Xiang Xin, Jia Dongmei, Yang Zongzheng, Jiang Fuguo, Yang Tingting, Cao Jingguo
College of Marine and Environmental Sciences, Tianjin University of Science & Technology, Tianjin, 300457, China.
College of Marine and Environmental Sciences, Tianjin University of Science & Technology, Tianjin, 300457, China; College of Chemical Engineering and Material Science, Tianjin University of Science & Technology, Tianjin, 300457, China.
Environ Res. 2025 Jan 15;265:120466. doi: 10.1016/j.envres.2024.120466. Epub 2024 Nov 26.
In this study, artificial neural network (ANN) and random forest (RF) were constructed to predict the Cd adsorption capacity of Fe-modified biochar. The RF model outperformed ANN model in accuracy and predictive performance (R = 0.98). Through the contribution factors analysis of SHAP, structural characteristics (55.44%) were most important of Fe composite-modified biochar (CBC). And CBC have the best adsorption performance when C, Fe, O, H, N, and pH content were <50%, 10-20%, 10-20%, 0.5-1%, 0-2%, and >10, respectively. The Fe-Ca modified biochar (FeCa-BC) of different raw materials (wheat straw, corn straw and walnut shell) were successfully prepared according to the ML results, and the experimental data of FeCa-BC verified the accurate predictive ability of RF model (R = 0.89). The developed GUI toolbox results showed that the error between predicted and actual values was less than 5% based on the training set, testing set, and experimental validation set. The analysis of FTIR, XRD and XPS indicated that surface complexation, precipitation, and ion exchange were the main Cd adsorption mechanisms of FeCa-BC. This work presents new insights for the targeted preparation of functional biochar and its application in contaminated water through ML.
在本研究中，构建了人工神经网络（ANN）和随机森林（RF）来预测铁改性生物炭对镉的吸附能力。RF模型在准确性和预测性能方面优于ANN模型（R = 0.98）。通过SHAP贡献因子分析，结构特征（55.44%）是铁复合改性生物炭（CBC）最重要的因素。当C、Fe、O、H、N和pH含量分别<50%、10-20%、10-20%、0.5-1%、0-2%和>10时，CBC具有最佳吸附性能。根据机器学习结果成功制备了不同原料（小麦秸秆、玉米秸秆和核桃壳）的铁钙改性生物炭（FeCa-BC），FeCa-BC的实验数据验证了RF模型的准确预测能力（R = 0.89）。开发的GUI工具箱结果表明，基于训练集、测试集和实验验证集，预测值与实际值之间的误差小于5%。FTIR、XRD和XPS分析表明，表面络合、沉淀和离子交换是FeCa-BC吸附镉的主要机制。这项工作为通过机器学习有针对性地制备功能性生物炭及其在污染水体中的应用提供了新的见解。
