氧化镁基生物质炭高效去除水体中磷的特性

发布时间：2018-09-11 10:30

【摘要】：利用花生壳为前驱体,在高温限氧条件下,将氧化镁(MgO)负载于生物质炭(BC)表面制备出氧化镁基生物质炭(MgOBC)复合材料.系统研究了MgO-BC对水体中P的吸附特性,并探讨了溶液pH值、接触时间、竞争离子等因素对P的吸附效果的影响.结果表明,P的最佳吸附初始pH为7~9,过酸过碱的环境均不利于P的吸附;P的吸附过程可在540 min内达到平衡,且动力学曲线较好地符合伪一级和伪二级动力学模型,拟合系数可达97.3%和99.0%;当Cl~-、HCO_3~-、NO_3~-等共存离子的量浓度达到P的10倍时,MgO-BC对P仍具有较强的吸附能力;P的吸附过程较好地符合Langmuir等温模型,拟合系数达99%,理论最大吸附容量为138.07 mg·g~(-1),远高于其它未经改性或改性的生物质炭和几种典型P吸附剂的吸附容量.此外,吸附P后的复合材料可作为肥料施入土壤,可有效实现P的再利用.综上所述,该MgO-BC复合材料在净化实际P污染水体中有着广阔的应用前景.
[Abstract]:Magnesium oxide based biomass carbon (MgOBC) composites were prepared on the surface of biomass carbon (BC) by magnesium oxide (MgO) (MgO) supported on the surface of biomass carbon (BC) under the condition of high temperature and oxygen limit by using peanut shell as precursor. The adsorption characteristics of P in water by MgO-BC were systematically studied, and the effects of pH value, contact time and competitive ion on the adsorption of P were discussed. The results show that the optimal adsorption initial pH of P is 7 ~ 9, and the environment of superacid and superalkali is not conducive to the equilibrium of P adsorption process within 540 min, and the kinetic curves are in good agreement with pseudo-first-order and pseudo-second-order kinetic models. The fitting coefficients can reach 97.3% and 99.0.When the concentration of Cl~-,HCO_3~-,NO_3~- and other coexisting ions reaches 10 times of that of P, the adsorption process of P with MgO-BC still has a strong adsorption capacity. The adsorption process of P is in good agreement with the Langmuir isothermal model. The theoretical maximum adsorption capacity is 138.07 mg g ~ (-1), which is much higher than that of other unmodified or modified biomass carbon and several typical P adsorbents. In addition, the composite material after P adsorption can be applied into soil as fertilizer, which can effectively realize the reuse of P. In conclusion, the MgO-BC composite has a broad application prospect in purifying P polluted water.
【作者单位】：浙江工业大学环境学院;
【基金】：国家自然科学基金青年科学基金项目(51508511) 浙江省自然科学基金项目(LR16E080003,LQ15E080011)
【分类号】：X52

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