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Grade-recovery modelling and optimization of the froth flotation process of a lepidolite ore

机译:Lepidolite Ore的泡沫浮选过程的等级恢复建模与优化

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With the increase in the demand for lithium, Li-bearing minerals could be considered as alternative resources to achieve the supplying. So, efficient technological solutions for the valorization of these minerals are required. In this context, the froth flotation process of a lepidolite ore was modelled and optimized. Closely following the response surface methodology (RSM), the effects of three independent process variables (pulp pH, flotation collector dosage and flotation time) upon two common measures of the separation (lithium recovery and lithium content) were studied. These were modelled using the experimental data obtained starting with the implementation and execution of a full 23 factorial design and ending with a (face-centered) central composite design (CCD), a second order design. The coefficients of the second-order polynomial regression models were fitted by solving linear least squares problems. After statistical validation, the fitted models were used to support the identification of the significant effects of the process variables and to provide estimations of the measures of separation (responses) for combinations of the levels of the process variables over a feasible region of interest. Using directly the measured values of Li recovery and Li content, the selected experimental Pareto optimal combination of the levels of the process variables is: pulp pH = 2, dosage of collector = 500 g.t(-1) and flotation time = 12 min producing a concentrate with Li recovery of 91.51% and a Li content of 1.96%. Using the fitted second order models for the separation criteria, a refined Pareto optimal combination was obtained as the solution of the multicriteria optimization (maximization) problem that was solved by different methods (Weighted Sum of Objectives, Goal Programming and Desirability Functions). The refined Pareto optimal combination was the same than the selected experimental Pareto optimal combination, only the collector dosage decreased to 470478 g.t(-1), producing a concentrate with Li recovery around 92.50% and a Li content of 2.00%. (C) 2016 Elsevier B.V. All rights reserved.
机译:随着锂电锂需求的增加,Li-轴承矿物可以被视为实现供应的替代资源。因此,需要有效的技术解决这些矿物质的技术解决方案。在这种情况下,模拟和优化锂岩石矿石的泡沫浮选过程。研究了响应面方法(RSM),研究了三种独立的过程变量(纸浆pH,浮选收集剂剂量和浮选时间)在分离(锂回收率和锂含量)的两个常见措施上的影响。这些是使用从实施和执行开始的实验数据建模的完整23因子设计,并以(以面为中心)的中央复合设计(CCD),二阶设计来实现。通过求解线性最小二乘问题,拟合二阶多项式回归模型的系数。在统计验证之后,使用拟合模型来支持识别过程变量的显着影响,并提供分离(反应)的估计,以便在可行的感兴趣区域中的过程变量的水平的组合。使用直接测量的LI恢复和LI含量的测量值,所选实验帕累托的过程变量水平的最佳组合是:纸浆pH = 2,收集器剂量= 500gt(-1)和浮选时间= 12分钟产生a浓缩李恢复91.51%,李含量为1.96%。使用适用于分离标准的拟合二阶模型,获得了通过不同方法(重量的目标,目标编程和期望功能)解决的多准型优化(最大化)问题的解决方案。精制的帕累托最佳组合与所选实验帕累托最佳组合相同,只有收集剂剂量降至470478g(-1),产生浓浓度的锂恢复约92.50%,LI含量为2.00%。 (c)2016年Elsevier B.v.保留所有权利。

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