Machine-Learning Aided Design for Cemented Paste Backfill

Chongchong Qi

doi:10.26182/5dbba1d68cc28

Machine-Learning Aided Design for Cemented Paste Backfill

Chongchong Qi

School of Engineering

Research output: Thesis › Doctoral Thesis

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Abstract

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.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Ma, Guowei, Supervisor Fourie, Andy, Supervisor
Award date	3 Oct 2019
DOIs	https://doi.org/10.26182/5dbba1d68cc28
Publication status	Unpublished - 2019

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TH19 251 THESIS - DOCTOR OF PHILOSOPHY - QI Chongchong - 2019
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Cite this

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title = "Machine-Learning Aided Design for Cemented Paste Backfill",

abstract = "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. ",

keywords = "Cemented paste backfill, Artificial intelligence, Mine tailings, Flocculation and sedimentation, backfill design, Mix design, machine learning, Pipe transport",

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N2 - 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.

AB - 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.

KW - Cemented paste backfill

KW - Artificial intelligence

KW - Mine tailings

KW - Flocculation and sedimentation

KW - backfill design

KW - Mix design

KW - machine learning

KW - Pipe transport

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DO - 10.26182/5dbba1d68cc28

M3 - Doctoral Thesis

ER -

Machine-Learning Aided Design for Cemented Paste Backfill

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