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改性花生壳对水中镉的动态吸附研究
摘要点击 1390  全文点击 1206  投稿时间:2011-11-20  修订日期:2012-03-29
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中文关键词  花生壳    吸附  固定床  穿透曲线
英文关键词  peanut shell  cadmium  adsorption  fixed-bed  breakthrough curve
作者单位E-mail
龙腾 华南理工大学环境科学与工程学院, 广州 510006 longteng524@yahoo.cn 
易筱筠 华南理工大学环境科学与工程学院, 广州 510006
工业聚集区污染控制与生态修复教育部重点实验室, 广州 510006 
xyyi@scut.edu.cn 
党志 华南理工大学环境科学与工程学院, 广州 510006
工业聚集区污染控制与生态修复教育部重点实验室, 广州 510006 
 
中文摘要
      采用高锰酸钾改性花生壳吸附剂对镉离子进行固定床吸附实验,考察了床层高度(30~50 cm)、初始离子浓度(0.55~11.00 mg·L-1)、进料流速(15.11~37.00 mL·min-1)等操作参数对镉吸附特性的影响,同时对吸附穿透曲线进行拟合. 实验结果表明,改性花生壳固定床对水中镉具有较好的吸附效果,在吸附操作初期,吸附柱出水镉离子浓度几乎为0(<0.001 mg·L-1),吸附操作时间根据不同的操作条件可达2~62 h,镉离子总去除率均大于54%. 传质区长度主要受初始离子浓度、进料流速影响. 床层高度的增加使得穿透时间增加,但传质区长度几乎保持不变; 初始离子浓度和进料流速增加,穿透时间缩短,传质区长度增加. 在低浓度条件下,BDST模型实验穿透曲线拟合效果较好(R2>0.99),运用该模型能准确预测吸附柱的操作时间.
英文摘要
      Peanut shells modified by potassium permanganate were used as absorbents for cadmium (Ⅱ) removal in a fixed bed. The effects of influencing parameters such as bed height (ranging between 30 and 50 cm), initial concentration (ranging between 0.55 and 11.00 mg· L-1) and influent flow rate (ranging between 15.11 and 37.00 mL· min-1) were studied and the corresponding breakthrough curves were obtained. This result indicated that the column packed with modified peanut shells had good adsorption properties for cadmium (Ⅱ) removal. In the initial adsorption stage cadmium (Ⅱ) outflow concentration was less than 0.001 mg·L-1. According to different operating conditions the operation time reached 2-62 h and the removal rate was above 54%. The height of mass transfer zone was primarily driven by initial concentration and influent flow rate. The height of mass transfer zone kept substantially unchanged and the breakthrough time increased with the increase of bed height. The height of mass transfer zone increased and the breakthrough time decreased with the elevated initial concentration and influent flow rate. The bed depth service time (BDST) model was used to fit the experiment data resulting in a good effect with R2>0.99 under low concentration. The operation time can be accurately predicted using the BDST model.

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