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Artificial neural networks models for rate of penetration prediction in rock drilling

Author(s):


Medium: journal article
Language(s): Finnish
Published in: Rakenteiden Mekaniikka = Journal of Structural Mechanics, , n. 3, v. 50
Page(s): 252-255
DOI: 10.23998/rm.64969
Abstract:

Prediction of the rate of penetration (ROP) is an important task in drilling economical assessments of mining and construction projects. In this paper, the predictability of the ROP for percussive drills was investigated using the artificial neural networks (ANNs) and the linear multivariate regression analysis. The “power pack” frequency, the revolution per minute (RPM), the feed pressure, the hammer frequency, and the impact energy were considered as input parameters. The results indicate that the ANN with the regression model predicts the ROP under different conditions with high accuracy. It also demonstrates that the ANN approach is a beneficial tool that can reduce cost, time and enhance structure reliability.

License:

This creative work has been published under the Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given and the same license is used as for the original work (the above link must be included). Any alterations to the original must also be mentioned.

  • About this
    data sheet
  • Reference-ID
    10677076
  • Published on:
    02/06/2022
  • Last updated on:
    10/11/2022
 
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