Cemented paste backfill (CPB) technology is a promising way to substantially alter the way mine tailings are managed. At present, CPB design is mainly experiment-based, which is labour-intensive and time-consuming. Towards this end, a stateof-the-art concept for CPB design is proposed in this thesis, namely machine-learning aided design for CPB (MLAD_CPB). The methodology of MLAD_CPB is presented and its feasibility is validated through three example applications. This thesis presents a number of advances in CPB design, which, with further consolidation and refinement, may become important tools in predicting process parameters starting from constituent materials of CPB.
|Qualification||Doctor of Philosophy|
|Award date||3 Oct 2019|
|Publication status||Unpublished - 2019|