Gas Emission Rate Prediction in Fully-Mechanized Excavated Faces Based on Support Vector Machine

Author

Wang Changlong ; Weihua, Fu

Author_Institution

Sch. of Civil Eng., Hebei Univ. of Eng., Handan, China

fYear

2009

fDate

21-22 Nov. 2009

Firstpage

440

Lastpage

443

Abstract

In order to ensure safety in coal production, full assurance is given for fully-mechanized excavated faces. Based on the vector supporting machine for regression (SVR), a model is established for predicting the gas emission in fully-mechanized excavated faces. The index system is analyzed and the model parameters are chosen. Then, the sample set of gas emission in fully-mechanized coal driving workface is use to realize the model by programming based on Matlab. The predictions turn out that the precise is much larger than 90%, which satisfies the demands of design and production of coal mines and provides a reliable foundation for mine ventilation and gas management in fully-mechanized excavated faces.

Keywords

accident prevention; coal; learning (artificial intelligence); mining industry; regression analysis; support vector machines; Matlab; coal driving workface; coal production; fully-mechanized excavated faces; gas emission rate prediction; gas management; mine ventilation; support vector machine; support vector regression; Civil engineering; Information technology; Machine intelligence; Management training; Mathematical model; Predictive models; Production; Statistical learning; Support vector machine classification; Support vector machines; SVM; Tracking; emission rate; fully-mechanized excavated faces; gas prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology Application Workshops, 2009. IITAW '09. Third International Symposium on

Conference_Location

Nanchang

Print_ISBN

978-1-4244-6420-3

Electronic_ISBN

978-1-4244-6421-0

Type

conf

DOI

10.1109/IITAW.2009.60

Filename

5419589