Practical Strategies for Managing Water Balance and Quality at Open Pit Mines

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 4388

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Guest Editor
Geotechnical Engineering Centre, School of Civil Engineering, The University of Queensland, Brisbane, Australia
Interests: Applying Geotechnical Engineering principles to mine waste management and closure
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Special Issue Information

Dear Colleagues,

The impacts of open pit mining are very much dependent on the climatic and topographic environment of the site, as well as the geochemistry of the materials exposed by mining and mineral processing. Open pit mining does not produce salinity; salts exist naturally in the soil/rock profile. What mining and processing does produce is a concentration of salinity, usually through the evaporation of ponded mine-affected water. The mining of sulfidic orebodies often leads to the exposure of sulfidic waste rock and tailings to oxygen and rainfall ingress, potentially resulting in acid and metalliferous drainage. Open pit mines typically have a zero (water) discharge condition imposed upon them, which necessitates that all mine-affected water be stored on site, where it accumulates and becomes saline (and possibly acidic) due to evaporation-induced concentration. This almost guarantees closure and rehabilitation difficulties, in net positive water balance climates in which excessive mine-affected water is generated, and in net negative water balance climates in which salts and oxidation products are flushed to a dry climatic environment by rainfall events. Sites that have natural marginally net positive water balances can be rendered net negative and contaminating due to the residual surface voids and waste storages left by mining and processing. Pit lakes in a net negative water balance climate will inevitably turn increasingly saline and/or acidic, and/or alkaline, and there is unlikely to be enough fresh water available to flush them. Articles that address practical strategies for managing water balance and quality at open pit mines are sought for a Special Issue of Water.

Prof. David J. Williams
Guest Editor

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Keywords

  • Acid and metalliferous drainage
  • Mine water balance
  • Mine water quality
  • Practical water management strategies at open pit mines
  • Mining-induced salinity
  • Water management at open pit mines

Published Papers (1 paper)

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Research

17 pages, 2051 KiB  
Article
A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor
by Dan Ma, Hongyu Duan, Xin Cai, Zhenhua Li, Qiang Li and Qi Zhang
Water 2018, 10(11), 1618; https://doi.org/10.3390/w10111618 - 9 Nov 2018
Cited by 25 | Viewed by 4036
Abstract
Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal [...] Read more.
Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries. Full article
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