The strength prediction model of iron ore sinter based on an artificial neural network
The strength prediction model of iron ore sinter based on an artificial neural network
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DOI：
10.1080/03019233.2022.2096991
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发表时间：
2022-07
影响因子：
2.1
通讯作者：
Qing-yun Huang;Zenghao Liu;Zhongci Liu;X. Lv
中科院分区：
文献类型：
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作者：
Qing-yun Huang;Zenghao Liu;Zhongci Liu;X. Lv
烧结矿仍是高炉炼铁的主要原料，其强度和还原性等性能对高炉的生产率和顺行至关重要。在本研究中，建立了一个基于人工神经网络（ANN）的烧结矿强度预测模型。利用已发表论文的可信数据生成样本集，对神经网络模型进行训练。对直接预测模型和以热力学理论计算的液相和脊相量为中间层的间接预测模型进行了比较。结果表明，间接人工神经网络模型的预测精度明显高于无中间层的直接模型，对验证模型的参数研究表明，烧结矿强度随SiO2含量的增加先增加到5.4%，然后随SiO2含量的增加而降低。
The iron ore sinter is still the main raw material for the blast furnace ironmaking process, its properties, such as strength and reducibility, are of vital importance to the productivity and the smooth operation of the blast furnace. In the present study, one model based on an artificial neural network (ANN) was established to predict the sinter strength. The ANN model was trained with the sample set, which was generated from the credible data from the published papers. The comparison between the direct prediction model and the indirect prediction model with the amount of liquid phase and spinal phase calculated with thermodynamic theory as the middle layer was carried out. The results show that the indirect ANN model gave much higher accurate prediction results than that of the direct one without the middle layer.The parametric study with the validated model shows that the sinter strength increased first with increasing the SiO2 to 5.4% and then decreased with further increasing the SiO2.