Abstract
Cemented paste backfill (CPB) is a profitable tailings management strategy for underground mines. CPB is designed to meet specific objectives and constraints such as strength and workability at reasonable costs. Traditional design of CPB is costly due to large number of variables such as properties of tailings/cement. This paper proposes an innovative AI-based approach for multi-objective mixture design optimisation of CPB considering properties of tailings and cement. This approach contributes to meliorating mine economy according to the United Nations Sustainable Development Goals by reducing the amount of cement considering pozzolanic potential of tailings, thereby reducing carbon footprint and operational costs.
| Original language | English |
|---|---|
| Pages (from-to) | 110-126 |
| Number of pages | 17 |
| Journal | International Journal of Mining, Reclamation and Environment |
| Volume | 37 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2022 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- Cemented paste backfill
- machine learning
- mixture design
- multi-objective optimisation
- pozzolanic potential
- variabilities of materials
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Geology
- Earth-Surface Processes
- Management of Technology and Innovation
