Removal of antibiotics through fixed bed adsorption: Comparison of different breakthrough curve models

Fixed bed adsorption serves as an effective method for eliminating organic micropollutants, including antibiotics, from water. To evaluate the performance of fixed bed adsorbers, researchers typically rely on the use of breakthrough curve models. Of particular significance is the accuracy of these models in predicting the time at which a specific breakthrough concentration occurs. In this study, we conducted a comparative analysis of four distinct breakthrough curve models. Within this set of models, the Thomas and Yan models are well-established, while the log-Gompertz and Chu-Hashim models are more recent additions to the field. The log-Gompertz model is essentially empirical, whereas the Chu-Hashim model represents a modified version of the Thomas model. The Chu-Hashim model incorporates the concept of steric hindrance, accounting for the decline in the rate of adsorption as the adsorber approaches saturation. Our modeling study revolved around the adsorptive removal of antibiotics, specifically flumequine and tetracycline. The findings revealed that both the Thomas and Yan models provided poor correlations to the antibiotic breakthrough data. Furthermore, they consistently underestimated the time at which a specific breakthrough concentration was reached. The Thomas model performed the worst in this respect. In contrast, both the log-Gompertz and Chu-Hashim models correlated well with the experimental breakthrough data, with the Chu-Hashim model consistently delivering superior correlations. Additionally, the breakthrough times predicted by these recently introduced models closely aligned with the experimental times, showcasing their practical utility and superiority over the widely used Thomas and Yan models.

Automated summary

This study compared four breakthrough curve models and found that the log-Gompertz and Chu-Hashim models performed well in predicting antibiotic breakthrough data, showing strong correlations and accurate breakthrough time predictions.