Removal of boron by a modified resin in fixed bed column: Breakthrough curve analysis using dynamic adsorption models and artificial neural network model

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.

Automated summary

This study investigated the continuous removal of toxic element boron from aqueous solution using a new phenolic hydroxyl modified resin (T-resin). The results showed that the breakthrough time, exhaustion time, and uptake capacity of the column bed increased with increasing column bed height and decreased with increasing influent flow rate. The artificial neural network (ANN) model was found to be more accurate in predicting the breakthrough behavior of the column compared to the conventional models. The adsorption capacity of boron changed during five time regeneration.