dc.creator | Veličkovska, Ivana | |
dc.creator | Mihajlović, Ivan | |
dc.creator | Njagulović, Boban | |
dc.date.accessioned | 2023-03-09T20:14:03Z | |
dc.date.available | 2023-03-09T20:14:03Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2620-0597 | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/5596 | |
dc.description.abstract | The metallurgical process of the copper production is a very complex process and
requires the consumption of electrical energy in large quantities. One of the challenges of
today is to reduce the use of electrical energy by increasing the energy efficiency of the
system. This challenge can be solved by developing energy management in mining
companies. In order to approach the development of energy management, it is necessary to
create models for predicting the volume of copper production by investigating electricity
consumption in the main production stages. In this paper, the consumption of electricity
required in the process of copper production is analyzed on the example of a local mining
company. Data on electricity consumption were collected for a period longer than one year
and the parameters were divided according to the main phases of the metallurgical process.
Two models for predicting copper production using artificial neural network were created and
the most influential parameters were identified. The significance of the models is reflected in
the efficient forecasting of the copper production and therefore the demand for electrical
energy. Another advantage of the models is the increased possibility for rationalization of
electricity consumption on the basis of the influential parameters. The models are recognized
as flexible and can find their application in related companies. | sr |
dc.language.iso | en | sr |
dc.publisher | University of Belgrade - Technical Faculty in Bor | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/inst-2020/200131/RS// | sr |
dc.rights | openAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | International May Conference on Strategic Management – IMCSM20 | sr |
dc.subject | Electricity consumption | sr |
dc.subject | copper production | sr |
dc.subject | prediction model | sr |
dc.subject | artificial neural network | sr |
dc.title | PREDICTION OF THE COPPER PRODUCTION IN THE FRAMEWORK OF ELECTRICAL ENERGY CONSUMPTION USING ARTIFICIAL NEURAL NETWORK | sr |
dc.type | conferenceObject | sr |
dc.rights.license | BY | sr |
dc.rights.holder | University of Belgrade - Technical Faculty in Bor | sr |
dc.identifier.fulltext | http://machinery.mas.bg.ac.rs/bitstream/id/13738/bitstream_13738.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_machinery_5596 | |
dc.type.version | publishedVersion | sr |