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Improved mine waste characterisation through static blended test work

Parbhakar-Fox, A ORCID: 0000-0002-3570-1195, Fox, N, Hill, R, Ferguson, T and Maynard, B 2018 , 'Improved mine waste characterisation through static blended test work' , Minerals Engineering, vol. 116 , pp. 132-142 , doi: 10.1016/j.mineng.2017.09.011.

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Abstract

Prediction of the geoenvironmental characteristics of mine waste materials typically relies on the static andkinetic testing of individual waste units. In reality, waste rock piles are heterogeneous landforms containingpotentially acid forming (PAF) units juxtaposed with non-acid forming (NAF) units. Despite this, predictivegeoenvironmental test work programs rarely characterise blends of waste materials. Through undertaking laboratory-based blended static testing and mineralogical assessments, a first insight into the leachate chemistry ofa waste landform can be ascertained. This was tested using three waste units (Type A – alkaline, B – neutral andD – PAF; n=31) obtained from the Savage River mine, Western Tasmania. Seven daughter blend types (1–7;n=42) were prepared using different ratios of the individual waste types, and characterised by acid baseaccounting and mineralogical techniques. Blend types were NAF when a maximum of 20% of Type D was used,with daughter blend 4 (90% Type A +10% Type D) the most inert when screened against ANZECC (2000)aquatic protection trigger values. This study demonstrates that through first-pass static blended testing, opportunitiesexist to improve waste-rock handling practices and design NAF waste piles once field trials, involvinga range of particle sizes, have been conducted.

Item Type: Article
Authors/Creators:Parbhakar-Fox, A and Fox, N and Hill, R and Ferguson, T and Maynard, B
Keywords: acid mine drainage, waste management, sulphide, mining, mine planning
Journal or Publication Title: Minerals Engineering
Publisher: Pergamon-Elsevier Science Ltd
ISSN: 0892-6875
DOI / ID Number: 10.1016/j.mineng.2017.09.011
Copyright Information:

Crown copyright 2017. Published by Elsevier

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