LIU X, CHENG M G, WEI S R, et al. A rapid determination method for gas drainage radius based on FI screening and DNN［J］.Journal of Henan Polytechnic University（Natural Science），doi: 10.16186/j.cnki.1673-9787. 2026030019

doi:10.16186/j.cnki.1673-9787.2026030019

Received：2026-03-16

Revised：2026-08-27

Online：2026-09-01

A rapid determination method for gas drainage radius based on FI screening and DNN

Liu Xiao¹, Cheng Mingguo¹, Wei Shaoren¹, Yang Chengtao², Ni Xiaoming¹, Zhang Yubo¹, Wang Yonglong¹, Li Likang¹, Han Lei¹, Li Bo¹

(1.School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, Henan, China; 2.Research Institute of Henan Energy and Chemical Industry Group, Zhengzhou 450046, Henan, China.)

Abstract: [Objectives] The gas extraction radius was considered a core parameter for mine safety and efficient gas extraction. It was influenced by multiple factors. Under current standards, direct measurement methods, such as pressure method and flow method, were used. These methods involved large underground drilling work, long testing periods, and data prone to human influence. [Methods] A rapid determination method of the gas extraction radius was proposed based on feature importance and deep neural network (DNN). The gas extraction radius was set as the target value. Feature importance (FI) was calculated using a random forest algorithm to evaluate the weights of influencing factors. The main controlling factors were selected as features. Historical extraction radius data were combined with COMSOL simulation data to build a neural network training dataset. Features were used as input samples, and the target value was used as output. [Results] A calculation model of the gas extraction radius was established through DNN learning, iteration, and optimization. Corresponding visualization software was developed. The model was validated using field test data. Compared with linear regression and support vector regression models, the DNN model reduced the mean absolute error by 34.7% and 48.3%, and the root mean square error by 22.4% and 26.8%, respectively. The coefficient of determination reached 0.988. [Conclusions] Five factors, including original gas pressure, coal seam thickness, permeability, extraction time, and borehole radius, were identified as main controlling factors. The proposed rapid determination method, based on this model, demonstrated high efficiency and convenience. A new technical approach was provided for quick and accurate determination of the gas extraction radius.

Key words: gas extraction radius; influencing factors; feature importance; neural network; numerical simulation

CLC: