基于BP神经网络技术的红土镍矿还原焙烧-磁选工艺条件的优化

还原焙烧—磁选工艺可有效提取红土镍矿中的镍和铁等有价金属，由于影响红土镍矿还原焙烧—磁选效果的因素较多，导致工业生产中的选矿指标不稳定。为进一步提高还原焙烧—磁选工艺处理红土镍矿的效果，本研究以青海某镍矿为原料，采用正交试验与BP神经网络相结合的方法，对还原焙烧—磁选工艺的还原剂用量、焙烧温度、料层厚度、焙烧时间及磁场强度等因素进行了优化。结果表明:通过BP神经网络模型优化后的试验条件为还原剂用量9.5%、焙烧温度1 070℃、料层厚度10.0 mm、焙烧时间65 min及磁场强度2.5 kA·m-1，在此条件下可获得产率为30.29%的镍粗精矿，比采用正交试验最优因素组合条件所得的镍粗精矿产率提高了2.83%。 

Condition Optimization of Reduction Roasting Magnetic Separation Technology for Laterite Nickel Ore by BP Neural Network Technique

Reduction roasting magnetic separation process can effectively extract nickel, iron and other valuable metals from laterite nickel ore. Due to the multiple factors existing in the process of reduction roasting magnetic separation of laterite nickel ore, the industrial indicators are unstable. In order to further improve the effect of reduction roasting magnetic separation process in laterite nickel ore, the factors of reducing agent dosage, roasting temperature, material thickness, roasting time and magnetic field intensity were optimized with a nickel ore in Qinghai as raw material by combining orthogonal experiment and BP neural network. The results showed that the optimized experimental conditions by BP neural network model are as follows: dosage of reducing agent 9.5%, roasting temperature 1 070 ℃, layer thickness 10.0 mm, roasting time 65 min and magnetic field strength 2.5 kA·m-1. Under these conditions, a rough nickel concentrate with a yield of 30.29% can be obtained, which is 2.83% higher than the yield of nickel rough concentrate obtained by using the optimal factor combination conditions of the orthogonal test.