Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology (DU), Pune, India.
Mechanical Engineering Department, Indian Institute of Technology Jodhpur, Jodhpur, India.
Environ Monit Assess. 2023 Jul 24;195(8):984. doi: 10.1007/s10661-023-11599-7.
Machine learning (ML) models have become a potent tool for advancing environmentally conscious research in materials science, allowing the prediction of wastewater treatment efficacy using eco-materials. In this study, we showcase the potential of an advanced decision tree-based ensemble learning algorithm to model the eviction of emerging organophosphate-based pesticidal pollutants in aqueous systems. The model is trained using laboratory-based biochar adsorption data, and it establishes the relationship between independent experimental factors and the % organophosphate pesticide adsorption efficiency as the output parameter. We classified the experimental dataset into input and output parameters to build the model. The input parameters included pyrolysis temperature, solution pH, surface area, pore volume, and initial pesticide concentration. Grid search optimization in Python was employed to train the model using sets of input-output patterns. The results indicated that the XGBoost-based ensemble ML framework provides the best forecast for pesticide adsorption on the biochar matrix, achieving high scores for the regularization coefficient (R = 0.998, R = 0.981). The concentration of the organophosphorus compound and the morphology of biochar significantly influenced the pesticide adsorption behavior. These findings demonstrate the potential of using ensemble learning algorithms for the balanced design of carbon-enriched biomaterials to remove emerging micropollutants from water effectively.
机器学习 (ML) 模型已成为推动环境意识材料科学研究的有力工具，可用于预测使用生态材料进行废水处理的效果。在这项研究中，我们展示了一种先进的基于决策树的集成学习算法在模拟水系统中新兴有机磷类农药污染物驱逐方面的潜力。该模型使用基于实验室的生物炭吸附数据进行训练，并建立了独立实验因素与 %有机磷农药吸附效率之间的关系，后者作为输出参数。我们将实验数据集分类为输入和输出参数来构建模型。输入参数包括热解温度、溶液 pH 值、表面积、孔体积和初始农药浓度。使用输入-输出模式集在 Python 中进行网格搜索优化来训练模型。结果表明，基于 XGBoost 的集成机器学习框架为生物炭基质上的农药吸附提供了最佳预测，正则化系数的得分较高（R = 0.998，R = 0.981）。有机磷化合物的浓度和生物炭的形态显著影响了农药的吸附行为。这些发现表明，使用集成学习算法对于平衡设计富含碳的生物材料以有效去除水中的新兴微污染物具有潜力。
