Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation,Journal of Hazardous Materials

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Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation
Journal of Hazardous Materials ( IF 12.2 ) Pub Date : 2024-09-16 , DOI: 10.1016/j.jhazmat.2024.135853
Chong Liu ₁ , Paramasivan Balasubramanian ₂ , Fayong Li ₃ , Haiming Huang ₄

Affiliation

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 (C0 > 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.

中文翻译：

水炭染料吸附机器学习预测：参数优化与实验验证

为了应对不断升级的全球废水问题，尤其是染料污染物问题，许多研究已经开始使用 Hydrochar 吸附废水中的染料。然而，水炭的制备条件、水炭的性质、实验条件、染料种类和平衡吸附能力（Q）之间的关系尚未得到充分探索。本研究使用 12 种不同的 ML 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优异的性能，R² （0.9629）和 RMSE （0.1166）是评估模型中最有效的。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如下：实验条件（41.5 %）、水炭的性质（26.0 %）、制备条件（18.1 %）和染料类型（14.4 %）。同时，实验条件（C0 > 30 mmol/g，pH > 8 和更高的溶剂温度）和水炭特性（BET 表面积 > 2000 m²/g，（O+N）/C 摩尔比 < 0.6，H/C 摩尔比约为 0.06）显示染料的 Q 值较高。GBR 模型的实验验证证实了其实际效用，具有适当的预测准确性（R² = 0.8704）。此外，该研究开发了一个基于 Python 的 GUI，该 GUI 集成了最好的 GBR 模型，以促进研究人员持续应用和改进该预测模型。这项研究不仅强调了 ML 在增强对水炭吸附染料的理解方面的功效，而且为未来通过生物基吸附剂可持续去除污染物的研究开创了先例。

更新日期：2024-09-16

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