Immobilization of Marbofloxacin for Water Treatment by Adsorption in Batch Scale and Fixed-Bed Column: Applying of Monte Carlo Bayesian Modeling

Abstract

Pharmaceutical molecules identified in the wastewater belong to several classes of antibiotics such as marbofloxacin (MBX). This study focuses on the immobilization of MBX by adsorption in batch scale and a fixed-bed column of granular activated carbon (GAC). The effects of significant operational parameters, such as adsorbent dosage, contact time, and pH, on the adsorption efficiency were investigated. After this, a fixed-bed column study was carried out to evaluate the behavior of breakthrough curves, adsorption capacity, and bed saturation. Additionally, a novel strategy was incorporated that uses Monte Carlo Bayesian modeling to improve the ability to predict the most suitable conditions of the process. Results showed a high efficiency of MBX removal by the methods used where the most suitable conditions for batch scale were at natural pH, a GAC mass of 1.5 g, and 60 min of contact time for about 80% removal. Regarding fixed bed column experiments that were evidenced, the impact of the initial MBX concentration on the breakthrough curve impacted the adsorbent’s saturation rate, generating changes in the concentration gradient. In addition to the experimental data analysis, the model applied to simulate the breakthrough curves showed be robust, and it was capable of making good predictions, given that the lowest coefficient of determination (R2) was 0.85. The mass balance model used was validated and was able to make good predictions when compared with the experimental data, so it can be used to make inferences about the breakthrough curve under different operating conditions within the range in which the experiments were carried out.