Predicting Gold Mine Surface Cooling Systems Energy Consumption

Kabelo Donald Lomko, Khmaies Ouahada, Hailing Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An artificial neural network (ANN) was utilised to predict the energy consumption of the fridge plants of a mine's surface cooling system. Predictive accuracy of 96, 89% was achieved. The maximum and minimum predicted energy consumption on the fridge plants was found to be 17 MW, 12 MW, respectively, which is fairly close to the real-time energy consumption of the machines. This model was implemented under automated load shift conditions to reinforce a hypothesis of this research, which is that demand side management (DSM) initiatives can be augmented by accurate predictive models. Accurate predictive models will ensure effective cooling system planning, sufficient machine maintenance, effective cooling system operation, optimal mine energy allocation, and energy management on the mine cooling systems, particularly its fridge plants/chillers. As the mining industry traverses towards automation of its DSM initiatives, intelligent systems have to be implemented for full automation to be achieved, and this research sought to make a contribution to that aspect. An ANN was found to outclass multiple linear regression, thus, ANNs were found to be better models for integration into DSM projects. Finally, the number of fridge plants that need to operate were determined based on the predicted energy consumption. The number of fridge plants that operated during Eskom's morning and evening peak periods was 4 and 1, respectively. This was found to be better than the traditional mode of operation whereby the entire number of fridge plants (6) operate all day.

Original languageEnglish
Title of host publicationIEEE AFRICON 2019
Subtitle of host publicationPowering Africa's Sustainable Energy for All Agenda: The Role of ICT and Engineering, AFRICON 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132891
DOIs
Publication statusPublished - Sept 2019
Event2019 IEEE AFRICON, AFRICON 2019 - Accra, Ghana
Duration: 25 Sept 201927 Sept 2019

Publication series

NameIEEE AFRICON Conference
Volume2019-September
ISSN (Print)2153-0025
ISSN (Electronic)2153-0033

Conference

Conference2019 IEEE AFRICON, AFRICON 2019
Country/TerritoryGhana
CityAccra
Period25/09/1927/09/19

Keywords

  • artificial neural network
  • demand side management
  • fridge plants/chillers
  • load shift
  • mine cooling systems

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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