Fei Tang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Fei Tang has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
Yueping Qin
School of Emergency Management and Safety Engineering, China University of Mining and Technology, Beijing, China
2 shared publications- Dawei Zhong
School of Emergency Management and Safety Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
1 shared publication - Dongyang Han
School of Emergency Management and Safety Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
1 shared publication - Fengjie Zhang
School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China
1 shared publication Hairong Wang
School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing, People’s Republic of China
1 shared publication- Hao Xu
College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
1 shared publication
Latest Publications
- Open Access
- 1 January 2026
- Published by National Library of Serbia in Thermal Science
- Vol. 30 (2 Part B) , 1295-1303
Machine learning based prediction model and interpretable analysis of airflow temperature in water dropping shaft
Accurate prediction of the air-flow temperature in the mine water dropping shaft is crucial for scientifically guiding the prevention and control of thermal hazards in mines. To improve the precision, stability, and interpretability of the prediction model for the air-flow temperature in mine drainage shafts, combined with relevant literature research and the actual characteristics of mine air-flow temperature, Pearson correlation analysis was introduced to screen the characteristic variables. The parameters of the LSBoost model are optimized using the WOS algorithm, and a mine drainage shaft air-flow temperature prediction model based on WOS-LSBoost is established. Under the same sample set conditions, compare the prediction precision of the four models with the established DT model, RF model, LSBoost model, and SVM model, and conduct interpretability analysis on the prediction models based on the SHAP analysis method. Research has demonstrated that the WOS-LSBoost model has the best predictive performance, stronger generalization ability, and can effectively improve the precision of air-flow temperature prediction; the temperature of the wellhead air-flow has the largest effect on the prediction model, while the surface pressure has the smallest effect on the prediction model. This study provides scientific reference for the prevention and control of thermal hazards in underground mines.
- 1 January 2026
- Published by National Library of Serbia in Thermal Science
- Vol. 30 (2 Part B) , 1295-1303
Open AccessMachine learning based prediction model and interpretable analysis of airflow temperature in water dropping shaft
Accurate prediction of the air-flow temperature in the mine water dropping shaft is crucial for scientifically guiding the prevention and control of thermal hazards in mines. To improve the precision, stability, and interpretability of the prediction model for the air-flow temperature in mine drainage shafts, combined with relevant literature research and the actual characteristics of mine air-flow temperature, Pearson correlation analysis was introduced to screen the characteristic variables. The parameters of the LSBoost model are optimized using the WOS algorithm, and a mine drainage shaft air-flow temperature prediction model based on WOS-LSBoost is established. Under the same sample set conditions, compare the prediction precision of the four models with the established DT model, RF model, LSBoost model, and SVM model, and conduct interpretability analysis on the prediction models based on the SHAP analysis method. Research has demonstrated that the WOS-LSBoost model has the best predictive performance, stronger generalization ability, and can effectively improve the precision of air-flow temperature prediction; the temperature of the wellhead air-flow has the largest effect on the prediction model, while the surface pressure has the smallest effect on the prediction model. This study provides scientific reference for the prevention and control of thermal hazards in underground mines.
- 23 December 2025
- Published by Springer Nature in Journal of Thermal Analysis and Calorimetry
- Vol. 151 (1) , 125-141
Bibliometric review of mine thermal environment research based on knowledge graph analysis: evolution, trends, and future directions
- 23 December 2025
- Published by Springer Nature in Journal of Thermal Analysis and Calorimetry
- Vol. 151 (1) , 125-141
Bibliometric review of mine thermal environment research based on knowledge graph analysis: evolution, trends, and future directions
- Open Access
Optimization of the Monitoring of Coal Spontaneous Combustion Degree Using a Distributed Fiber Optic Temperature Measurement System: Field Application and Evaluation
Coal spontaneous combustion (CSC) in gob not only leads to wasted resources and casualties, but also produces a lot of waste gas that pollutes the underground environment. Mastering the degree of CSC helps ensure that timely and effective control measures are taken. The real-time and accurate monitoring of temperature, as the primary indicator of the extent of CSC, is difficult due to the harsh and hidden environment of gob, resulting in a reduced ability to anticipate and prevent CSC. In this work, a complete distributed optical fiber temperature sensing system (DTSS) set with strong anti-damage ability was developed. The optical cable is protected by internal parallel steel cables and external protective pipes, which greatly improve the system’s reliability and integrity when used in gob. During its application in the Wangyun Coal Mine, an abnormal temperature rise in gob was discovered in time, and the effect of inhibitor spraying was monitored and evaluated. The degree of CSC in the gob was accurately identified, which shows that the work of coal mining can be targeted. This work is expected to improve early warning capability to prevent the risk of CSC in gob, and has promising applications.
Open AccessOptimization of the Monitoring of Coal Spontaneous Combustion Degree Using a Distributed Fiber Optic Temperature Measurement System: Field Application and Evaluation
Coal spontaneous combustion (CSC) in gob not only leads to wasted resources and casualties, but also produces a lot of waste gas that pollutes the underground environment. Mastering the degree of CSC helps ensure that timely and effective control measures are taken. The real-time and accurate monitoring of temperature, as the primary indicator of the extent of CSC, is difficult due to the harsh and hidden environment of gob, resulting in a reduced ability to anticipate and prevent CSC. In this work, a complete distributed optical fiber temperature sensing system (DTSS) set with strong anti-damage ability was developed. The optical cable is protected by internal parallel steel cables and external protective pipes, which greatly improve the system’s reliability and integrity when used in gob. During its application in the Wangyun Coal Mine, an abnormal temperature rise in gob was discovered in time, and the effect of inhibitor spraying was monitored and evaluated. The degree of CSC in the gob was accurately identified, which shows that the work of coal mining can be targeted. This work is expected to improve early warning capability to prevent the risk of CSC in gob, and has promising applications.