Optimization of blasting parameters in opencast mine with the help of firefly algorithm and deep neural network

作     者：Bisoyi, Sunil Kumar Pal, Bhatu Kumar 

作者机构：Natl Inst Technol Rourkela Dept Min Engn Rourkela 769008 India 

出 版 物：《SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES》 (Sadhana)

年 卷 期：2022年第47卷第3期

页      面：1-11页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：The authors would like to thank all the technical staff  including Mr. D. Pantawane and Dr. G. G. Manekar of Dongri Buzurg mine  who helped with the field investigation 

主　　题：Artificial neural networks Back-propagation algorithms Ground vibration Peak particle velocity Firefly algorithm Meta-heuristic algorithms 

摘      要：Blasting has been one of the most important contributors of mining since the start of mineral extraction and excavation. Along with fragmentation of the rocks, blasting also produces an excess of energy in the form of heat and vibration. Due to the spread of the vibration, the surrounding environment gets affected. Therefore, this paper aims to minimize the vibration to reduce the impact of ground vibration happening due to the mine blasting. In order to optimize the blasting parameters, a good predictor of such vibration is to be created. Hence, the paper compares a lot of predictors including empirical formulas and ANNs (Artificial Neural Networks). The best performing predictor has been used as the objective function for the optimization of parameters. Among the various optimization methods, the firefly algorithm proved to be a very good optimizer. Therefore, it was used to optimize the field parameters and implemented. The resulting optimized parameters showed a significant reduction in the ground vibration of 14.58%.