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首页> 外文期刊>International Journal of Mineral Processing >Large-scale homogenization in mammoth silos: calculating homogenization efficiency and modeling input properties
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Large-scale homogenization in mammoth silos: calculating homogenization efficiency and modeling input properties

机译：庞大的筒仓中的大规模均质化：计算均质化效率并建模输入属性

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摘要

The homogenization theory of mammoth silos is investigated in order to establish the homogenization efficiency of mammoth silos. These silos, where homogenization is achieved by intersecting multiple layers during reclaiming of the silo, e.g., by inclining the screw conveyor, can be used for large-scale homogenization of both cohesive and free-flowing materials and are therefore an alternative for blending piles. The presented homogenization model and the calculation of the homogenization efficiency in mammoth silos depend on two variables: the volume distribution and the input properties of the bulk material to be homogenized. The silo geometry and the chosen stacking and reclaiming method determine the first variable. The second variable, time series representing the input properties of a material flow, depends on the material to be homogenized. This paper focuses on modeling input properties and shows that higher order ARMA(p,q) models are required for describing these input properties, instead of the frequently assumed AR(1) models in literature. It does not concentrate on the comparison of predicted and simulated output variances. Conducted simulations of the homogenization efficiency with both ARMA and AR(1) models are found to be very encouraging because the standard deviation of the output properties is reduced on average by a factor 5, i.e., the standard deviation of the output properties is reduced to 20 percent of the standard deviation of the input properties.
机译：研究了大型仓仓的均质化理论，以建立大型仓仓的均质化效率。这些筒仓通过在筒仓回收期间相交多层来实现均质化，例如通过倾斜螺旋输送机来实现均质化，这些筒仓可用于均质粘性材料和自由流动材料的大规模均质化，因此是搅拌桩的替代方案。提出的均质化模型和猛sil仓的均质化效率的计算取决于两个变量：要均质的散装物料的体积分布和输入特性。料仓的几何形状和所选的堆积和回收方法确定了第一个变量。第二个变量是表示物料流输入特性的时间序列，取决于要均质的物料。本文着重于对输入属性进行建模，并显示了描述这些输入属性所需的高阶ARMA（p，q）模型，而不是文献中经常采用的AR（1）模型。它不专注于比较预测的输出方差和模拟的输出方差。发现使用ARMA和AR（1）模型进行均质化效率的模拟非常令人鼓舞，因为输出属性的标准偏差平均减少了5倍，即输出属性的标准偏差减小为输入属性的标准偏差的20％。

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