Estimation of tetracycline antibiotic photodegradation from wastewater by heterogeneous metal-organic frameworks photocatalysts.

In this work, the potential ability of various modern and powerful machine learning methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient-Boosted Decision Trees (GBDT), Extra Tree (ET), Decision Trees (DT), and Random Forest (RF) were investigated to estimate tetracycline (TC) photodegradation from wastewater by 10 different metal-organic frameworks (MOFs). A comprehensive databank was gathered, including 374 data points from the photodegradation percentage of MOFs in various practical conditions. The inputs of the employed models were chosen as catalyst dosage, antibiotic concentration, Illumination time, solution pH, and specific surface area and pore volume of the investigated MOFs, and the output was TC degradation efficiency. Different statistical criteria were calculated for the validation of the developed models. Average absolute percent relative error (AAPRE) and standard deviation error (STD) values of 1.19% and 0.0431, 3.07% and 0.0628, 2.88% and 0.0751, 2.86% and 0.1304, 8.73% and 0.2751, 4.24% and 0.1024, 2.83% and 0.0934, and 11.56% and 0.4459 were obtained for CatBoost, LightGBM, XGBoost, AdaBoost, GBDT, ET, DT, and RF approaches, respectively. Among all implemented models, the CatBoost was found to be the most trustable model. Moreover, this model followed the expected trends of the TC degradation process with variation of catalyst dosage, initial TC concentration, and reaction pH. The developed CatBoost model predicted the removal of TC by MOFs accurately, which proved the capability of this approach in solving complex problems with numerous data points and its straightforwardness and cost-effectiveness for environmental applications.