【摘要】 分析烧结生产中影响烧结矿FeO含量的众多因素,选择碱度、配煤量、一次温度、制粒效果、加水量、料层厚度、点火温度、煤气流量等8个工艺参数以及4种矿粉配比作为FeO含量预报模型的输入变量.分别采用BP神经网络、RBF神经网络、SVM 3种进行建模预测.预测结果表明,SVM预测性能优于BP神经网络,RBF神经网络优于SVM.更多还原

【Abstract】 Many factors affecting FeO content in sinter were analyzed and the eight technological parameters such as basicity,blending coal ratio,first temperature,pelleting effect,adding water volume,bed depth,ignition temperature and coal gas flow and proportion of the four kinds of fine iron ore were chosen as the input variables to form a model to predict FeO content in the sintering ore.Three algorithms: BP neural network,RBF neural network,and SVM were selected.The results showed that predicting performance of SVM is better than that of BP neural network and predicting performance of RBF neural network is better than that of SVM.更多还原