The abuse of antibiotics poses a threat to the ecological environment and biological health, and how to effectively reduce the residue of tetracycline (TC) in the environment has attracted much attention. In this study, three types of pristine biochar (BCs: PBC300, PBC500, and PBC700) were prepared using agricultural waste shiitake mushroom bran at different pyrolysis temperatures to remove TC from water. The structure and surface chemistry of the adsorbents were characterized using different analytical techniques such as scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and thermogravimetric analysis. These changes in physicochemical properties improve the adsorption capacity of BC. The PBC300 and PBC500 conform to the Langmuir isothermal adsorption model, while the PBC700 is more compatible with the Freundlich model. According to the fitting results of the Langmuir isotherm model, the maximum saturated adsorption capacities of PBC300, PBC500 and PBC700 for TC were 7.568 mg/g, 14.994 mg/g and 17.684 mg/g, respectively. The correlation coefficients of the pseudo-second-order kinetic models were 0.9882, 0.9882 and 0.9996, respectively, which could well fit the adsorption process of TC by the three BCs, indicating that chemical adsorption was dominant. With the help of machine learning, the relationship between the physicochemical properties of BC and the adsorption capacity of TC was effectively explored. The random forest model was able to fit the adsorption process of BC on TC better. It is expected that this study will guide the rational application of BC in the treatment of TC wastewater.
Keywords: Antibiotic; Biochar; Machine learning; Sorptive removal; Water pollution.
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