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The Use of a Large-strain Drying and Consolidation Model to Optimise Multilift Tailing Deposits

机译:使用大菌株干燥和固结模型来优化多倍拖尾沉积物

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Thin-lift atmospheric fine drying (AFD) is a technique used to dewater mine and oil sand tailings, which utilises both self-weight consolidation and atmospheric evaporation. The disposed layers undergo a cyclic drying and rewetting process due to precipitation and deposition of additional lifts on top of the dried layer. The current research aims to optimise the deposition process via use of a numerical model and realistic atmospheric conditions, including both periods of drying and wetting. The model is based upon water balance and includes large strain considerations. A number of material behaviours are characterised using empirical fitting curves based upon laboratory measurement of material characteristics, including both the shrinkage and water retention curves for drying and rewetting. The model is able to model multiple lifts, simulating field scale and realistic climatic conditions within timescales suitable for engineering practice (eg seconds or minutes). The model has been previously validated against controlled laboratory experiments and utilised to simulate field tests. A series of simulations are presented to illustrate the ability of the model to be used as a practical tool for the optimisation of a tailings deposition strategy.
机译:薄升力大气精细干燥(AFD)是一种用于脱水矿山和油砂尾矿的技术,可利用自重固结和大气蒸发。由于在干燥层顶部的沉淀和沉积附加升降机,所设置的层经历循环干燥和重新润湿过程。目前的研究旨在通过使用数值模型和现实的大气条件来优化沉积过程,包括干燥和润湿的两个时期。该模型基于水平衡,包括大的应变考虑因素。基于材料特性的实验室测量,使用经验拟合曲线的许多材料行为的特征在于,包括用于干燥和重新灌注的收缩和水保持曲线。该模型能够在适合工程实践(例如秒或分钟)的时间尺寸内模拟多个升降机,模拟现场规模和现实气候条件。该模型先前已经验证了控制实验室实验并用于模拟现场测试。提出了一系列模拟以说明模型用作优化尾矿沉积策略的实用工具的能力。

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