Prediction of Coal Volatile Matter Content Based on Terahertz Spectroscopy and Random Forest Algorithm
Journal Title: Chinese Journal of Inorganic Analytical Chemistry - Year 2025, Vol 15, Issue 6
Abstract
This study investigate the prediction of volatile content in coal samples using the Random Forest (RF) algorithm，based on Terahertz time-domain spectroscopy (THz-TDS) data from 257 coal samples.The volatile content of each sample was experimentally determined.To optimize feature extraction and reduce data dimensionality，Local Max detection and Principal Component Analysis (PCA) were employed to identify key peaks and transform the spectral data. Three distinct Random Forest regression models were developed and compared：the RF-LM model，which relie solely on Local Max features；the RF-PCA model，which use only PCA features；and the RF-LM+PCA model，which integrate both feature sets. The RF-LM+PCA model exhibited the highest prediction accuracy，achieving a coefficient of determination (R²) of 0.989，a root mean square error (RMSE) of 1.493，and a mean absolute error (MAE) of 0.902. This research introduces a novel approach that combines terahertz spectroscopy with machine learning techniques for coal quality analysis.It provides an innovative and practical strategy for the intelligent coal mining sector，potentially advancing both technology and application in this field.
Authors and Affiliations
Limin WANG, Lijiang ZHU, Jin'ge LIU
Keywords
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- EP ID EP767900
- DOI 10.20236/j.CJIAC.2025.06.014
- Views 8
- Downloads 0
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