The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam,impurities,and changing lighting conditions that disrupt the collection of tailings ima...The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam,impurities,and changing lighting conditions that disrupt the collection of tailings images.To address this challenge,we present a method for ash content detection in coal slime flotation tailings.This method utilizes chromatographic filter paper sampling and a multi-scale residual network,which we refer to as MRCN.Initially,tailings are sampled using chromatographic filter paper to obtain static tailings images,effectively isolating interference factors at the flotation site.Subsequently,the MRCN,consisting of a multi-scale residual network,is employed to extract image features and compute ash content.Within the MRCN structure,tailings images undergo convolution operations through two parallel branches that utilize convolution kernels of different sizes,enabling the extraction of image features at various scales and capturing a more comprehensive representation of the ash content information.Furthermore,a channel attention mechanism is integrated to enhance the performance of the model.The combination of the multi-scale residual structure and the channel attention mechanism within MRCN results in robust capabilities for image feature extraction and ash content detection.Comparative experiments demonstrate that this proposed approach,based on chromatographic filter paper sampling and the multi-scale residual network,exhibits significantly superior performance in the detection of ash content in coal slime flotation tailings.展开更多
At present, coal is mainly consumed as fuel. In fact, coal is also a kind of precious raw material in chemical industry on the premise that some harmful minerals should be removed from coal. The paper presents the res...At present, coal is mainly consumed as fuel. In fact, coal is also a kind of precious raw material in chemical industry on the premise that some harmful minerals should be removed from coal. The paper presents the results of the research on producing low ash (<2%) coal with triboelectrostatic separator used for producing high-grade active carbon. The test is conducted in bench-scale system, whose capacity is 30～100 kg/h. The results indicate that: 1) the ash content of clean coal increases with the increase of solid content of feedstock, on the contrary, the yield of clean coal is declining; 2) a high velocity may result in a good separation efficiency; 3) for the same solid content, the reunion caused by intermolecular force makes the separation efficiency drop down when the ultra-fine coal is separated; 4) the separation efficiency is improved with the increase of electric field intensity, but there is a good optimized match between the electric field intensity and yield of clean coal; 5) a low rank coal is easy-to-wash in triboelectrostatic separation process; 6) the yield of clean coal can be enhanced and the ash decreased through adapting optimized conditions according to various coals.展开更多
基金This work was supported by National Natural Science Foundation of China:Grant No.62106048.
文摘The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam,impurities,and changing lighting conditions that disrupt the collection of tailings images.To address this challenge,we present a method for ash content detection in coal slime flotation tailings.This method utilizes chromatographic filter paper sampling and a multi-scale residual network,which we refer to as MRCN.Initially,tailings are sampled using chromatographic filter paper to obtain static tailings images,effectively isolating interference factors at the flotation site.Subsequently,the MRCN,consisting of a multi-scale residual network,is employed to extract image features and compute ash content.Within the MRCN structure,tailings images undergo convolution operations through two parallel branches that utilize convolution kernels of different sizes,enabling the extraction of image features at various scales and capturing a more comprehensive representation of the ash content information.Furthermore,a channel attention mechanism is integrated to enhance the performance of the model.The combination of the multi-scale residual structure and the channel attention mechanism within MRCN results in robust capabilities for image feature extraction and ash content detection.Comparative experiments demonstrate that this proposed approach,based on chromatographic filter paper sampling and the multi-scale residual network,exhibits significantly superior performance in the detection of ash content in coal slime flotation tailings.
基金National Development Programs of Major Basic Research Project(G19990 2 2 2 0 5 -0 3 )
文摘At present, coal is mainly consumed as fuel. In fact, coal is also a kind of precious raw material in chemical industry on the premise that some harmful minerals should be removed from coal. The paper presents the results of the research on producing low ash (<2%) coal with triboelectrostatic separator used for producing high-grade active carbon. The test is conducted in bench-scale system, whose capacity is 30～100 kg/h. The results indicate that: 1) the ash content of clean coal increases with the increase of solid content of feedstock, on the contrary, the yield of clean coal is declining; 2) a high velocity may result in a good separation efficiency; 3) for the same solid content, the reunion caused by intermolecular force makes the separation efficiency drop down when the ultra-fine coal is separated; 4) the separation efficiency is improved with the increase of electric field intensity, but there is a good optimized match between the electric field intensity and yield of clean coal; 5) a low rank coal is easy-to-wash in triboelectrostatic separation process; 6) the yield of clean coal can be enhanced and the ash decreased through adapting optimized conditions according to various coals.
