基于可设计绿色材料的混合物的表面张力估计的洞察力,使用集体学习方案
Reza Soleimani1, Amir Hossein Saeedi Dehaghani2
1Department of Chemical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, P.O. Box 14115-143, Tehran, Iran.
Scientific reports
|August 29, 2023
概括
随机梯度增强 (SGB) 准确地模拟了离子液体混合物的表面张力. SGB模型的性能优于其他方法,在工业过程设计中显示出高可靠性.
科学领域:
- 物理化学 物理化学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 准确的物理性质估计对于设计涉及离子液体 (IL) 的工业过程至关重要.
- 表面张力是IL及其混合物的关键性质,影响其工业应用.
- 了解IL混合物的表面张力对于过程优化至关重要.
研究的目的:
- 评估随机梯度提升 (SGB) 树模型在预测二元离子液体混合物的表面张力方面的有效性.
- 将SGB模型的性能与其他各种机器学习技术进行比较.
- 确定影响IL混合物表面张力预测的关键变量.
主要方法:
- 使用了48个IL和20个非IL组件的4010个实验数据点的综合数据集.
- 应用了随机梯度增强 (SGB) 树算法来建模表面张力.
- 与SVM,GA-SVM,GA-LSSVM,CSA-LSSVM,GMDH-PNN,ANN,ANFIS和MGGP模型相比,SGB模型的性能进行了比较.
- 采用威廉的情节来评估模型的适用性领域.
主要成果:
- 该SGB模型显示了高精度,相关系数 (R) >0.999和平均相对绝对误差<0.004.
- 在预测表面张力方面,SGB显著超过了所有其他测试模型.
- 离子液体的分子分数被确定为对表面张力预测最有影响力的变量.
- 威廉的图表表明,98.5%的数据点属于模型的适用范围.
结论:
- SGB模型提供了一种可靠和准确的方法来预测离子液体混合物的表面张力.
- 拟议的SGB模型具有广泛的应用领域,确保对工业应用进行有效和可靠的预测.
- 这些发现支持使用SGB来优化涉及离子液体混合物的过程.
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