Research on the prediction model of gas emission based on grey system theory
Abstract Accurate prediction of the gas emission volume in the mining face can prevent gas accidents in advance, optimize the mining plan, reduce energy consumption, and contribute to the safe, efficient and green development of coal mines. In order to improve the prediction accuracy of gas emission...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09163-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849768705873936384 |
|---|---|
| author | Liyang Bai Hui Geng Guangming Yu |
| author_facet | Liyang Bai Hui Geng Guangming Yu |
| author_sort | Liyang Bai |
| collection | DOAJ |
| description | Abstract Accurate prediction of the gas emission volume in the mining face can prevent gas accidents in advance, optimize the mining plan, reduce energy consumption, and contribute to the safe, efficient and green development of coal mines. In order to improve the prediction accuracy of gas emission volume.A prediction model of gas emission based on grey system theory is proposed.11 indexes such as gas content, coal seam depth, coal seam thickness, coal seam dip angle and inclined length of working face are selected as the influencing factors of gas emission.The weight of each factor is determined by grey correlation analysis. The related factors with a grey correlation degree greater than 0.7, from largest to smallest, are: coal seam gas content X1 > coal seam thickness X3 > mining intensity X11 > coal seam depth X2 > adjacent gas content X8.Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission. |
| format | Article |
| id | doaj-art-032a0a84b9304cec8638f3a7e8dd1a14 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-032a0a84b9304cec8638f3a7e8dd1a142025-08-20T03:03:42ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-09163-zResearch on the prediction model of gas emission based on grey system theoryLiyang Bai0Hui Geng1Guangming Yu2School of Civil Engineering, Qingdao University of TechnologyCollege of Civil Engineering and Architecture, Xinjiang UniversitySchool of Civil Engineering, Qingdao University of TechnologyAbstract Accurate prediction of the gas emission volume in the mining face can prevent gas accidents in advance, optimize the mining plan, reduce energy consumption, and contribute to the safe, efficient and green development of coal mines. In order to improve the prediction accuracy of gas emission volume.A prediction model of gas emission based on grey system theory is proposed.11 indexes such as gas content, coal seam depth, coal seam thickness, coal seam dip angle and inclined length of working face are selected as the influencing factors of gas emission.The weight of each factor is determined by grey correlation analysis. The related factors with a grey correlation degree greater than 0.7, from largest to smallest, are: coal seam gas content X1 > coal seam thickness X3 > mining intensity X11 > coal seam depth X2 > adjacent gas content X8.Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission.https://doi.org/10.1038/s41598-025-09163-zGrey theoryGM(0,N) modelGrey correlation degreeGas emission quantityPredictionMultiple linear regression |
| spellingShingle | Liyang Bai Hui Geng Guangming Yu Research on the prediction model of gas emission based on grey system theory Scientific Reports Grey theory GM(0,N) model Grey correlation degree Gas emission quantity Prediction Multiple linear regression |
| title | Research on the prediction model of gas emission based on grey system theory |
| title_full | Research on the prediction model of gas emission based on grey system theory |
| title_fullStr | Research on the prediction model of gas emission based on grey system theory |
| title_full_unstemmed | Research on the prediction model of gas emission based on grey system theory |
| title_short | Research on the prediction model of gas emission based on grey system theory |
| title_sort | research on the prediction model of gas emission based on grey system theory |
| topic | Grey theory GM(0,N) model Grey correlation degree Gas emission quantity Prediction Multiple linear regression |
| url | https://doi.org/10.1038/s41598-025-09163-z |
| work_keys_str_mv | AT liyangbai researchonthepredictionmodelofgasemissionbasedongreysystemtheory AT huigeng researchonthepredictionmodelofgasemissionbasedongreysystemtheory AT guangmingyu researchonthepredictionmodelofgasemissionbasedongreysystemtheory |