Quantitative analysis of coal industrial index based on data set partitioning method

You-quan Dou; Qing-song Wang; Sen Wang; Xi Shu; Ming-hui Ni; Li-Xiao Shen; Yan Li

doi:10.1117/12.2692262

18 December 2023 Quantitative analysis of coal industrial index based on data set partitioning method

You-quan Dou, Qing-song Wang, Sen Wang, Xi Shu, Ming-hui Ni, Li-Xiao Shen, Yan Li

Proceedings Volume 12959, AOPC 2023: Laser Technology and Applications; and Optoelectronic Devices and Integration; 1295904 (2023) https://doi.org/10.1117/12.2692262
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

In order to further improve the accuracy of quantitative analysis of coal quality by laser induced breakdown spectroscopy (LIBS), the influence of data set partitioning method on quantitative model was studied. The spectral data of 40 different coal samples were collected, and the Support Vector Regression (SVR) model and random forest (RF) model were established by Random Selection (RS), Kennard-Stone (KS) and Sample Partitioning based on joint X-Y distances (SPXY), respectively. The prediction results of ash, volatile matter and calorific value under the two models were compared. The results show that the regression model established by SPXY method combined with RF algorithm has better fitting prediction performance. The predicted root mean square errors (RMSEP) of ash, volatile matter and calorific value are 1.8872, 1.4537 and 0.9020, respectively, and the mean relative errors (MRE) are 6.96%, 3.87% and 2.14%, respectively.

Citation Download Citation

You-quan Dou, Qing-song Wang, Sen Wang, Xi Shu, Ming-hui Ni, Li-Xiao Shen, and Yan Li "Quantitative analysis of coal industrial index based on data set partitioning method", Proc. SPIE 12959, AOPC 2023: Laser Technology and Applications; and Optoelectronic Devices and Integration, 1295904 (18 December 2023); https://doi.org/10.1117/12.2692262

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available