Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks
Embedded systems in production equipment and Internet of Things (IoT) sensors on production lines are one of the elements that constitute an industrial cyber-physical system. In this paper, an in-depth study and analysis of the optimization of blasting parameters and prediction of vibration effects...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823001485 |
| _version_ | 1797833128354512896 |
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| author | Runcai Bai Pengfei Zhang Zhiqiang Zhang Xue Sun Honglu Fei Shijie Bao Gang Hu Wenyan Li |
| author_facet | Runcai Bai Pengfei Zhang Zhiqiang Zhang Xue Sun Honglu Fei Shijie Bao Gang Hu Wenyan Li |
| author_sort | Runcai Bai |
| collection | DOAJ |
| description | Embedded systems in production equipment and Internet of Things (IoT) sensors on production lines are one of the elements that constitute an industrial cyber-physical system. In this paper, an in-depth study and analysis of the optimization of blasting parameters and prediction of vibration effects in open pit mines using deep neural network arithmetic are present. Based on the deep neural network research and analysis of the relationship between blasting parameters and rock fragmentation, a prediction model for blasting parameters and fragmentation for the East Open Pit Mine was established, and sensitivity analysis was performed on blasting parameters, and the unit consumption of explosives and the perforation rate were established. It was found that the average relative errors of both numerical simulation results and depth prediction results were no more than 10%, while the average relative errors of Sadowski's formula prediction results were more than 20%. The results show that the neural network optimized by a genetic algorithm and the numerical simulation has the highest accuracy in predicting the blasting result parameters. The research model and results obtained in this paper can be used as a reference guide for engineering practice. |
| first_indexed | 2024-04-09T14:19:40Z |
| format | Article |
| id | doaj.art-86faa5f53b8a4a57aa0572c6b517341e |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-09T14:19:40Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-86faa5f53b8a4a57aa0572c6b517341e2023-05-05T04:39:58ZengElsevierAlexandria Engineering Journal1110-01682023-05-0170261271Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networksRuncai Bai0Pengfei Zhang1Zhiqiang Zhang2Xue Sun3Honglu Fei4Shijie Bao5Gang Hu6Wenyan Li7School of Mining, Liaoning Technical University, Fuxin, Liaoning 123000, China; Institute of Technology and Equipment for the Development and Utilization of Mineral Resources, Liaoning Provincial College of Engineering, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaSchool of Mining, Liaoning Technical University, Fuxin, Liaoning 123000, China; School of Energy Engineering, Longdong University, Qingyang, Gansu 745000, China; Corresponding authors at: School of Mining, Liaoning Technical University, Fuxin, Liaoning 123000, China (P. Zhang).School of Science, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaSchool of Energy Engineering, Longdong University, Qingyang, Gansu 745000, China; Corresponding authors at: School of Mining, Liaoning Technical University, Fuxin, Liaoning 123000, China (P. Zhang).School of Science, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaSchool of Science, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaSchool of Science, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaSchool of Science, Liaoning Technical University, Fuxin, Liaoning 123000, ChinaEmbedded systems in production equipment and Internet of Things (IoT) sensors on production lines are one of the elements that constitute an industrial cyber-physical system. In this paper, an in-depth study and analysis of the optimization of blasting parameters and prediction of vibration effects in open pit mines using deep neural network arithmetic are present. Based on the deep neural network research and analysis of the relationship between blasting parameters and rock fragmentation, a prediction model for blasting parameters and fragmentation for the East Open Pit Mine was established, and sensitivity analysis was performed on blasting parameters, and the unit consumption of explosives and the perforation rate were established. It was found that the average relative errors of both numerical simulation results and depth prediction results were no more than 10%, while the average relative errors of Sadowski's formula prediction results were more than 20%. The results show that the neural network optimized by a genetic algorithm and the numerical simulation has the highest accuracy in predicting the blasting result parameters. The research model and results obtained in this paper can be used as a reference guide for engineering practice.http://www.sciencedirect.com/science/article/pii/S1110016823001485Deep neural networkOpen pit blastingParameter optimizationVibration effect prediction |
| spellingShingle | Runcai Bai Pengfei Zhang Zhiqiang Zhang Xue Sun Honglu Fei Shijie Bao Gang Hu Wenyan Li Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks Alexandria Engineering Journal Deep neural network Open pit blasting Parameter optimization Vibration effect prediction |
| title | Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| title_full | Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| title_fullStr | Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| title_full_unstemmed | Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| title_short | Optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| title_sort | optimization of blasting parameters and prediction of vibration effects in open pit mines based on deep neural networks |
| topic | Deep neural network Open pit blasting Parameter optimization Vibration effect prediction |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823001485 |
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