摘要
为了实现货运火车车厢内余煤分布不同情况下,余煤清扫机器人吸风量的自适应智能调控,建立了吸风量、余煤体积量二者与风机电流之间的控制模型,根据风机电流变化趋势实现吸风量的智能调控。首先利用果蝇优化算法对概率神经网络的平滑因子进行在线优化,构建基于改进概率神经网络的风机电流精准预测模型,并根据设备特性与现场经验建立了吸风量模糊化分档调控规则库。为了验证相关理论和方法,在HCQS-75/110型火车车厢余煤清扫机器人上开展吸风量智能调控实验,结果表明:改进后的概率神经网络对风机电流预测精度达到97.4%,搭载吸风量智能调控系统的清扫机器人可实现车厢一次清扫合格率达到98.2%,并节省能耗26.5%,可以满足现场环保和能耗要求。
In order to realize adaptive and intelligent control of aspirating quantity for coal residual cleaning robot under the different distribution of residual coal in the freight train carriage,a control model based on the aspirating quantity,residual coal volume and the fan current was established,and the aspirating quantity can be controlled intelligently according to the changing trend of fan current.Firstly,fruit fly optimization algorithm was applied to obtain an optimal smooth factor of probabilistic neural network.Then an accurate prediction model for the fan current based on the improved probabilistic neural network was constructed consequently.And a fuzzy step adjustment control rule base was set up by the combination of equipment characteristics and field experience.In order to verify the relevant theories and methods,an intelligent control experiment of aspirating quantity was carried out on residual coal cleaning robot for HCQS-75/110 train carriage.The results show that the prediction accuracy of fan current by the improved probabilistic neural network reaches 97.4%.The cleaning robot equipped with intelligent control system of aspirating quantity can achieve a pass rate of 98.2%and save 26.5%energy consumption,which can meet the requirements of on-site environmental protection and energy consumption.
作者
孙建福
王正强
李宁
SUN Jianfu;WANG Zhengqiang;LI Ning(Shanghai Meishan Steel Co.,Ltd.,Nanjing 210039,China)
出处
《矿业安全与环保》
北大核心
2021年第5期64-68,共5页
Mining Safety & Environmental Protection
基金
宝钢股份集团产学研合作项目(K20TMXY329)。
关键词
余煤清扫机器人
吸风量智能调控
概率神经网络
果蝇优化算法
平滑因子
模糊化调控
residual coal cleaning robot
aspirating quantity intelligent adjustment
probabilistic neural network
fruit fly optimization algorithm
smooth factor
fuzzy adjustment
