| Issue |
E3S Web Conf.
Volume 385, 2023
2023 8th International Symposium on Energy Science and Chemical Engineering (ISESCE 2023)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 5 | |
| Section | Energy Development and Utilization and Energy Storage Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202338501012 | |
| Published online | 04 May 2023 | |
Prediction of gas emission in mining face based on GA-PSO-SVM
College of Safety Science and Engineering, Xi’an University of Science and Technology, 710054, China
* Correspondence E-mail: 15202318806@163.com
In order to prevent the gas from exceeding the limit and accurately and effectively predict the gas emission, this paper puts forward a prediction method of gas emission in mining face based on GA-PSO-SVM. The historical data of a coal mine is analyzed by comprehensively considering five factors that affect the gas emission from the working face. By predicting the gas emission from the test set, the values of MSE, MAE and RMSEP of GA-PSO-SVM model in the return gas concentration prediction are 0.029942, 0.001323 and 0.036378, respectively, and the three indexes are superior to the other three prediction models, indicating that the combined model is better than the single GA-SVM and PSO.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.