Macerals particle characteristics analysis of tar-rich coal in northern Shaanxi based on image segmentation models via the U-Net variants and image feature extraction. (1st June 2023)
- Record Type:
- Journal Article
- Title:
- Macerals particle characteristics analysis of tar-rich coal in northern Shaanxi based on image segmentation models via the U-Net variants and image feature extraction. (1st June 2023)
- Main Title:
- Macerals particle characteristics analysis of tar-rich coal in northern Shaanxi based on image segmentation models via the U-Net variants and image feature extraction
- Authors:
- Fan, Jinwen
Du, Meili
Liu, Lei
Li, Gang
Wang, Dechao
Liu, Shuo - Abstract:
- Graphic abstract: Highlights: Applying U-Net variants to the identification of macerals and liberation. Compare and analyze the similarities and differences between the PASM and GCPM. New method (the pixel area statistics method) to guide macerals density separation. Abstract: To investigate the particle characteristics of tar-rich coal macerals before separation, this study focuses on the tar-rich coal in northern Shaanxi, China, as the research object. The image segmentation models of U-Net variants (Mobile-Unet, VGG-Unet, Res-Unet, and TransUNet) are combined with OpenCV feature extraction to systematically study the particle morphology, particle size, liberation characteristics, and density separation process of coal macerals. The results indicate that the morphology of vitrinite tends to an olive-like shape as the particle size decreases, whereas the morphology of inertinite is complex and changeable. Further, the particle sizes of raw coal, vitrinite, and inertinite range from 0.09 mm to 0.075 mm, and their morphology uniformity is the best in this range. The statistical difference between the grid calculation point method (GCPM) and the pixel area statistics method (PASM) is mainly due to the particle size uniformity. When the particle size is less than 0.03 mm, the error is less than 3.0 %. By narrowing the range of the liberation densities and separating them preferentially, samples within the range of the unliberated densities are returned to the regrinding processGraphic abstract: Highlights: Applying U-Net variants to the identification of macerals and liberation. Compare and analyze the similarities and differences between the PASM and GCPM. New method (the pixel area statistics method) to guide macerals density separation. Abstract: To investigate the particle characteristics of tar-rich coal macerals before separation, this study focuses on the tar-rich coal in northern Shaanxi, China, as the research object. The image segmentation models of U-Net variants (Mobile-Unet, VGG-Unet, Res-Unet, and TransUNet) are combined with OpenCV feature extraction to systematically study the particle morphology, particle size, liberation characteristics, and density separation process of coal macerals. The results indicate that the morphology of vitrinite tends to an olive-like shape as the particle size decreases, whereas the morphology of inertinite is complex and changeable. Further, the particle sizes of raw coal, vitrinite, and inertinite range from 0.09 mm to 0.075 mm, and their morphology uniformity is the best in this range. The statistical difference between the grid calculation point method (GCPM) and the pixel area statistics method (PASM) is mainly due to the particle size uniformity. When the particle size is less than 0.03 mm, the error is less than 3.0 %. By narrowing the range of the liberation densities and separating them preferentially, samples within the range of the unliberated densities are returned to the regrinding process to separate and enrich the vitrinite and inertinite groups in coal, which effectively improves their purity and recovery. In summary, Res-UNet, TransUNet, and the PASM not only have considerable potential for the qualitative analysis of maceral compositions and their liberation types but also provide powerful computer-assisted measurement to achieve reasonable separation and utilization of coal macerals. … (more)
- Is Part Of:
- Fuel. Volume 341(2023)
- Journal:
- Fuel
- Issue:
- Volume 341(2023)
- Issue Display:
- Volume 341, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 341
- Issue:
- 2023
- Issue Sort Value:
- 2023-0341-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-06-01
- Subjects:
- Macerals particle characteristics -- Image segmentation -- U-Net variants -- Image feature extraction
Fuel -- Periodicals
Coal -- Periodicals
Coal
Fuel
Periodicals
662.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/latest/00162361 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.fuel.2023.127757 ↗
- Languages:
- English
- ISSNs:
- 0016-2361
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4048.000000
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