Yang Minge, Yue Qiqing, He Junyi
Department of Science and Technology, Hunan Automotive Engineering Vocational University, Zhuzhou, 412001, Hunan, China.
Sci Rep. 2025 Jun 3;15(1):19477. doi: 10.1038/s41598-025-04324-6.
In this study, we utilized a comprehensive dataset comprising over 19,000 data points with inputs represented by coordinates (x, y) and the corresponding output denoted as concentration (C). The case study was analysis of mesoporous material for adsorption separation of target solute from aqueous solution. Mass transfer and machine learning evaluations were carried out to obtain separation efficiency. Our objective was to develop predictive models for C using three distinct base models: decision tree (DT), support vector regression (SVR), and Gaussian process regression (GPR). To enhance the predictive performance of these base models, we employed the ensemble method AdaBoost, which combines their outputs to yield more accurate predictions. Hyper-parameter optimization was achieved through particle swarm optimization, allowing us to fine-tune the models for optimal results. The results of our experiments demonstrate promising performance across the ensemble models. Specifically, AdaBoost combined with decision tree (ADA-DT) yielded an impressive R of 0.96984 and a mean squared error (MSE) of 7.9407E+00. AdaBoost combined with SVR (ADA-SVR) achieved an even higher R score of 0.97148 with an MSE of 7.6362E+00, while AdaBoost combined with GPR (ADA-GPR) produced a commendable R score of 0.95963 with an MSE of 8.16352E+00.
在本研究中，我们使用了一个包含超过19000个数据点的综合数据集，其输入由坐标（x，y）表示，相应的输出记为浓度（C）。案例研究是对用于从水溶液中吸附分离目标溶质的介孔材料进行分析。进行了传质和机器学习评估以获得分离效率。我们的目标是使用三种不同的基础模型开发C的预测模型：决策树（DT）、支持向量回归（SVR）和高斯过程回归（GPR）。为了提高这些基础模型的预测性能，我们采用了集成方法AdaBoost，它将它们的输出组合起来以产生更准确的预测。通过粒子群优化实现了超参数优化，使我们能够对模型进行微调以获得最佳结果。我们的实验结果表明，整个集成模型都具有良好的性能。具体而言，AdaBoost与决策树相结合（ADA-DT）产生了令人印象深刻的R值0.96984和均方误差（MSE）7.9407E+00。AdaBoost与SVR相结合（ADA-SVR）实现了更高的R分数0.97148，MSE为7.6362E+00，而AdaBoost与GPR相结合（ADA-GPR）产生了值得称赞的R分数0.95963，MSE为8.16352E+00。
