Application of mineralogy in the interpretation of laboratory scale acid rock drainage (ARD) prediction tests : a gold case study

The mining and beneficiation of gold generates large tonnages of waste, with up to 99% of mined gold ore discharged as waste. The waste generated contains unoxidized sulfides that when exposed to air and water react to form acid, which results in acid rock drainage (ARD). ARD is usually associated with low pH, high sulfate content and elevated concentrations of toxic elements. The mobility of ARD affects our scarce water resources, land and aquatic species. Methods applied to treat ARD do not provide a walk-away solution and they are either expensive or difficult to maintain. The best solution to completely eradicate ARD is to prevent it from the source. However, the effectiveness of ARD prevention depends on the accuracy of predicting future drainage quality. This can be done by using ARD prediction tests, which are generally classified as either static (acid base accounting, ABA, net acid generation, NAG) or kinetic (column leach, humidity cell, biokinetic test). There is no single test capable enough to accurately predict acid generating potential. It is therefore usual practise to conduct more than one test and cross-check results to ensure that the appropriate conclusions are made. In doing so, the reliability of the tests is improved but in some cases the different test results do not correlate. Mineralogy is an analytical technique that can be used to understand the nature of the errors and to better understand the leaching behaviour of minerals in the different tests. This study uses mineralogy to analyse both static and biokinetic test results of a Witwatersrand gold sample in order to improve the understanding of behaviour of mine wastes under different ARD prediction test conditions. A run-of-mine gold sample from the Witwatersrand region in South Africa was used as a case study to explore the mineral leaching behaviour for different ARD prediction tests.

Grantor dc:publisher.institution

Centre for Bioprocess Engineering Research

Year dc:date.issued

2014

Author dc:creator

• Dyantyi, Noluntu

Advisors dc:contributor.advisor

• Becker, Megan
• Broadhurst, Jennifer Lee

• Bioprocess Engineering

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/13377

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/13377