PDF
Full
Outline
The rapid and comprehensive determination of coal quality is of great significance for the optimization of boiler combustion and the digital transformation of coal-fired power plants. Laser-induced breakdown spectroscopy (LIBS) has the potential to be applied effectively in the rapid determination of coal quality. In order to meet the application goal of rapid coal inspection, 46 sets of spectral data of coal samples from different power plants were collected by the experimental device of coal particle flow LIBS, and the research of simultaneous rapid inspection of multiple indicators of coal quality by combining LIBS with machine learning was carried out systematically. In view of the considerable spectral fluctuations observed in the particle flow state, the number of single-pulse acquisitions was optimized. In addition, invalid spectral screening, spectral averaging and spectral normalization data preprocessing methods were established. Furthermore, four machine learning algorithms (PLSR, SVR, PSO-SVR, and LSTM) and four spectral feature inputs (full spectra, eigenbands, intensity integration, and PCA extraction) were compared in terms of their performance in predicting multiple indicators of coal quality. The results demonstrate that the uncertainty of the spectral signals can be maintained at a maximum of 5% when 200 single-pulse spectra are collected for spectral averaging in a single test. The PSO-SVR algorithm exhibits the most optimal prediction performance in the quantitative analysis of coal quality indicators, and the PCA algorithm reduces the dimensionality of the spectral data, which reduces the amount of model computation and at the same time improves the prediction performance of the model, and the model established by combining both of them has the best performance, the root mean square error (RMSEP) of the coal heat content is 0.289 MJ/kg, and the mean absolute error (MAE) is 0.231 MJ/kg. The coal carbon mass fraction, ash content and volatile matter content are also predicted satisfactorily, with the RMSEP of 0.987%, 1.310% and 1.612%, and the MAE of 0.839%, 1.014%, and 1.033%, respectively. The results show that, combined with appropriate machine learning algorithms, the LIBS technique can achieve simultaneous accurate and rapid determination of multiple indicators of coal quality, which has a broad application prospect in the scenario of efficient and clean coal utilization.
PDF
147
66
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
