Numerical Modeling and Database development for Electrokinetic Dewatering of Ultra Fine Mine Tailings

超细尾矿电动脱水的数值模拟和数据库开发

• 批准号: RGPIN-2016-03700
• 负责人: Shang, Julie
• 金额: $ 1.97万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=614564
• 关键词: Numerical; Modeling; Database; development; Electrokinetic

Ultrafine tailings (UFT) have low solid content, high water retention and virtually zero shear strength over long period after disposal. Without treatment, the tailings remain in a fluid state in tailings ponds for decades, reducing efficiency of water recycle, generating environmental impacts and causing delays in mine closure.Currently dewatering of UFT is mainly relying on self-weight consolidation. Existing dewatering technologies, such as chemical additives, vacuum filtration and preloading consolidation, have shown limited success in large scale field applications. The proposed research will address challenges facing the mining industry on disposal and reclamation of mine tailings. The applicant has carried out extensive studies on electrokinetic (EK) dewatering of a wide variety of UFTs from copper, zinc, gold, uranium and oil sand processing. Large quantities of experimental data have been collected on thickening, dewatering, and consolidation by electrokinetics. In the proposed research the applicant and her research team will develop numerical models to simulate large strain consolidation process and physiochemical changes after EK treatment. The deliverables of this research over the 5 year funding period include: (1) large strain consolidation models to simulate electrokinetics and gravitation / surcharge /vacuum combined consolidation and dewatering of ultrafine mine tailings. The analysis will be carried out using computer software Comsol Multiphysics; (2) artificial neural network (ANN) models to predict the physio-chemical responses of ultrafine tailings to electrokinetic treatment, especially due to electrochemical reactions induced cementation effects and pore fluid changes. The analysis will be conducted using ANN software Trajan 4.0; (3) database containing results of EK treatment over a wide variety of geo-materials, with focus on UFTs. The numerical modes will provide much needed foundation for the analysis, design and implementation of EK dewatering ultrafine mine tailings and other geomaterials. The proposed projects will provide HQPs opportunities to pursue postgraduate education in the fields of mining, geotechnical and environmental engineering. Two PhD students and two Master students will be supported over the 5-year DG funding period. The trainees will gain knowledge and become experts in EK technology, a multi-disciplinary subject with applications in broad fields. More importantly, they will develop analytical, problem solving, communication, and leadership skills, which will greatly benefit their future careers. This research will make significant contribution to the analysis, design and implementation of EK dewatering in UFTs, secure the applicant’s position of leading edge research on electrokinetics internationally, and lead to the technical transfer of EK dewatering technology to engineering applications.

超细尾矿具有固含量低、保水性好、长期抗剪强度为零等特点。尾矿在未经处理的情况下，几十年来一直处于流体状态，降低了水循环效率，对环境造成影响，并导致矿山关闭延迟。目前UFT的脱水主要依靠自重固结。现有的脱水技术，如化学添加剂、真空过滤和预压固结，在大规模现场应用中显示出有限的成功。拟议的研究将解决采矿业在尾矿处置和回收方面面临的挑战。 申请人已经对来自铜、锌、金、铀和油砂加工的各种UFT的电动（EK）脱水进行了广泛的研究。大量的实验数据已经收集到的浓缩，脱水和固结的电动力学。在拟议的研究中，申请人及其研究团队将开发数值模型，以模拟大应变固结过程和EK处理后的物理化学变化。本研究在5年资助期内的成果包括：（1）模拟超细尾矿电动力学和重力/堆载/真空联合固结和脱水的大应变固结模型。分析将使用计算机软件Comsol Multiphysics进行;（2）人工神经网络（ANN）模型来预测超细尾矿对电动处理的物理化学响应，特别是由于电化学反应引起的胶结效应和孔隙流体变化。将使用人工神经网络软件Trajan 4.0进行分析;（3）包含各种地质材料的EK处理结果的数据库，重点是UFT。数值模型的建立将为EK脱水超细尾矿及其它岩土工程的分析、设计和实施提供必要的依据。 拟议的项目将为HQP提供机会，在采矿、岩土工程和环境工程领域接受研究生教育。两名博士生和两名硕士生将在5年的DG资助期内获得支持。学员将获得知识，并成为EK技术的专家，EK技术是一个多学科的学科，在广泛的领域应用。更重要的是，他们将发展分析，解决问题，沟通和领导能力，这将大大有利于他们未来的职业生涯。 该研究将为UFT中EK脱水的分析、设计和实施做出重大贡献，确保申请人在国际电动力学研究方面的领先地位，并导致EK脱水技术向工程应用的技术转移。