Liu Chong, Balasubramanian Paramasivan, Li Fayong, Huang Haiming
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China; Department of Chemical & Materials Engineering, University of Auckland, 0926, New Zealand.
Department of Biotechnology & Medical Engineering, National Institute of Technology Rourkela, 769008, India.
J Hazard Mater. 2024 Dec 5;480:135853. doi: 10.1016/j.jhazmat.2024.135853. Epub 2024 Sep 16.
In response to escalating global wastewater issues, particularly from dye contaminants, many studies have begun using hydrochar to adsorb dye from wastewater. However, the relationship between the preparation conditions of hydrochar, the properties of hydrochar, experimental conditions, types of dyes, and equilibrium adsorption capacity (Q) has not yet been fully explored. This study conducted a comprehensive assessment using twelve distinct ML models. The Gradient Boosting Regressor (GBR) model exhibited superior performance with R² (0.9629) and RMSE (0.1166) in the test dataset, marking it as the most effective among the evaluated models. Moreover, this study also proved the feasibility of the GBR model through stability testing and residual analysis. A feature importance analysis prioritized the variables as follows: experimental conditions (41.5 %), properties of hydrochar (26.0 %), preparation conditions (18.1 %), and type of dye (14.4 %). Meanwhile, experimental conditions (C > 30 mmol/g, pH > 8, and higher solvent temperatures) and hydrochar properties (the BET surface area > 2000 m²/g, an (O+N)/C molar ratio < 0.6, and an H/C molar ratio of approximately 0.06) show higher Q for dyes. Experimental validation of the GBR model confirmed its practical utility with a suitable predictive accuracy (R² = 0.8704). Moreover, the study developed a Python-based GUI that has integrated the best GBR models to facilitate researchers' ongoing application and improvement of this predictive model. This study not only underscores the efficacy of ML in enhancing the understanding of dye adsorption by hydrochar but also sets a precedent for future research on sustainable contaminants removal through bio-based adsorbents.
针对全球范围内不断升级的废水问题，特别是来自染料污染物的问题，许多研究已开始使用水炭从废水中吸附染料。然而，水炭的制备条件、水炭的性质、实验条件、染料类型与平衡吸附容量（Q）之间的关系尚未得到充分探索。本研究使用12种不同的机器学习模型进行了全面评估。梯度提升回归器（GBR）模型在测试数据集中表现出卓越的性能，R²为0.9629，RMSE为0.1166，在评估模型中最为有效。此外，本研究还通过稳定性测试和残差分析证明了GBR模型的可行性。特征重要性分析对变量的优先级排序如下：实验条件（41.5%）、水炭性质（26.0%）、制备条件（18.1%）和染料类型（14.4%）。同时，实验条件（C>30 mmol/g、pH>8和较高的溶剂温度）和水炭性质（BET表面积>2000 m²/g、（O+N）/C摩尔比<0.6和H/C摩尔比约为0.06）对染料显示出更高的Q值。GBR模型的实验验证证实了其具有合适预测准确性（R² = 0.8704）的实际效用。此外，该研究开发了一个基于Python的图形用户界面，集成了最佳的GBR模型，以方便研究人员持续应用和改进此预测模型。本研究不仅强调了机器学习在增强对水炭吸附染料理解方面的功效，还为未来通过生物基吸附剂去除可持续污染物的研究树立了先例。
