Open Access

ARTICLE

Ash Detection of Coal Slime Flotation Tailings Based on Chromatographic Filter Paper Sampling and Multi-Scale Residual Network

Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹

1 School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528000, China
2 China Coal Technology Engineering Group Tangshan Research Institute, Tangshan, 063000, China

* Corresponding Author: Zhengjun Zhu. Email:

Intelligent Automation & Soft Computing 2023, 38(3), 259-273. https://doi.org/10.32604/iasc.2023.041860

Received 09 May 2023; Accepted 09 November 2023; Issue published 27 February 2024

Abstract

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.

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Keywords

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