基于人工智能技术的不同数学模型的应用,以预测溶解物通过聚合物膜的度分布
Jowaher Alanazi1, Mohammad M Algahtani2, Muteb Alanazi3
1Department of Pharmacology and Toxicology, College of Pharmacy, University of Ha'il, Ha'il 81442, Saudi Arabia.
本研究引入了一种机器学习方法,使用Adaboost与K-Nearest Neighbors来预测膜净化中的溶液度. 增强KNN实现了最高的准确性,证明了其优化治疗剂分离的潜力.
科学领域:
- 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:
- 基于膜的净化是治疗剂的关键新兴技术,在传统方法上具有优势.
- 需要进一步的研究来评估聚合物膜用于去除杂质的操作可行性.
研究的目的:
- 使用机器学习开发数值策略,以预测膜分离中的溶液度分布.
- 为此预测任务评估不同机器学习模型的性能.
主要方法:
- 使用了Adaboost (自适应增强) 算法,使用K-最近邻 (KNN),线性回归 (LR) 和高斯过程回归 (GPR) 作为基础学习者.
- 采用BA优化算法来对自适应增强模型进行超参数调整.
- 分析了两个输入变量 (r,z) 来预测目标输出 (C),使用超过8000个数据点.
主要成果:
- 提升KNN,提升LR和提升GPR的R平方得分分别为0.9853,0.8751和0.9793.
- 增强型KNN模型表现出卓越的性能,并被确定为最适合的模型.
- 增强KNN模型表现出较低的错误率,MAE为2.073e1和MAPE为1.06e-2.
结论:
- 开发的基于Adaboost的机器学习策略,特别是Boosted KNN,对于预测膜分离过程中的溶液度非常有效.
- 这种方法为优化聚合物膜在治疗剂净化中的操作可行性提供了一个有希望的数值工具.
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