摘要
研究了涂铁多孔陶瓷填料柱对水中亚甲基蓝(MB)的动态吸附性能。探讨了填料高度、MB初始质量浓度和流速对穿透曲线的影响;分别用BDST模型、Thom as模型和线性回归对动态吸附实验数据拟合,获得了相关参数,并研究了填料的再生性能。结果表明,涂铁多孔陶瓷(IOCPC)能有效去除水中MB,填料层升高,穿透曲线上的穿透点向右移动,穿透时间延长;而随流速、MB初始质量浓度的增大,穿透曲线上的穿透点向左移动,穿透时间缩短。用BDST模型能准确预测穿透时间,误差<5%;用Thom as模型可较好地描述了MB浓度为10和50 mg/L、初速为2 mL/m in时IOCPC对MB的吸附动力学,相关系数分别为0.99和0.93,平衡吸附容量分别为0.078和0.13 mg/g。对吸附饱和后的涂铁多孔陶瓷可用pH=3的硝酸再生,重复使用3次穿透曲线上的穿透点基本不变。
In this study, the capability of iron-oxide coated porous ceramics （IOCPC） to adsorb methylene blue （MB） from aqueous solution was investigated in a fixed-bed column. The effects of important parameters on breakthrough curve, such as the filler height, the flow rate and the initial mass concentration of MB, were stud- ied. The BDST and Thomas models were applied to simulate column adsorption data and to gain the characteristic parameters of the two models using linear regression. At the same time, the regeneration of saturated IOCPC was studied. The results showed that IOCPC as an adsorbent to remove MB from solution was efficient. When the fill- er height increasing, the breakthrough point in the breakthrough curves moved to right, this suggested the extension of breakthrough time. But with increase of the flow rate and the initial concentration of MB, the break- through point moved to left, which implied the decrease of breakthrough time. The breakthrough time at different flow rate and initial concentration was forecasted accurately by the BDST model （ error 〈 5% ） , and that the Thomas model was found suitable for describing the column adsorption kinetics at flow rate 2 mL/ rain, initial concentration of MB 10 and 50 mg/L（The correlation coefficients were equal to 0.99 and 0.93, and the balance adsorption capacity was 0. 078 and 0. 13 mg/g）. Regeneration of saturated IOCPC came true using nitric acid （pH = 3） , and the breakthrough point in the breakthrough curves remained basically unchanged after three cycles of regeneration-adsorption.
出处
《环境工程学报》
CAS
CSCD
北大核心
2009年第3期385-390,共6页
Chinese Journal of Environmental Engineering
基金
国家自然科学基金资助项目(20577008)
湖南科技大学博士基金资助项目(E58109)
