基于灰色关联分析的烧结矿碱度应用研究

在现代钢铁企业中,高炉原料的烧结过程是其中重要的生产工序.烧结矿碱度稳定性直接影响到烧结矿的质量和产量,但由于烧结生产过程非常复杂,很难用一组较为准确的数学模型进行描述.加之过程所具有的大时滞性和动态时变性,采取一些依赖于对象数学模型的传统控制理论和方法难以解决烧结矿碱度的波动问题.因此长期以来,烧结矿碱度的控制一直是钢铁企业中的一个难点.据此提出利用灰色关联分析和BP神经网络建立烧结矿碱度的预报模型.通过对现场实际数据进行仿真,表明该方法鲁棒性强,准确性高,泛化能力广,具有很强的实用性和推广价值.

Application Research on the Alkalinity of Sintering Process Based on Grey Relation Analysis

In the modern steel enterprises,the sintering process of blast furnace material is one of the best important production process.The sintering production alkalinity has a direct effect on production and economic benefits of whole steel enterprise.Therefore almost every steel factory is equipped with many instruments and automatic control systems in its sintering plant for its producton process control.But the complexity of sintering production process makes difficult to be described by a set of mathematic models. Since this process often has large time delay and dynamic time -varilabilityit, is hard to perform control tasks of total sintering process by using conventional control models.Prediction models of in sintering process based on grey relation analysis and BP neural network is proposed to judge the trend of the alkalinity.The application result shows that the prediction with this method can achieve higher robust, better utility and expensive value.