基于预测度分布和质量转移的制药干燥过程的机器学习分析和模拟
Khaled Almansour1, Hashem O Alsaab2
1Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia. kh.almansour@uoh.edu.sa.
Scientific reports
|November 3, 2025
概括
这项研究介绍了一种混合模型,将质量转移和机器学习结合起来,用于制药冷化. 使用龙算法优化的支持向量回归 (SVR) 可以准确预测低温干燥过程中的化学度.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 制药科学 制药科学
- 计算机建模 计算建模
背景情况:
- 解冷化 (冷干燥) 对于制药化合物的稳定性至关重要.
- 在干燥过程中准确预测度对于过程优化至关重要.
- 传统的建模方法可能无法有效地捕捉复杂的动态.
研究的目的:
- 开发和评估一种混合模型来分析制药冷化.
- 为了比较回归 (RR),支向量回归 (SVR) 和决策树 (DT) 模型的预测精度.
- 使用龙算法 (DA) 优化机器学习模型.
主要方法:
- 采用了融合质量转移原理和机器学习的混合模型.
- 研究了三种机器学习模型 (RR,SVR,DT),用于预测3D空间 (X,Y,Z) 中的度 (C).
- 使用龙算法 (DA) 进行了超参数优化.
主要成果:
- 支持向量回归 (SVR) 模型显示出优异的预测性能.
- SVR获得了0.999234的R2测试分数和0.999187的R2列车分数,表明了出色的概括性.
- 低根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 值证实了该模型的高精度.
结论:
- 龙算法优化的SVR模型提供了一个非常准确和可靠的方法来预测化学度.
- 这种方法对化学工程和需要精确的工艺控制的相关领域有价值.
- 混合模型推进了对低温干燥过程的理解和优化.
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