Optimal decarbonisation pathway for mining truck fleets

Loading...
Thumbnail Image

Authors

Yu, Gang
Ye, Xianming
Ye, Yuxiang
Huang, Hongxu
Xia, Xiaohua

Journal Title

Journal ISSN

Volume Title

Publisher

KeAi Communications

Abstract

The fossil fuel powered mining truck fleets can contribute up to 80% of total emissions in open pit mines. This study investigates the optimal decarbonisation pathway for mining truck fleets. Notably, our proposed pathway incorporates power generation, negative carbon technologies, and carbon trading. Technical, financial, and environmental models of decarbonisation technologies are established, capturing regional variations and time dynamic characteristics such as cost trends and carbon capture efficiency. The dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation pathway. To address this, we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period. A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed model. Results indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost, annual budget, and carbon trading prices. The proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.

Description

DATA AVAILABILITY : Data will be made available on request.

Keywords

Coal mine, Truck fleet, Carbon emission, Optimal decarbonisation pathway, SDG-07: Affordable and clean energy, SDG-09: Industry, innovation and infrastructure, SDG-12: Responsible consumption and production

Sustainable Development Goals

SDG-07:Affordable and clean energy
SDG-09: Industry, innovation and infrastructure
SDG-12:Responsible consumption and production

Citation

Yu, G., Ye, X., Ye, Y. et al. 2024, 'Optimal decarbonisation pathway for mining truck fleets', Journal of Automation and Intelligence, vol. 3, pp. 129-143. https://DOI.org/10.1016/j.jai.2024.03.003.