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Investigation of sinter plant production rate and RDI using neural networks

机译:使用神经网络调查烧结厂生产率和RDI

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The current study helped to deepen understanding of the mechanisms behind reduction degradation of iron ore sinter. The model accuracy was very good taking into account the multivariable and complex nature of the sintering process. MSE was 0.25 using 7 inputs and 5 hidden nodes. The model is able to be investigated visually using 3-D video clips (Figure 1). The dislocation of RDI optima in a function of the time variable (b_2, in Figure 1) can be observed easily. It was found that the outside-air temperature can be used to describe the long-term behavior of RDI. It could not be determined, however, whether RDI depends on other periodic factors also. The effect of increasing temperature was detrimental on sinter. Its effect on production rate was also negative. The coke ratio in the sintering mix was found to be the best control variable concerning RDI. The index of mix physical properties (c-index) was found to decrease RDI and production rate values. High basicity was found to improve the sinter quality and also production rate. It was also found that alumina is not a significant predictor of RDI. Titanium content was found to be detrimental to the sinter. Ti content may also be an indicator of properties of certain special Ti bearing raw materials. The hearth layer height appeared to increase the RDI value and production rate.
机译:目前的研究有助于加深对铁矿石烧结减少降解后的机制的理解。考虑到烧结过程的多变量和复杂性,模型精度非常好。 MSE为0.25,使用7个输入和5个隐藏节点。该模型能够使用3-D视频剪辑在视觉上进行调查(图1)。可以容易地观察到时间变量(图1中的函数中RDI Optima的脱位。发现外部气温可用于描述RDI的长期行为。但是,RDI是否取决于其他周期因素也无法确定。温度升高的效果对烧结有害。它对生产率的影响也是阴性的。发现烧结混合物中的焦炭比是关于RDI的最佳控制变量。发现混合物理性质(C折射率)的指数降低了RDI和生产率值。发现高碱度提高烧结品质和生产率。还发现氧化铝不是RDI的显着预测因子。发现钛含量对烧结有害。 Ti含量也可以是某些特殊TI轴承原料的性质的指标。炉膛高度似乎增加了RDI值和生产率。

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