Intelligent prediction of clean coal ash content based on multi-source heterogeneous information fusion
CHENG Rongjie
CUI Teng
Abstract：To address the issue that X-ray detection alone is insufficient to fully capture the complex material changes during the flotation clean coal process,an intelligent flotation system based on multi-source heterogeneous information fusion,integrating X-ray detection and machine vision technologies,was designed.A prediction model for clean coal ash content was established using Federated Learning(FED)and Convolutional Neural Networks(CNN).Elemental analysis of flotation clean coal slurry was conducted using an X-ray ash analyzer.1D-CNN was applied to process the elemental content data to extract temporal features.Meanwhile,the ash content of flotation clean coal was detected using flotation froth vision technology,and 2D-CNN was used to process tailings image information to extract spatial features.An attention mechanism was adopted to fuse the temporal and spatial features derived from multi-source heterogeneous information,and through a fully connected layer to conduct regression prediction of ash content in flotation clean coal.The FED model effectively addressed privacy protection and collaborative modeling in multi-source heterogeneous information fusion through a modular aggregation method and a dynamic weighting strategy.Experimental results showed that the FED-CNN model achieved a maximum error of 4.44%and a coefficient of determination(R2)of 0.94.The prediction accuracy was higher than that of the 2D-CNN model based on tailings images and the 1D-CNN model based on X-ray data.
Keywords：intelligent flotationclean coal ash predictionmulti-source heterogeneous information fusionX-ray fluorescence spectroscopymachine visionfederated learningconvolutional neural network
Publication Date：2025-07-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 142-148 )