Sakhre, Devendra Kumar and Sahoo, Lalit Kumar (2025) Development of Coal Quality Exploration Technique based on Convolutional Neural Network and Hyperspectral Imaging. International Journal of Oil, Gas and Coal Technology. pp. 2003-2036.
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Coal is India's prime energy source, contributing about 60% of total electricity production. Coal India, a major coal-producing public sector unit, has produced record 703.2 million tons of coal during the year 2022–2023. Therefore, this paper proposes an idea of instant prediction of coal quality parameters using hyperspectral imaging and deep neural network. We have collected coal samples from 35 different coal mines of all areas of Western Coalfields Ltd (WCL), and 257 different types of samples have been generated. All 257 coal samples were imaged using camera PIKA NIR 320. The RegNet model was applied to predict coal quality based on moisture, ash, volatile matter, gross calorific value, fixed carbon, and sulphur. The results were validated through chemical analysis results received from the lab. The proposed approach achieved good prediction accuracy, nearly 96% for coal quality parameters. Moisture showed the highest accuracy, 96.09% in quality prediction. [Received: October 25, 2023; Accepted: April 14, 2024]
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | coal quality parameters, hyperspectral imaging, HSI, deep learning, spectral data, spatial data |
| Subjects: | Fuel Scinece |
| Divisions: | UNSPECIFIED |
| Depositing User: | Mr. B. R. Panduranga |
| Date Deposited: | 29 Apr 2025 10:01 |
| Last Modified: | 29 Apr 2025 10:06 |
| URI: | http://cimfr.csircentral.net/id/eprint/2784 |
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