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
The use of wash oil as a coal collector is proposed to overcome the disadvantages of regular collectors in coal slime flotation. These disadvantages include high price, limited sources and high consumption. The effect...The use of wash oil as a coal collector is proposed to overcome the disadvantages of regular collectors in coal slime flotation. These disadvantages include high price, limited sources and high consumption. The effect of additives on flotation was studied and an innovative ＂one rough separation--one cleaning separation＂ flotation technology was developed. The experimental resuits show that the clean coal ash content decreases by about 1.36% and the clean coal yield declines by around 10% with the application of the depressant. There is an increase of 3.76% in the yield of clean coal and a decrease of 0.40% in the ash content caused by utilizing a dispersant. An ultimate product having an ash content of 10.78% and yield of 70.12% can be attained using a combination of dispersant and depressant. The use of this new technology decreases the ash content by 1.21%, decreases the yield by 2.80% and an increases the coal flotation perfect index by 2.03%. Compared to common flotation, the utilization of the new technology reduces ash by 0.17%, increases yield by 5.3% and increases perfect index by 4.18%.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Financial support for this research,provided by the National Natural Science Foundation of China (No. 50921002)
文摘The use of wash oil as a coal collector is proposed to overcome the disadvantages of regular collectors in coal slime flotation. These disadvantages include high price, limited sources and high consumption. The effect of additives on flotation was studied and an innovative ＂one rough separation--one cleaning separation＂ flotation technology was developed. The experimental resuits show that the clean coal ash content decreases by about 1.36% and the clean coal yield declines by around 10% with the application of the depressant. There is an increase of 3.76% in the yield of clean coal and a decrease of 0.40% in the ash content caused by utilizing a dispersant. An ultimate product having an ash content of 10.78% and yield of 70.12% can be attained using a combination of dispersant and depressant. The use of this new technology decreases the ash content by 1.21%, decreases the yield by 2.80% and an increases the coal flotation perfect index by 2.03%. Compared to common flotation, the utilization of the new technology reduces ash by 0.17%, increases yield by 5.3% and increases perfect index by 4.18%.
