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Acid rock drainage prediction: A critical review
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.
Number of Authors: 1
2016 (English)In: Journal of Geochemical Exploration, ISSN 0375-6742, E-ISSN 1879-1689, Vol. 172, 120-132 p.Article in journal (Refereed) Published
Abstract [en]

Acid rock drainage (ARD) prediction is a very important issue in order to predict and prevent environmental pollution associated with mining activities. Nowadays, simple tests are widely applied and established in the mining and consulting business for ARD prediction. These tests have many known errors and problems, as that they do not account for the complexity of the mineral assemblage of an ore deposit, and therefore are not able to predict the geochemical behavior accurately. This critical review has the aim of first, highlighting the geochemical processes associated to the problems of ARD prediction. Secondly, the errors and limitations of the standard static and kinetic tests are highlighted. The currently applied calculation factor of 31.25 for sulfide acid potential calculation overestimates the carbonate neutralization potential by 100% in its geochemical assumptions. Thus, the calculation factor 62.5, based on the effective carbonate speciation at neutral pH, is recommended. Additionally, standard ABA procedure ignore the acid potential of Fe(III) hydroxides and/or sulfates and do not distinguish between different carbonate minerals. This can be critical, as for example siderite can be a net acid producing carbonate. Therefore, it is crucial to count on accurate quantitative mineral data in order to be able to accurately predict ARD formation and potential liberation of hazardous trace elements to the environment.

In many modern mining operations, quantitative mineral data is nowadays produced in order to enhance the recovery of the extraction process by the incorporation of geometallurgical information (e.g. quantitative mineralogy, mineral liberation, textural information, grain size distribution). Thus, the use of this very same existing data for ARD prediction can increase importantly the precision of ARD prediction, often without additional costs and testing. The only requirement is the interdisciplinary collaboration between the different divisions and data exchange in a modern mining operation.

Place, publisher, year, edition, pages
2016. Vol. 172, 120-132 p.
National Category
Geochemistry
Research subject
Applied Geochemistry
Identifiers
URN: urn:nbn:se:ltu:diva-59627DOI: 10.1016/j.gexplo.2016.09.014ScopusID: 2-s2.0-84992597891OAI: oai:DiVA.org:ltu-59627DiVA: diva2:1033926
Note

Validerad; 2016; Nivå 2; 2016-11-11 (andbra)

Available from: 2016-10-10 Created: 2016-10-10 Last updated: 2016-11-11Bibliographically approved

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Dold, Bernhard
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