437
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Application of compound Poisson process for modelling of ore flow in a belt conveyor system with cyclic loading

, , , &
Pages 376-391 | Received 01 Jun 2017, Accepted 26 Jul 2017, Published online: 24 Nov 2017
 

Abstract

This paper deals with the analysis of the random process of cyclic loading of a mining belt conveyor with portions of ore discharged by loaders or trucks. Such transfer of transported ore from a cyclic to a continuous transport is typical for the specific mining operations implemented in the underground copper ore mines with room and pillar mining. The conveyors in such systems are usually significantly oversized in order to match the peak loads of cumulated discharges of ore hauled from mining fields by loaders. Therefore, the actual loadings that occur in the mining transportation systems need to be analysed to provide the data for more accurate design and control of belt conveyors. The large data-set of actual loadings of the belt conveyors has been used for the stochastic modelling of the analysed process. The compound Poisson process has been proposed as mathematical tool to analyse/describe properties of ore flow. The discussion of the chosen distribution functions and results of the fitted model simulations compared with the examined measurement data are presented. In this paper, the case of a belt conveyor loaded only by a single feeding point where loaders are randomly discharged have been analysed. More complex cases (several loading points, mixed ore supply from cyclic and continuous ore mass flow from preceding conveyors or ore bunkers) are under investigation and will be presented in the future.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by the Framework Programme for Research and Innovation Horizon 2020 under grant agreement no. 636834 (DISIRE -Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock). This work is also supported by OPUS [grant number 2016/21/B/ST1/00929].

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.