Affiliation:
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology Beijing, Beijing 100083, China
2. State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, China
3. Inner Mongolia Research Institute, China University of Mining and Technology Beijing, Ordos 017004, China
Abstract
Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450–2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with Rp2 values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal.
Funder
National Natural Science Foundation of China Science Foundation Project
Open Research Fund of The State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, CUMT
China University of Mining and Technology (Beijing) LongRuan Technology Fund Student Innovation & Enterprise Program
Doctoral Innovative Talents Cultivation Project at China University of Mining and Technology
Reference71 articles.
1. IEA (2024, August 14). Coal. Available online: https://www.iea.org/reports/coal-2023.
2. Literature overview of Chinese research in the field of better coal utilization;Li;J. Clean. Prod.,2018
3. Understanding coal quality and the critical importance of comprehensive coal analyses;Hower;Int. J. Coal Geol.,2022
4. Kiang, Y.-H. (2018). Fuel Property Estimation and Combustion Process Characterization: Conventional Fuels, Biomass, Biocarbon, Waste Fuels, Refuse Derived Fuel, and Other Alternative Fuels, Academic Press.
5. (2013). Solid Mineral Fuels—Hard Coal—Determination of Moisture in the General Analysis Test Sample by Drying in Nitrogen (Standard No. ISO11722).