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Enhancing recovery of uranium column bioleaching by process optimization and kinetic modeling 被引量:5

采用工艺优化和动力学模型提高铀矿生物柱浸的回收率(英文)
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摘要 This research aimed to enhance the column bioleaching recovery of uranium ore by Acidithiobacillus ferrooxidans.Seven factors were examined for their significance on bioleaching using a Plackett-Burman factorial design.Four significant variables([Fe2+]initial,pH,aeration rate and inoculation percent)were selected for the optimization studies.The effect of these variables on uranium bioleaching was studied using a central composite design(CCD).The optimal values of the variables for the maximum uranium bioleaching recovery(90.27±0.98)%were as follows:[Fe2+]initial=2.89g/L,aeration rate420mL/min,pH1.45and inoculation6%(v/v).[Fe2+]initial was found to be the most effective parameter.The maximum uranium recovery from the predicted models was92.01%.This value was in agreement with the actual experimental value.The analysis of bioleaching residue of uranium ore under optimum conditions confirmed the formation of K-jarosite on the surface of minerals.By using optimal conditions,uranium bioleaching recovery is increased at column and jarosite precipitation is minimized.The kinetic model showed that uranium recovery has a direct relation with ferric ion concentration. 采用嗜酸氧化亚铁硫杆菌提高轴矿的生物柱浸回收率,利用Plackett-Burman因子设计法研究七因素对铀矿生物柱浸显著性的影响。选取[Fe^(2+)]_(initial)、pH值、通气速率和接种率4个显著性参数进行优化试验,采用一种中心复合设计法研究这些参数对铀矿生物浸出的影响。铀矿生物浸出率最大时((90.27±0.98)%)对应的优化参数如下:[Fe^(2+)]_(initial)=2.89 g/L、通气速率420 mL/min、pH 1.45、接种率6%(体积分数)。[Fe^(2+)]_(initial)对铀矿生物浸出的影响最大。由预测模型得到铀的最大回收率为92.01%,此数据与实验所得结果吻合较好。最佳浸出条件下铀矿浸出渣的分析结果表明,矿物表面形成了黄钾铁矾。最佳浸出条件下铀的生物柱浸率提高,黄钾铁矾沉淀量减少。动力学模型预测结果表明,铀的回收率与Fe^(2+)浓度密切相关。
出处 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2017年第12期2691-2703,共13页 中国有色金属学报(英文版)
基金 the Tarbiat Modares University & Nuclear Science and Technology Research Institute for their financial support
关键词 column bioleaching uranium ore SCREENING optimization kinetic model Acidithiobacillus ferrooxidans 生物柱浸 铀矿 筛选 优化 动力学模型 嗜酸氧化亚铁硫杆菌
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