从辅料运行特性的角度对烧结工艺做了总体分析，并对烧结矿的质量指标及其影响因素做了研究，在此基础上运用了一种带动量项和变学习率的BP神经网络算法建立了烧结矿质量预测模型。模型预报结果表明，用拓扑结构为15-20-4的BP神经网络和0000191的网络误差进行训练后，模型的命中率在833%以上，充分展示了基于辅料运行特性的烧结矿质量预测模型的准确性和有效性。
A general analysis of sinter process from the aspect of characteristic of subsidiary material′s movement was finished, and the quality index and its influence factors were investigated. The quality prediction model for the sinter was built using a transformed BP neural network algorithm with momentum and variable learning rate. The prediction results show that the predictive hit-ratio of random samples is over 833% through adopting the BP neural network with the 15-20-4 structure and network err of 0000191. It can be concluded that the model have higher accuracy and effectiveness of prediction on the basis of characteristic of subsidiary material.
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