Citation

Ji P, Shi S. J. Saf. Sci. Resil. 2023; 4(2): 151-158.

Copyright

(Copyright © 2023, KeAi Communications, Publisher Elsevier Publishing)

DOI

10.1016/j.jnlssr.2022.12.001

PMID

unavailable

Abstract

Currently, coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results. Owing to this, an artificial immune algorithm (AIA) was developed for coal and gas outburst prediction based on the Hamming distance (HD) calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm (HDAIA). The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm. The HD algorithm was used to obtain the affinity between the antibody and antigen, and the minimum HD was screened to obtain the prediction result. The collected dynamic data of the drilling cuttings gas desorption index K1 and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province, China, were used as prediction indices. The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts, and it has a good prediction of the risk of coal and gas outbursts. The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.

Language: en

Keywords

Artificial immune algorithm; Coal and gas outburst; Hamming distance; Risk prediction