整合机器学习和CFD用于模拟用膜分离工艺处理水中的质量转移
Kamal Y Thajudeen1, Mohammed Muqtader Ahmed2, Saad Ali Alshehri3
1Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, 62529, Saudi Arabia. kthajudeen@kku.edu.sa.
Scientific reports
|October 13, 2024
概括
机器学习模型可以准确地预测膜分离过程中的溶液度. 多层感知器 (MLP) 模型表现出卓越的性能,为分离科学中的预测建模提供了宝贵的见解.
科学领域:
- 膜分离科学 膜分离科学
- 计算流体动力学 计算流体动力学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确预测溶液度 (C) 分布对于优化膜分离过程至关重要.
- 传统的方法经常与这些系统中空间数据的复杂性作斗争.
研究的目的:
- 评估各种机器学习模型在基于空间坐标的溶液度分布预测方面的有效性.
- 为了比较不同的回归算法的性能,包括内核回归 (KRR),半径最近邻居回归 (RNN),K-最近邻居 (KNN),LASSO和多层感知器 (MLP).
主要方法:
- 运用计算流体动力学 (CFD) 与用于过程模拟的机器学习相结合.
- 使用哈里斯·霍克斯优化 (HHO) 来微调模型的超参数.
- 评估了多重回归模型,包括KRR,RNN,KNN,LASSO和MLP.
主要成果:
- 多层感知器 (MLP) 模型实现了最高的精度,测试R2值为0.98637.
- 在培训和测试数据集上,MLP表现出强大的概括能力,在训练和测试数据集上表现一致.
- 在MLP模型中观察到低根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 值.
结论:
- 机器学习回归方法,特别是MLP,对于在膜分离中从空间坐标估计溶液度是有效的.
- 该研究强调了混合模型的潜力,将CFD和机器学习集成到分离科学中的预测空间数据分析中.
- 这些发现为推进基于膜的分离技术的预测建模提供了新的视角.
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