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Simultaneous removal of As(III) and As(V) from wastewater by co-precipitation using an experimental design approach

机译:使用实验设计方法通过共沉淀同时去除废水中的As(III)和As(V)

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Response surface methodology (RSM) with central composite design was used to determine the significant effects of pH, ferric ion, and initial arsenic concentrations on the removal efficiency of arsenic by a co-precipitation method. The regression function, with coefficients calculated by multiple linear regression, was calibrated and validated using external experimental runs. The correlation coefficients (R-2) of the actual vs. predicted arsenic removal percentages were 0.9871 and 0.9478 for As(III) and As(V), respectively. All major factors were determined to be significant by analysis of variance, with p-values < 0.01 and had a district effort on the removal process. Multi-layer response surfaces were developed to determine the highest removal efficiency. The maximum removal efficiencies for arsenic species were approximately 100%, achieved by model prediction with a Fe/As mole fraction of 3.34 at pH 7. These optimized conditions were then applied to remove arsenic from two industrial wastewater samples, giving efficiencies of 93.98 and 91.48%. The results reveal that the chosen conditions from the RSM approach are applicable for arsenic removal from real water samples, without any pretreatment process.
机译:采用中心复合设计的响应表面方法(RSM)通过共沉淀法确定pH,铁离子和初始砷浓度对砷去除效率的显着影响。使用外部线性实验对通过多元线性回归计算得出的系数的回归函数进行校准和验证。 As(III)和As(V)的实际砷去除率与预测的砷去除率的相关系数(R-2)分别为0.9871和0.9478。通过方差分析确定所有主要因素均具有显着性,p值<0.01,并且对清除过程进行了区域努力。开发了多层响应表面以确定最高去除效率。通过模型预测,在pH 7时Fe / As摩尔分数为3.34,砷的最大去除效率约为100%。然后采用这些优化条件从两个工业废水样品中去除砷,效率分别为93.98和91.48。 %。结果表明,从RSM方法中选择的条件适用于从真实水样中去除砷,而无需任何预处理过程。

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