Multi-source heterogeneous data fusion and real-time sensing technology for sintering industry
Iron ore sintering is a key preliminary process in blast furnace ironmaking.Online real-time intelligent sensing of key production indicators in the sintering process is one of the key technol-ogies to achieve green,low-consumption,and efficient development of the sintering process.However,on one hand,traditional methods for measuring sintering production indicators(such as FeO content)are time-consuming and difficult to meet the needs of real-time control.On the other hand,the sinte-ring process data has nonlinearity,multi-source heterogeneity,and time lag,which poses a great chal-lenge to improving modeling accuracy.To this end,this paper proposes a multi-source heterogeneous data fusion and real-time sensing technology for the sintering industry.This study uses a multi-source heterogeneous information fusion method to extract shallow and deep features respectively through ex-pert knowledge and the FcaNet model based on discrete cosine transform(DCT)for the cross-sec-tional image data of the sintering machine collected by the infrared thermal imaging camera,achieving feature level and data level fusion.In the prediction task,the two-dimensional convolution block is applied to the time series data,and FcaBlock is used as the convolution block for feature extraction,which effectively extracts the frequency component information of the time series data.On the iron ore sintering data set of a real steelmaking plant,the prediction accuracy and stability of this model are superior to existing models,and it significantly improves the online real-time sensing of key quality indicators of the sintering process.
sintering processintelligent sensingmulti-source heterogeneous information fusionma-chine visiontime series prediction
NETL
NSTL
万方数据
