摘要
通风机在保证矿井安全方面发挥着重要作用。利用先进的变频技术和智能算法程序,设计了局部通风机的智能控制系统。在已知当前矿井环境参数的基础上,利用Elman神经网络模型可以对矿井局部位置的需求通风量进行准确实时预测,精度可以控制在0.79%范围内。利用模糊PID控制技术对通风机的实际风量进行控制,经过模拟分析发现具有良好的控制精度,延时3~4 s。主控制器和变频器是系统中重要的硬件装置,选用的型号分别为STM32F103VB和BPJ96-660。将智能控制系统部署到工程实践中,经现场测试发现能够根据矿井环境对通风量进行实时智能化调整。经过6个月的现场应用,局部通风机设备每年可以节省192万kWh的电能,经济效益显著。
Ventilators play an important role in ensuring mine safety.The intelligent control system of local ventilators is designed by using advanced frequency conversion technology and intelligent algorithm program.On the basis of the known current mine environmental parameters,the Elman neural network model can be used to accurately and real-timely predict the required ventilation at the local location of the mine,and the accuracy can be controlled within the range of 0.79%.The fuzzy PID control technology is used to control the actual air volume of the ventilators.After simulation analysis,it is found that it has good control accuracy and the delay is 3~4 s.The main controller and frequency converter are important hardware devices in the system,and the selected models are STM32F103VB and BPJ96-660 respectively.The intelligent control system is deployed in engineering practice,and it is found through field tests that the ventilation volume can be adjusted intelligently in real-time according to the mine environment.After 6 months of on-site application,the local ventilator equipment can save 1.92 million kWh of electric energy every year,and the economic benefits are remarkable.
作者
陈华
Chen Hua(Tianjin University,Tianjin 300072,China)
出处
《能源与环保》
2023年第9期200-205,共6页
CHINA ENERGY AND ENVIRONMENTAL PROTECTION
基金
张家口市2019度社会科学研究课题(2019146)。
关键词
煤矿
局部通风机
神经网络
智能控制
coal mine
local ventilator
neural network
intelligent control
