通风机
煤矿
遗传算法
BP神经网络
智能监控系统
ventilator
coal mine
genetic algorithm
BP neural network
intelligent monitoring system
