Kulkarni Om, Dongare Priya, Shanmughan Bhavana, Nighojkar Amrita, Pandey Shilpa, Kandasubramanian Balasubramanian
Indian Space Research Organization, Banglore, India.
Defence Institute of Advanced Technology (DU), Pune, India.
Environ Monit Assess. 2025 Feb 1;197(2):223. doi: 10.1007/s10661-025-13664-9.
Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However, improper disposal of dyes into wastewater has raised major concerns due to their harmful effects on plants, animals, and humans. Using engineered carbon systems (ECSs) to treat dye-contaminated wastewater has shown promise for sustainable waste management. Dye adsorption on ECSs is a complex, non-linear process, making it essential to understand ECSs' dye removal capabilities through a modeling framework that includes experimental and environmental factors. To support this, a database of ECSs used in dye removal from textile wastewater was compiled. Twelve machine learning models, including XGBoost, Light Gradient Boost, Random Forest, Gradient Boost, CatBoost, AdaBoost, Decision Tree, Artificial Neural Network, K-Nearest Neighbor, Support Vector Machine, Huber, and Ridge Regressor, were applied to analyze ECSs' dye removal potential. Out of all the models, XGBoost exhibited the highest coefficient of determination (R) of 0.986 during the training and 0.978 during testing, alongside the lowest prediction error (MSE) of 0.01 and 0.136 in the training phase and testing phase. The quantity of ECS, concentration of dye (C), and pH of wastewater highly influenced the adsorption process. The optimization results indicated the highest affinity of direct, reactive, and dispersed dyes towards ECSs in the acidic solution. In contrast, the maximum adsorption of Basic and VAT dye on ECSs was found in the alkaline solution. The partial dependence analysis provided valuable insights into the interaction between ECS dose and water matrix parameters that can lead to efficient extraction of dyes from aqueous matrices.
染料广泛应用于印刷、化妆品、造纸、皮革加工、纺织和制造业等行业，为产品增添色彩。然而，由于染料对植物、动物和人类具有有害影响，将其不当排放到废水中已引发了重大关注。使用工程碳系统（ECS）处理受染料污染的废水已显示出在可持续废物管理方面的潜力。染料在ECS上的吸附是一个复杂的非线性过程，因此有必要通过一个包含实验和环境因素的建模框架来了解ECS的染料去除能力。为了支持这一点，编制了一个用于去除纺织废水中染料的ECS数据库。应用了包括XGBoost、Light Gradient Boost、随机森林、梯度提升、CatBoost、AdaBoost、决策树、人工神经网络、K近邻、支持向量机、Huber和岭回归器在内的12种机器学习模型，来分析ECS的染料去除潜力。在所有模型中，XGBoost在训练期间的决定系数（R）最高，为0.986，在测试期间为0.978，同时在训练阶段和测试阶段的预测误差（MSE）最低，分别为0.01和0.136。ECS的用量、染料浓度（C）和废水的pH值对吸附过程有很大影响。优化结果表明，直接染料、活性染料和分散染料在酸性溶液中对ECS的亲和力最高。相比之下，碱性染料和还原染料在碱性溶液中对ECS的吸附量最大。偏依赖分析为ECS剂量与水基质参数之间的相互作用提供了有价值的见解，这有助于从水基质中高效提取染料。
