通过使用质量转移模型分析和计算机器学习设计膜,将有机分子从水中分离出来
Suranjana V Mayani1, Hessan Mohammad2,3, Soumya V Menon4
1Department of Chemistry, Faculty of Science, Marwadi University Research Center, Marwadi University, Rajkot, 360003, Gujarat, India. suranjana.mayani@marwadieducation.edu.in.
集成机器学习准确预测膜分离系统中的化学度. 极端随机树 (ET) 在从水中去除杂质方面表现出卓越的性能.
科学领域:
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 膜分离系统对于净水至关重要,但准确预测化学度分布是具有挑战性的.
- 了解质量转移动态对于优化杂质去除过程至关重要.
- 计算流体动力学 (CFD) 和机器学习为模拟这些复杂系统提供了强大的工具.
研究的目的:
- 调查整体机器学习方法对预测膜分离中的化学度分布的有效性.
- 评估和比较四种基于树的算法:决策树 (DT),极端随机树 (ET),随机森林 (RF) 和基于直方图的梯度增强回归 (HBGB).
- 为了优化模型的超参数,使用连续减半方法来有效地分配资源.
主要方法:
- 使用超过25,000个数据点的数据集,以辐射 (r) 和轴 (z) 位置作为输入.
- 采用了四种整体机器学习算法:DT,ET,RF和HBGB.
- 使用连续减半算法进行了超参数优化.
- 使用R平方 (R2),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标验证模型性能.
主要成果:
- 极端随机树 (ET) 模型实现了最高的性能,R2值为0.99674.4.
- 在ET模型中,低RMSE为37.0212mol/m3和MAE为19.6784mol/m3.
- 所有研究的集体机器学习模型都在模拟分离过程中表现出显著的能力.
结论:
- 集成机器学习技术对于在膜工程中准确预测溶液度概况非常有效.
- ET模型对膜分离过程的实时监控和优化具有特别的希望.
- 这项研究突出了先进的计算方法在增强水处理技术方面的潜力.
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