Removal of Sulfadimethoxine in Aqueous Solution by Adsorption on Mesoporous Carbon/Titania Composites: Batch Scale, Fixed-Bed Column, and Bayesian Modeling

Abstract

The antibiotic sulfadimethoxine (SMX) has been persistent among various pharmaceuticals in water sources. The purpose of this work is to investigate SMX removal through adsorption using four different meso-carbonaceous materials or composites based on mesoporous carbon/titania (C-meso; C-meso/TiO₂; CN-meso; CN-meso/TiO₂). The adsorption efficiency of the materials and the influence of the main operational parameters, such as adsorbent dosage, contact time, and pH, were investigated. Moreover, a fixed-bed column process was also conducted to simulate a continuous-flow scenario and evaluate the material’s adsorbent performance under practical conditions, resulting in the best outcomes. Breakthrough curves obtained from the column were assessed by the Monte Carlo Bayesian method, enhancing the accuracy and robustness of the adsorption process model. Outcomes show that the material with better results was C-meso/TiO₂, achieving the maximum quantity of SMX adsorbed in equilibrium (q_e) of 50 mg_SMX /g for 100% of SMX removed. Fixed-bed column outcomes show a broad range of maximum adsorption capacities (5.80 to 132.94 mg of SMX/g). Bayesian statistical methods identified the Yan and log-Gompertz models as the most appropriate with R² values of 0.94 and 0.95, respectively. The study demonstrated that C-meso/TiO₂ is a highly effective adsorbent for SMX removal in both batch and continuous systems. The results highlight the critical role of optimizing operational parameters to maximize the adsorption efficiency. The Bayesian statistical approach provided robust models for predicting breakthrough behaviors, which are essential for scaling up the process.