基于RBF神经网络的煤矿井下带式输送机节能优化研究
Research on energy-saving optimization of coal mine underground belt conveyor based on RBF neural network
马程 1雷鹏 2黄天尘 2张晓利 3叶鸥4
作者信息
- 1. 陕西陕煤榆北煤业有限公司,陕西榆林 719000
- 2. 陕西涌鑫矿业有限责任公司,陕西榆林 719000
- 3. 西安邮电大学,陕西西安 710000
- 4. 西安科技大学,陕西西安 710000
- 折叠
摘要
针对传统煤矿开采过程中能耗较高、污染物排放量较大的问题,利用径向基函数优化网络对输送机进行节能优化,构建了一种新的带式输送机节能优化模型.实验结果显示,研究模型在24 h内电能的平均节能率为17.3％,基于多目标优化算法的模型在24 h内的电能平均节能率为10.5％,基于粒子群算法的模型在24 h内的电能平均节能率为7.4％.研究模型下带式输送机每小时的能源消耗量为156.2 kWh,比粒子群模型和多目标模型的电用量分别减少423.53、367.5 kWh.综上可知,基于径向基函数优化网络的输送机在运行过程中可以减少能源消耗,实现节能减排,为提高煤矿生产设备的智能化水平提供了技术支持.
Abstract
Aiming at the problems of high energy consumption and large pollutant emissions in traditional coal mining processes,a new energy-saving optimization model for belt conveyors was constructed by using radial basis function optimization networks for energy-sav-ing optimization of conveyors.The results showed that the average energy-saving rate of the research model was 17.3％in 24 h.The av-erage energy-saving rate of the model based on multi-objective optimization algorithm was 10.5％in 24 h,and the average energy-saving rate of the model based on particle swarm optimization algorithm was 7.4％in 24 h.The energy consumption per hour of the study mod-el was 156.2 kWh,which was 423.53 kWh and 367.5 kWh less than that of the particle swarm model and the multi-objective model,respectively.To sum up,the conveyors based on the radial basis function optimized network can reduce energy consumption and achieve energy conservation and emission reduction during operation,and provide technical support for improving the intelligence of coal mine production equipment.
关键词
RBF神经网络/带式输送机/节能优化模型Key words
RBF neural network/belt conveyor/energy-saving optimization model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据