Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning

This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R-2 = 0.9625) compared to ANN (R-2 = 0.9410), GBDT (R-2 = 0.9152), and XGBoost (R-2 = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm(3)& BULL;g(-1) and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

Automated summary

This study aimed to develop a predictive model for tetracycline (TC) adsorption onto biochar (BC) using machine learning techniques. Four machine learning algorithms were used to model the adsorption of TC on BC using experimental data. The RF model had the highest predictive accuracy compared to other models. Factors such as specific surface area and particle size of BC were found to affect TC adsorption efficiency. The TC-to-BC ratio and concentration gradient were identified as influential factors. The developed predictive model can facilitate the selection of BC for TC wastewater treatment.