TY - GEN
T1 - Predicting Gold Mine Surface Cooling Systems Energy Consumption
AU - Lomko, Kabelo Donald
AU - Ouahada, Khmaies
AU - Zhu, Hailing
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - artificial neural network
KW - demand side management
KW - fridge plants/chillers
KW - load shift
KW - mine cooling systems
UR - http://www.scopus.com/inward/record.url?scp=85088373964&partnerID=8YFLogxK
U2 - 10.1109/AFRICON46755.2019.9133822
DO - 10.1109/AFRICON46755.2019.9133822
M3 - Conference contribution
AN - SCOPUS:85088373964
T3 - IEEE AFRICON Conference
BT - IEEE AFRICON 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE AFRICON, AFRICON 2019
Y2 - 25 September 2019 through 27 September 2019
ER -