Research of ultra-fine comminuting coal with premized water jet based on neural network
Due to the deficiency of premixed water jet theory and the complicated non-linear relations between the comminuting productive rate and its affecting factors, tt is difficult to establish mathematical model of comminuting productive rate with traditional mathematical method. Choosing comminuting pressure, slurry concentration and comminuting times as main influencing factors, adopting target comminuting method of dihedral nozzle submerge premixed water jet, ultra-fine comminuting to coal samples whit granularity between 0.3mm-0.5mm was carried out. According to experimental data, the artificial neural network was applied to establish mathematical model of comminuting productive rate. The mathematical model was used for the forecast of comminuting productive rate. The results indicate that the average error of model training is small, the forecast effect is good, and it can satisfy the request of forecast precision that engineering practice to comminuting productive rate.
water jet neural network comminuting coal grain forecast
Wang Rui-hong Chen Ke-feng Li De-yu Ma An-chang
College of Computer and Information Engineering, Heilongjiang Institute of Science and Technology, h College of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou
国际会议
The 6th International Conference on Mining Science & Technology ICMST 2009(第六届国际矿业科学技术大会)
徐州
英文
1-6
2009-10-18（万方平台首次上网日期，不代表论文的发表时间）