Green Intelligence Environmental School, Yangtze Normal University, No. 16 Juxian Road, Fuling, Chongqing, 408100, China; School of Ecology and Environment, Inner Mongolia University, No. 235 West University Road, Saihan, Hohhot, 010021, China.
School of Ecology and Environment, Inner Mongolia University, No. 235 West University Road, Saihan, Hohhot, 010021, China.
Chemosphere. 2022 Jun;296:134021. doi: 10.1016/j.chemosphere.2022.134021. Epub 2022 Feb 18.
Continuous removal of toxic element boron from aqueous solution was investigated with new phenolic hydroxyl modified resin (T-resin) using a fixed bed column reactor operated under various flow rates, bed height and influent concentrations. The breakthrough time, exhaustion time and uptake capacity of the column bed increased with increasing column bed height, whereas decreased with increasing influent flow rate. The breakthrough time and exhaustion time decreased, but uptake capacity increased with increasing influent concentration, and actual uptake capacity was obtained as 6.52 mg/g at a concentration of 7.64 mg/L. The three conventional models of bed depth service time (BDST), Thomas and Yoon-Nelson were used to appropriately predict the whole breakthrough behavior of the column and to estimate the characteristic model parameters for boron removal. However, artificial neural network (ANN) model was more accurate than the conventional models with the least relative error and the highest correlation coefficients. By the relative importance of the operational parameters obtained from ANN model, the sequence is as follows: total effluent time > initial concentration > flow rate > column height. The adsorption capacity of boron was changed between 5.24 and 1.74 mg/g during the five time regeneration. From the life factor calculation, it is suggested that the column bed could avoid the breakthrough time of t = 0 for 6.8 cycles, whereas, the uptake capacity would be zero after 7.8 cycles.
采用新型酚羟基改性树脂(T-树脂)在固定床柱反应器中,研究了不同流速、床层高度和入口浓度下从水溶液中连续去除有毒元素硼的情况。随着床层高度的增加,穿透时间、耗尽时间和柱床的吸附容量增加,而随着入口流量的增加则减少。随着入口浓度的增加,穿透时间和耗尽时间减少,而吸附容量增加,在浓度为 7.64mg/L 时,实际吸附容量为 6.52mg/g。采用床层深度接触时间(BDST)、Thomas 和 Yoon-Nelson 三种常规模型对整个柱穿透行为进行了适当预测,并对硼去除的特征模型参数进行了估计。然而,人工神经网络(ANN)模型比传统模型更准确,相对误差最小,相关系数最高。通过从 ANN 模型获得的操作参数的相对重要性,其顺序如下:总流出时间>初始浓度>流速>柱高。在五次再生过程中,硼的吸附容量在 5.24 和 1.74mg/g 之间变化。从寿命因子的计算来看,建议柱床可以避免 t=0 的穿透时间达到 6.8 个周期,而在 7.8 个周期后,吸附容量将为零。