基于BP神经网络钒钛烧结矿低温还原粉化性能预测

为了改善钒钛烧结矿的低温还原粉化性能,将BP神经网络算法应用于钒钛烧结矿低温还原粉化性能预测中,指标数据的样本分为输入样本和输出样本,其中:输入样本为配碳量、碱度、w(Mg O)以及FMG粉配比,输出样本为钒钛烧结矿RDI+3.15,运用BP神经网络算法探索输入样本与输出样本间的关系。结果表明:BP神经网络模型适用于烧结矿还原粉化性能的研究,可以根据输入样本有效的预测输出样本,且平均相对误差为5.7%,满足工程实践中预测精度的要求,为钒钛烧结矿生产提供了指导。

Forecast for Low Temperature Reduction Disintegration Properties of Vanadium-titanium Sinter Based on BP Neural Network

In order to improve the RDI+3.15 of vanadium-titanium sinter,BP neural network algorithm was applied to the prediction of low temperature reduction degradation of vanadium-titanium sinter.The samples of the indicator data were divided into input samples and output samples,the input samples includes carbon,basicity,w(MgO) and FMG ore,and the output sample was low temperature reduction degradation of vanadium-titanium sinter.The relationship between input samples and output samples was explored by using BP neural network algorithm.The results show that BP neural network model is suitable for studying on the low temperature reduction disintegration properties of sinter.It can predict the output samples effectively according to the input samples,and the average relative error is 5.7％,meeting the requirement of prediction precision in engineering practice,which provides guidance for the production of vanadium-titanium sinter.