Prediction of Calorific Value for Coal Gangue Based on the Machine Learning Algorithm

DOI

10.2991/978-94-6463-415-0_5How to use a DOI?

Keywords

Coal Gangue; Calorific Value Prediction; Support Vector Regression; Particle Swarm Optimization; Machine Learning Algorithm

Abstract

In order to solve the problems of inconvenience and high cost of coal gangue calorific value measurement, the machine learning algorithm is adopted and applied to the prediction of practical engineering. Firstly, the industrial analysis components and calorific value of coal gangue in Xinjiang are obtained by experiment following the Standard GB/T213-2008 and GB/T212-2008 in China. Then, the experimental methods and experimental data used are analyzed to establish a data set of coal gangue. Secondly, a nonlinear prediction model of coal gangue calorific value combined with the particle swarm optimization algorithm is proposed. The prediction model is trained and predicted based on the experimental data. Finally, the accuracy and reliability of the above model are verified by comparing the deviation between actual measurement values and prediction values. The research results show that calorific value prediction model for coal gangue based on PSO-SVR proposed in this study has high prediction accuracy and convergence speed, which can provide scientific basis for energy utilization of coal gangue.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiangbin Gao
PY  - 2024
DA  - 2024/05/14
TI  - Prediction of Calorific Value for Coal Gangue Based on the Machine Learning Algorithm
BT  - Proceedings of the 2023 9th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2023)
PB  - Atlantis Press
SP  - 28
EP  - 41
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-415-0_5
DO  - 10.2991/978-94-6463-415-0_5
ID  - Gao2024
ER  -