Predictive Waste Classification Using Field-Based and Environmental Geometallurgy Indicators, Mount Lyell, Tasmania
Best practice for acid rock drainage (ARD) risk assessment predominately relies on the geochemical properties of sulfidic rocks. Consequently, a plethora of geochemical tests are routinely utilised by the mining industry to predict ARD formation. Due to l
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Abstract Best practice for acid rock drainage (ARD) risk assessment predominately relies on the geochemical properties of sulfidic rocks. Consequently, a plethora of geochemical tests are routinely utilised by the mining industry to predict ARD formation. Due to limitations associated with these tests and their relatively high costs, analysis of recommended best practice sample numbers is rarely achieved, thus reducing the accuracy of waste management plans. This research aimed to address this through identifying potential geometallurgy indicators using drill core samples (n = 70) obtained from the Comstock Chert, a new prospect proximal to Mount Lyell, western Tasmania, Australia. Samples were subjected to a range of mineralogical analyses, routine ARD geochemical tests (i.e., paste pH; acid-base accounting, ABA; net acid generation, NAG), field-based techniques (e.g., portable X-ray fluorescence, pXRF; short-wave infrared spectrometry, SWIR), and geometallurgical analyses (i.e., HyLogger, Equotip). This study demonstrated: (1) HyLogger data allows identification of acid-neutralizing carbonate minerals; (2) Equotip hardness data provide a conservative indication of lag-time to acid formation; (3) CARD risk grading accurately identifies high and low risk ARD domains; and (4) pXRF data provides a sound indication of the abundance of environmentally significant elements. Consequently, the application of geometallurgical techniques to drill core allows the prediction of ARD characteristics that inform waste characterization and management plans.
A. Parbhakar-Fox (&) School of Physical Sciences, University of Tasmania, Private Bag 79, Hobart, TAS 7001, Australia e-mail: [email protected] B. Lottermoser Institute of Mineral Resources Engineering, RWTH Aachen University, Wüllnerstrasse 2, 52062 Aachen, Germany e-mail: [email protected] © Springer International Publishing Switzerland 2017 B. Lottermoser (ed.), Environmental Indicators in Metal Mining, DOI 10.1007/978-3-319-42731-7_9
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A. Parbhakar-Fox and B. Lottermoser
Introduction Determining the propensity of a rock unit to produce ARD is possible through the use of established and emerging geometallurgical tools and techniques. The geochemistry-mineralogy-texture (GMT) approach is one such protocol allowing for improved ARD prediction. Through using the GMT approach, it is possible to pursue best practice sample numbers (cf. Price 2009) for deposit-wide ore and waste characterization, rather than through using other protocols such as the Wheel Approach (Morin and Hutt 1998). Despite the merits of the GMT approach and the use of such simple pre-screening tests for deposit-wide domaining, it can be argued that the undertaking of such specialised ARD focused analyses (e.g., geochemical tests) is financially limiting as these data are fit-for-purpose and cannot be used to characterize other features of the ore body. Instead, to facilitate deposit-wide characterization and to add value to already existing datasets, proxies for ARD data must be ide
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