Fixed Bed Column Modeling of Cd(II) Adsorption on Bone Char Using Backward Bayesian Multiple Linear Regression

In this work, the backward Bayesian multiple linear regression (BBMLR) as a new approach is presented to predict the adsorption efficiency (AE) of Cd(II) ions by ostrich bone char (OBC) in the fixed bed adsorption with four operational variables consisting of pH (2-9), inlet Cd(II) concentration (C-o=25-100 mg/L), bed depth (h=3-9 cm) and feed flow rate (Q=0.5-30 mL/min). The performance of the BBMLR was evaluated using the coefficient of determination (R-2), normalized root means square error (NRMSE) and mean residual error (MRE). The AE of Cd(II) ions by OBC increased from 42.3% to 99.9% when pH was increased from 2 to 9 with h of 6 cm and Q of 1.5 mL min(-1). It was found that the AE dramatically increased from 70.5% to 99.9% with decreasing Q from 30 to 0.5 mL min(-1) at pH of 7 and h of 6 cm. At pH=7 and Q=1.5 mL min(-1), the AE increased from 71.9% to 100% when h increased from 3 to 9 cm. The BBMLR model presented excellent performance (NRMSE=6.69%) for predicting Cd(II) removal in a continuous adsorption system, although it gave a slight underestimation (approximately 3.52%). The BBMLR is more sensitive to the pH, followed by h and Q, while the C-o has no significant effect on it. This research displays that OBC has great potential as an eco-friendly low-cost adsorbent in removing Cd(II) ions from the contaminated waters.