基于多源信息融合的烧结混合料水分智能在线检测方法

Intelligent online detection method of sintering mixture moisture based on multi-source information fusion

烧结混合料水分的精准测控对改善料层透气性,提高烧结矿的产、质量具有重要作用。本文针对现有水分检测方法难以准确检测烧结混合料水分的问题,将智能方法引入水分检测,提出一种基于多源信息融合的烧结混合料水分智能在线检测方法。该方法首先采用高斯滤波进行图像预处理、利用以多类异质图像特征为输入的XGBoost构建烧结混合料水分检测子模型;再进一步通过四分位法对工艺数据进行预处理,利用以烧结过程变量为输入的GRU网络构建烧结混合料水分检测子模型;最后根据不同模态数据的特征将两个子模型进行融合。结果表明,该融合模型所得水分检测结果的稳定性较单一子模型有所提高,具有很好的应用价值。

The precise measurement and control of the sintering mixture moisture plays an important role in improving the permeability of the mixture and improving the production and quality of sinter.Aiming at the problem that the existing moisture detection methods are difficult to accurately detect the sintering mixture moisture, an intelligent method is introduced into the moisture detection of the sintering mixture, and an intelligent online detection method of the sintering mixture moisture based on the fusion of multi-source information is proposed.Firstly, Gaussian filtering is used for image pre-processing, and XGBoost with multi-class heterogeneous image features as input is used to build a sub-model for sintering mixture moisture detection;further, the process data is pre-processed by quadrature method, and GRU network with sintering process variables as input is used to build a sub-model for sintering mixture moisture detection;the two sub-models are fused according to the features of different modal data.The results show that the moisture detection results obtained from this fusion model are more stable than that of the single sub-model and have good application value.