Prediction of Acid Rock Drainage from Automated Mineralogy

Parbhakar-Fox, Anita; Lottermoser, Bernd G.; Hartner, Richard; Berry, Ron F.; Noble, Taryn L

doi:10.1007/978-3-319-42731-7_8

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…115 Parbharkar-Fox (2017) has also recommended the use of QEMSCAN or mineral liberation analysis for ARD prediction. 122 Developing a robust treatment strategy would be informed by the mineralogy of the ore body being mined, variations within the ore body, the processing methods and chemicals used, and the tailings storage method. Understanding the mineralogy and the ARD and ML potential and how this may affect the runoff and leachate is a well-established practice.…”

Section: Design and Construction Of Stackedmentioning

confidence: 99%

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Dewatered and Stacked Mine Tailings: A Review

et al. 2022

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Tailings storage is a prescient issue in mining, representing a visible and destructive liability. Dewatering tailings to a paste-like consistency is a popular option to reduce tailings volumes and increase dam safety, but dewatering further to a bulk cake consistency has mostly been reserved for small operations in locations where seismicity or terrain prohibited the construction of dams. The recent high-profile failures of tailings dams such at Fundão and Brumadinho facilities along with the push toward socially conscious investing have generated renewed and urgent interest in tailings dewatering, including stacked (“filtered” or “dry-stack”) tailings. While geochemistry and civil engineering topics relating to tailings in general are both extensively studied, very little academic literature is available specifically on stacked tailings, with discussion mostly confined to conference articles or extrapolation about the behavior of dewatered tailings. Furthermore, the nomenclature around and definition of this method of tailings storage is inconsistent, which can make researching the topic unnecessarily tedious and confusing and, in the worst case, can open the door to intentional misrepresentation of a tailings storage facility’s safety as this is a technology still being scaled up. In this article, the term stacked tailings (ST) is introduced to describe what has been otherwise called filtered tailings or dry-stack(ed) tailings to remedy the inaccuracies with the two existing names and gather the existing literature in one place. Topics covered include the existing definitions of dewatered and stacked tailings; opportunities and barriers to adoption; and construction and general operation for such tailings storage facilities. These are supplemented by an overview of literature in geotechnical and geochemical topics relevant to tailings dewatering (ex. clays) and tailings storage facilities. Emerging dewatering technologies and innovations which might be useful in the scaling up of the technology are also discussed. The aim is to provide a thorough background on the topic for a general audience, outline gaps in the literature, and encourage further exploration of stacked tailings and tailings cake by furnishing a comprehensive list of sources. Stacked tailings are discussed in a rigorous way to make the topic a more approachable way to encourage standardization and adoption, scale up, and the development of associated technologies.

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Section: Design and Construction Of Stackedmentioning

confidence: 99%

“…Dold (2017) recommended the use of QEMSCAN over static ARD prediction techniques due to their many problems and drawbacks . Parbharkar-Fox (2017) has also recommended the use of QEMSCAN or mineral liberation analysis for ARD prediction …”

Section: Design and Construction Of Stacked Tailings Facilitiesmentioning

confidence: 99%

Dewatered and Stacked Mine Tailings: A Review

et al. 2022

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“…The acquisition of quantitative data is expensive both in terms of costs and time. Thus, data prediction through mathematical modelling is not only a way to overcome these inconveniences, but also an interesting field of research in the mining industry (Berry et al, 2015;Lund et al, 2015;Rosa et al, 2014;Suazo et al, 2010). These types of prediction models can also be regarded as geometallurgical models, which are developed through a geometallurgical program (Lishchuk et al, 2015a;Lishchuk et al, 2015b) and visualized in a geometallurgical flowsheet (Lang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…To relate bulk chemistry to modal mineralogy estimations and results of laboratory tests of processing performance, would be beneficial. Modal mineralogy estimations based solely on chemistry are known as elementto-mineral conversion (EMC), and have been developed during the last four decades (Berry et al, 2011;Berry et al, 2015;Bryan et al, 1969;Hestnes and Sørensen, 2012;Johnson et al, 1985;Parian et al, 2015;Whiten, 2007;Yvon et al, 1990). Often these are based on the assumption of a linear relationship between mineral processing performance indicators and mineralogy (Berry et al, 2011;Mena Silva et al, 2018;Whiten, 2007).…”

Section: Introductionmentioning

confidence: 99%

A neural network approach for spatial variation assessment – A nepheline syenite case study

Silva

Ellefmo

Sandøy

et al. 2020

Minerals Engineering

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“…Third, utilizing data collected by other mine-site disciplines can cost-effectively assist in geoenvironmental pre-screening. For example, hyperspectral data using short-wave infrared data can be used to characterise drill core and waste materials [14][15][16][17][18][19], assay data can be used to calculate AMD [20,21] and automated mineralogical data can be used for waste characterisation following the methods described in [22][23][24][25]. By adopting a geometallurgical approach to this challenge, whereby proxy tests and methods to extract further information from existing datasets are developed and used as inputs for deposit-scale models, the opportunity is presented to adopt enhanced characterization practices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Geoenvironmental Risks: Integrated Applications of Mineralogical and Chemical Data

et al. 2018

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Management of solid mine wastes requires detailed material characterisation at the start of a project to minimize opportunities for the generation of acid and metalliferous drainage (AMD). Mine planning must focus on obtaining a thorough understanding of the environmental properties of the future waste rock materials. Using drill core obtained from a porphyry Cu project in Northern Europe, this study demonstrates the integrated application of mineralogical and geochemical data to enable the construction of enviro-geometallurgical models. Geoenvironmental core logging, static chemical testing, bulk- and hyperspectral mineralogical techniques, and calculated mineralogy from assay techniques were used to critically evaluate the potential for AMD formation. These techniques provide value-adding opportunities to existing datasets and provide robust cross-validation methods for each technique. A new geoenvironmental logging code and a new geoenvironmental index using hyperspectral mineralogical data (Hy-GI) were developed and embedded into the geochemistry-mineralogy-texture-geometallurgy (GMTG) approach for waste characterisation. This approach is recommended for new mining projects (i.e., early life-of-mine stages) to ensure accurate geoenvironmental forecasting, therefore facilitating the development of an effective waste management plan that minimizes geoenvironmental risks posed by the mined materials.

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Prediction of Acid Rock Drainage from Automated Mineralogy

Cited by 8 publications

References 21 publications

Dewatered and Stacked Mine Tailings: A Review

Dewatered and Stacked Mine Tailings: A Review

A neural network approach for spatial variation assessment – A nepheline syenite case study

Forecasting Geoenvironmental Risks: Integrated Applications of Mineralogical and Chemical Data

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