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<p>This study synthesized plastic waste-carbon nanomaterial (PW-CN) and evaluated its effectiveness in removing tetracycline (TC) from water. Characterization of the material was carried out with FTIR, BET, UV-Vis, PXRD, FESEM, and HRTEM. Machine learning models of Box Behnken design (BBD) and artificial neural networks (ANNs) coupled with non-linear regression analysis of kinetics and isotherms were employed to optimize and understand the adsorption process. Results indicate a BBD optimized time of 24.73&#xa0;min, at a 3.84 pH, 0.052&#xa0;g dosage, and concentration of 29.79&#xa0;mg/L with a removal efficiency of 93.96% through the desirability function. The ANN approach predicted the optimal conditions for TC removal with removal efficiencies of 93.67%. The pseudo-second order kinetic model with a correlation coefficient (R<sup>2</sup> = 0.9948) described the mechanism to occur by chemisorption. The Langmuir model gave a maximum adsorption capacity (q<sub>max</sub>) of 123.93&#xa0;mg/g. The data also show that the Freundlich model, with a high correlation coefficient (R<sup>2</sup> = 0.9771), described the mode of adsorption of TC by PW-AC to occur on multilayer energetically stable heterogeneous surfaces. The study presents a theoretical approach of using machine learning and mathematical models to describe the adsorption of TC onto PW-CN.</p>

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Modeling of tetracycline removal from water using plastic waste-carbon nanomaterial: a study based on machine learning and mathematical models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月13日

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  • Simon Bbumba,
  • Ibrahim Karume,
  • Joan Talibawo,
  • Gabriel Kasozi,
  • George William Nyakairu,
  • Muhammad Ntale,
  • Geofrey Kaddu,
  • Ivan Kiganda,
  • Ruth Mbabazi,
  • Moses Kigozi

摘要

This study synthesized plastic waste-carbon nanomaterial (PW-CN) and evaluated its effectiveness in removing tetracycline (TC) from water. Characterization of the material was carried out with FTIR, BET, UV-Vis, PXRD, FESEM, and HRTEM. Machine learning models of Box Behnken design (BBD) and artificial neural networks (ANNs) coupled with non-linear regression analysis of kinetics and isotherms were employed to optimize and understand the adsorption process. Results indicate a BBD optimized time of 24.73 min, at a 3.84 pH, 0.052 g dosage, and concentration of 29.79 mg/L with a removal efficiency of 93.96% through the desirability function. The ANN approach predicted the optimal conditions for TC removal with removal efficiencies of 93.67%. The pseudo-second order kinetic model with a correlation coefficient (R2 = 0.9948) described the mechanism to occur by chemisorption. The Langmuir model gave a maximum adsorption capacity (qmax) of 123.93 mg/g. The data also show that the Freundlich model, with a high correlation coefficient (R2 = 0.9771), described the mode of adsorption of TC by PW-AC to occur on multilayer energetically stable heterogeneous surfaces. The study presents a theoretical approach of using machine learning and mathematical models to describe the adsorption of TC onto PW-CN.