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Hierarchical Prediction Model Based on BP Neural Network for Predicting CO/CO2in Iron Ore Sintering Process

机译：基于BP神经网络的分层预测模型在铁矿石烧结过程中预测CO / CO 2

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摘要

The sintering process is one of the most energy-consuming processes in steelmaking, its carbon fuel consumption accounts for 8% to 10% in the steel production process. To find ways of reducing the energy consumption, it is necessary to predict the carbon efficiency. The value of CO / CO2in the carbon emission can reflect the utilization of carbon combustion in sintering process. In this study, the CO / CO2is taken to be a measure of carbon efficiency and a hierarchical model is built to predict it. Firstly, the physical and chemical reactions and the carbon flow mechanism in the sintering process are analyzed, and the process parameters that affect the CO/CO2are determined. Then, the gray relational analysis method is used to analyze the influence factors to determine the relationship between the parameters, and a hierarchical predictive model for CO/CO2is established based on the relationship between the parameters. The hierarchical predictive model is divided into two parts: the predictive models for the thermal state parameters and the predictive model for CO / CO2. The inputs of the predictive models for the thermal state parameters are the raw material parameters and the operating parameters, and the inputs of the predictive model for CO / CO2are the predicted values of the predictive models for the thermal state parameters. Finally, the simulation results verify the effectiveness of the proposed modeling method. This method can provide a theoretical basis for the optimization and control of carbon efficiency in the sintering process.
机译：烧结过程是炼钢过程中能耗最高的过程之一，其碳燃料消耗在钢铁生产过程中占8 \％至10 \％。为了找到减少能耗的方法，有必要预测碳效率。 CO / CO的值 2 碳排放量的增加可以反映烧结过程中碳燃烧的利用。在本研究中，CO / CO 2 被视为衡量碳效率的指标，并建立了一个层次模型对其进行预测。首先，分析了烧结过程中的理化反应和碳流动机理，并研究了影响CO / CO的工艺参数。 2 确定。然后，采用灰色关联分析法对影响因素进行分析，确定各参数之间的关系，并建立了CO / CO分层预测模型。 2 基于参数之间的关系建立。分层预测模型分为两部分：热状态参数的预测模型和CO / CO的预测模型 2 。热状态参数的预测模型的输入是原材料参数和操作参数，以及CO / CO的预测模型的输入 2 是热状态参数的预测模型的预测值。最后，仿真结果验证了所提建模方法的有效性。该方法可为烧结过程中碳效率的优化和控制提供理论依据。

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