Artificial neural networks models for rate of penetration prediction in rock drilling
Fathipour Azar, Hadi; Saksala, Timo; Jalali, Seyed-Mohammad Esmaiel (2017-08-21)
Fathipour Azar, Hadi
Saksala, Timo
Jalali, Seyed-Mohammad Esmaiel
21.08.2017
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201708291836
https://urn.fi/URN:NBN:fi:tty-201708291836
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [16929]