TY  - EJOU
AU  - Zhu, Wenbo 
AU  - Liu, Neng 
AU  - Zhu, Zhengjun 
AU  - Li, Haibing 
AU  - Fu, Weijie 
AU  - Zhang, Zhongbo 
AU  - Zhang, Xinghao 

TI  - Ash Detection of Coal Slime Flotation Tailings Based on Chromatographic Filter Paper Sampling and Multi-Scale Residual Network
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 38
IS  - 3
SN  - 2326-005X

AB  - 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.
KW  - Coal slime flotation; ash detection; chromatography filter paper; multi-scale residual network

DO  - 10.32604/iasc.2023.041860