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Remediation Strategies for the Long-term Management of Arsenic-trioxide Roaster Waste at the Giant Mine, Northwest Territories

Remediation Strategies for the Long-term Management of Arsenic-trioxide Roaster Waste at the Giant Mine, Northwest Territories
西北地区巨型矿山三氧化二砷焙烧废料长期管理的修复策略
批准号:
560243-2020
负责人:
Blowes, David
金额:
$16.07万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2021
资助国家:
加拿大
项目状态:
已结题
起止时间:
2021-01-01 至 2022-12-31

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中文摘要
翻译
位于西北地区耶洛奈夫的巨型金矿是加拿大最大的金矿之一,从1948年到2004年运营了56年。Giant矿场的所有权和补救责任在前运营商破产后归还给加拿大联邦政府,与该矿场相关的超过10亿美元的责任完全由加拿大纳税人承担。Giant矿的分散金与含毒砂(FeAsS)的矿石有关,该矿石经过焙烧从硫化物基质中释放出金。该过程产生三氧化二砷焙烧炉废物。约237,000吨三氧化二砷焙烧炉废料被存放在Giant矿场的采空区和专门建造的地下室中。一个关键的挑战是制定和实施一个可行的,长期的解决方案,稳定和管理三氧化二砷焙烧炉废物。本研究计划的总体目标是确定长期稳定和储存三氧化二砷废物的潜在策略。拟议的NSERC联盟计划,与巨型矿山监督委员会合作进行,建立在现有的研究工作的基础上,重点是修复巨型矿山,制定和评估管理焙烧炉废物的战略。该联盟计划包括七个有针对性的研究项目,以应对这一挑战,包括:1)三氧化二砷焙烧炉废物的化学和物理特性;通过2)砷酸铁沉淀,3)高温硫化,4)生物硫化,5)玻璃化和6)掺入水泥浆回填的三氧化二砷焙烧炉废物稳定化;(7)应用非传统同位素体系研究氧化砷焙烧炉废物转化的地球化学反应机理。这项研究计划的结果将有助于制定一项永久和安全的战略,用于修复三氧化二砷焙烧炉废物,无论是在巨人矿山网站和其他金矿,其中产生的砷废物。
英文摘要
The Giant Mine, located in Yellowknife, Northwest Territories, was one of the largest gold mines in Canadaduring its 56 years of operation from 1948 until 2004. Ownership and responsibility for remediation of theGiant Mine site reverted to the Federal Government of Canada following the bankruptcy of the formeroperator, and the > $1B liability associated with this site is entirely borne by Canadian taxpayers. Disseminatedgold at the Giant Mine was associated with arsenopyrite (FeAsS)-bearing ore, which was roasted to liberate thegold from the sulfide matrix. This process generated an arsenic-trioxide roaster waste. Approximately 237,000tonnes of arsenic-trioxide roaster waste was deposited in mined-out stopes and purpose-built chambersunderground at the Giant Mine site. A critical challenge is the development and implementation of a viable,long-term solution for stabilizing and managing the arsenic-trioxide roaster waste. The overall goal of thisresearch program is to identify potential strategies for long-term stabilization and storage of arsenic-trioxideroaster waste. The proposed NSERC Alliance program, conducted in partnership with the Giant MineOversight Board, builds on existing research efforts focused on remediation of the Giant Mine to develop andevaluate strategies for managing the roaster waste. This Alliance program includes seven targeted researchprojects to address this challenge, including: 1) chemical and physical characterization of the arsenic-trioxideroaster waste; arsenic-trioxide roaster-waste stabilization through 2) precipitation of iron arsenates, 3) high-temperature sulfidation, 4) biogenic sulfidation, 5) vitrification, and 6) incorporation into cemented-paste backfill; and 7) application of non-traditional isotope systems for understanding the biogeochemical reaction mechanisms which contribute to transformation of arsenic-trioxide roaster waste. The results of this research program will contribute to the development of a permanent and safe strategy for remediation of arsenic-trioxide roaster waste, both at the Giant Mine site and other gold mines where arsenical wastes are generated.
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Integration of Innovative Techniques to Improve Prediction and Remediation of Groundwater Contamination
  • 批准号:
    RGPIN-2019-07118
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $4.44万
  • 财政年份:
    2022
  • 负责人:
    Blowes, David
  • 依托单位:
Groundwater remediation
  • 批准号:
    CRC-2014-00012
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $3.64万
  • 财政年份:
    2022
  • 负责人:
    Blowes, David
  • 依托单位:
Integration of Innovative Techniques to Improve Prediction and Remediation of Groundwater Contamination
  • 批准号:
    RGPIN-2019-07118
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $4.44万
  • 财政年份:
    2021
  • 负责人:
    Blowes, David
  • 依托单位:
Groundwater Remediation
  • 批准号:
    CRC-2014-00012
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $14.57万
  • 财政年份:
    2021
  • 负责人:
    Blowes, David
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis