Prediction of Rock Fragmentation for Surface Mine Blasting Through Machine Learning Techniques

Raj, Anuj Kumar; Choudhary, Bhanwar Singh; Deressa, Geleta Warkisa

doi:10.1007/s40033-024-00812-7

J. Inst. Eng. India Ser. D

2024

DOI: 10.1007/s40033-024-00812-7

|View full text |Cite

Prediction of Rock Fragmentation for Surface Mine Blasting Through Machine Learning Techniques

Anuj Kumar Raj,

Bhanwar Singh Choudhary,

Geleta Warkisa Deressa

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Evaluating Productivity in Opencast Mines: A Machine Learning Analysis of Drill-Blast and Surface Miner Operations

Deressa,

Choudhary

2024

Nat Resour Res

View full text Add to dashboard Cite

Evaluating Productivity in Opencast Mines: A Machine Learning Analysis of Drill-Blast and Surface Miner Operations

Deressa,

Choudhary

2024

Nat Resour Res

View full text Add to dashboard Cite

Application of Blast-Pile Image Analysis in a Mine-to-Crusher Model to Minimize Overall Costs in a Large-Scale Open-Pit Mine in Brazil

Navarro Torres,

Ferreira,

de Carvalho

et al. 2024

Mining

View full text Add to dashboard Cite

Amazon rainforests have many hidden treasures; thus, a balance between mine activities and the environment must be maintained. In the northern region of Brazil, there is a large diversity of metal ore deposits, the exploitation of which requires innovative and sustainable mining operations. Historically, mining operations have caused various environmental issues, such as landscape deterioration, damage to natural structures due to detonations, and soil and water pollution, and have also contributed to CO2 emissions from diesel trucks. Here, to estimate and minimize the operating expenses of a large-scale open-pit iron mine, a mine-to-crusher model was developed. The calibration of the mine-to-crusher model was based on rock fragmentation from the blasting phase through the primary crushing phase from an analysis of pictures of the fragmented pile. A reduction in cost was determined for an optimum 90% passing size (P90). The calibration was performed with technical and economic parameters from 2 years before. For the studied iron ore mine site, an optimum P90 value between 0.29 and 0.31 m was determined.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Prediction of Rock Fragmentation for Surface Mine Blasting Through Machine Learning Techniques

Cited by 2 publications

References 46 publications

Evaluating Productivity in Opencast Mines: A Machine Learning Analysis of Drill-Blast and Surface Miner Operations

Evaluating Productivity in Opencast Mines: A Machine Learning Analysis of Drill-Blast and Surface Miner Operations

Application of Blast-Pile Image Analysis in a Mine-to-Crusher Model to Minimize Overall Costs in a Large-Scale Open-Pit Mine in Brazil

Contact Info

Product

Resources

About