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Advanced Analytics for Drilling and Blasting

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Advanced Analytics in Mining Engineering

Abstract

The mining operation is associated with uncertainties that are not limited to ore characteristics and market demand; operational costs, operational performance, regulations are all a part of these uncertainties. Managing these uncertainties effectively and consistently through the mine life helps miners to maintain sustainability in their operations. Drilling and blasting as a key activity in the mining cycle have a key role in operational risk management. Many empirical methods have been developed to determine blast design parameters or the prediction of blast outcomes. However, these methods cannot cover all associated parameters and factors in design or prediction. Applications of advanced analytical methods, including AI-capable engineers, develop powerful tools that consider multiple parameters to predict blasting results or determine design parameters to achieve desired results. This chapter shows how analytical models can optimize blast patterns according to the real bench geometry and discusses advanced analytical solutions for predicting blast fragmentation and vibration by considering affecting parameters on the blast outcomes. In addition, it presents developed models based on recorded data in a mine site that can be used to determine blast design parameters to achieve desired blast outcomes. Discussed models in this chapter are based on AI and machine learning.

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Authors and Affiliations

  1. Business Development Manager at GKM Consultants, Toronto, Canada

    Ali Siamaki

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  1. Ali Siamaki
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Correspondence to Ali Siamaki .

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  1. Artificial Intelligence Center, Vale, WA, Australia

    Ali Soofastaei

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Siamaki, A. (2022). Advanced Analytics for Drilling and Blasting. In: Soofastaei, A. (eds) Advanced Analytics in Mining Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91589-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-91589-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91588-9

  • Online ISBN: 978-3-030-91589-6

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