基于改进BP神经网络的镁还原率预报研究

Research on magnesium reduction degree prediction based on improved BP neural network

介绍了BP神经网络和遗传算法的原理及特点,简述了皮江法炼镁工艺流程。为了研究各工艺参数与镁还原率之间的关系,针对标准BP神经网络存在的收敛速率慢、易陷入局部极小值等缺陷,建立了以煅白活性度、配硅比、制球压力、还原时间、还原温度、真空度为输入,镁还原率为输出的基于遗传算法优化的BP神经网络镁还原率预报模型。利用筛选后的生产数据对模型进行训练和预测,结果显示该预报模型能够较为精确地预报镁还原率,预测误差在±1.0%范围内的命中率达96%,最大误差小于1.3%,一定程度上可用于指导皮江法炼镁工艺中工艺参数的选择。

The principle and characteristics of BP neural network and genetic algorithm are introduced and Pidgeon process is briefed.The standard BP neural network has disadvantages of slow-rate convergence and getting easily into local minima value. The prediction model based on BP neural network optimized by genetic algorithm with input of calcined dolomite activity,silicon ratio,pelletizing pressure,reduction time,reduction temperature,and vacuum degree is established to study the relationship between process parameters and magnesium reduction degree.The model is rehearsed and tested by the screening production data.The results show that the prediction model can precisely predict the magnesium reduction degree,the hit rate of the model withΔηMg≤ ±1.0%is about 96%,the maximum error is less than 1.3%.To some extent the selection of process parameters in Pidgeon process can be extracted by the model.