A Monte Carlo simulation for predicting truck payload distribution

Hewavisenthi, Ranishka, Lever, Paul and Tadic, Dihon (2011). A Monte Carlo simulation for predicting truck payload distribution. In: Proceedings of the 2011 Australian Mining Technology Conference. 2011 Australian Mining Technology Conference, Noosa, Qld., Australia, (61-72). 12-14 September 2011.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
Author Hewavisenthi, Ranishka
Lever, Paul
Tadic, Dihon
Title of paper A Monte Carlo simulation for predicting truck payload distribution
Conference name 2011 Australian Mining Technology Conference
Conference location Noosa, Qld., Australia
Conference dates 12-14 September 2011
Proceedings title Proceedings of the 2011 Australian Mining Technology Conference
Place of Publication Pinjarra Hills, Qld., Australia
Publisher CRC Mining
Publication Year 2011
Sub-type Fully published paper
Start page 61
End page 72
Total pages 12
Collection year 2012
Language eng
Abstract/Summary Reducing truck payload variance in surface mining operations improves productivity by reducing bunching effects and machine wear from overloaded trucks. This paper outlines a Monte-Carlo simulation that has been used to study the effect of bulk density, fill factor, bucket size and number of loading passes on the long term payload distribution of earthmoving systems. The simulation can be used in applications where the distribution of bulk density and fill factor can be experimentally determined or estimated from prior knowledge. This information can be used to select loader buckets to minimize truck payload variance and thus improve productivity.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Conference theme: Decision Support - embedding process capability in mining systems

Document type: Conference Paper
Collections: School of Mechanical & Mining Engineering Publications
Non HERDC
 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 183 Abstract Views, 17 File Downloads  -  Detailed Statistics
Created: Wed, 02 Nov 2011, 09:41:55 EST by Viviane Victoria Crosthwaite on behalf of Faculty Of Engineering, Architecture & Info Tech