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A neural network based model of sinter quality and sinter plant performance indices

机译：基于神经网络的烧结矿质量和烧结厂性能指标模型

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摘要

A prerequisite of a smooth operation of the ironmaking blast furnace is that the quality of the burden is stable. In blast furnaces where sinter is used as the (main) iron bearing material, its quality plays a crucial role in productivity and fuel economy. Simultaneously the corresponding factors must be considered for the sinter plant. The present paper studies the influence of three variables characterising the bedding piles and five sinter plant operation variables on sinter quality, sinter plant productivity, specific fuel consumption and share of cold return fines. Daily mean values for a period of five years of operation were used in the data driven modelling based on feedforward neural networks. The resulting models were found to describe the major changes in the outputs well. The input-output relations captured by the models were analysed by perturbing one input variable of the networks at a time and analysing the predicted behaviour of the outputs.
机译：炼铁高炉平稳运行的先决条件是物料质量稳定。在将烧结矿用作（主要）含铁材料的高炉中，其质量对生产率和燃油经济性起着至关重要的作用。同时，烧结厂必须考虑相应的因素。本文研究了表征垫层桩的三个变量和五个烧结厂运行变量对烧结质量，烧结厂生产率，比燃料消耗和冷回收细粉份额的影响。在基于前馈神经网络的数据驱动建模中，使用了五年运行期间的每日平均值。发现结果模型很好地描述了产出的主要变化。通过一次扰动网络的一个输入变量并分析输出的预测行为，分析了模型捕获的输入-输出关系。

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