基于神经网络的烧结矿综合性能预测

Predictive Model of the Sinter Comprehensive Performance Base on Neural Network

烧结矿的质量和成分稳定都将直接影响到炼铁生产的产量、质量及能源消耗。稳定控制烧结矿化学成分和科学分析烧结能耗对降低炼铁成本、节能减排具有重要意义。应用MATLAB的m文件编辑器直接编写代码,基于BP神经网络,建立了烧结矿两种重要化学成分(TFe和FeO)、烧结成品率和烧结固体燃耗的预测系统。利用取自现场的生产数据对预测模型进行了训练。现场应用结果表明,预测系统准确率高,稳定可靠,进一步提高了烧结生产率,降低了生产成本。

The sinter quality and the stability of composition could directly affect the yield,quality and energy con-sumption of ironmaking production. It is important for iron and steel industry to steadily control sinter chemical com-position and analyze sintering energy consumption. The MATLAB m file editor was used to write code directly in this paper. A predictive system for two important sinter chemical composition（TFe and FeO）,sinter output and sintering solid fuel consumption of was established based on BP neural network,which was trained by actual production da-ta. ）The application results show that the prediction system has high accuracy rate,stability and reliability,the sin-tering productivity was improved effectively.