制药喷雾干燥的最新发展:建模,过程优化和机器学习的新兴趋势

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Waasif Wahab¹, Raya Alshamsi¹, Bouta Alharsousi¹

¹Department of Chemical and Petroleum Engineering, United Arab Emirates University, Sheikh Khalifa Bin Zayed Street, Al-Ain 15551, United Arab Emirates.

|December 31, 2025

概括

喷雾干燥建模正在推进机器学习 (ML) 和计算流体动力学 (CFD). 结合ML和CFD的混合模型显示出优化制药喷雾干燥过程的前景.

科学领域:

制药技术制药技术制药技术
化学工程是化学工程的组成部分.
计算建模计算建模

背景情况:

喷雾干燥对于制造制药粉末至关重要,过程参数显著影响产品质量.
像计算流体动力学 (CFD) 这样的传统建模方法在复杂的喷雾干燥场景中面临精度和计算成本的限制.

研究的目的:

审查制药应用喷雾干燥工艺建模方面的进展和挑战.
探索机器学习 (ML) 和混合建模方法的潜力.

主要方法:

审查现有的关于喷雾干燥建模技术的文献.
计算流体动力学 (CFD) 局限性的分析.
机器学习 (ML) 模型的评估,以优化喷雾干燥.
讨论新兴的混合ML-CFD模型,数字双胞胎,转移学习和可解释AI (XAI).

主要成果:

虽然CFD模型已经建立,但它存在一些局限性,包括高计算成本和在复杂条件下的准确性问题.
机器学习模型为喷雾干燥提供了有希望的准确性,但需要高质量的数据,并可能与新参数预测作斗争.
结合ML和CFD的混合模型,以及数字双胞胎和XAI等技术,正在成为强大的工具.

结论:

机器学习展示了一种新兴的技术,用于增强制药喷雾干燥过程.
结合ML和CFD的混合建模方法提供了一条克服个体方法局限性的途径.
对数字双胞胎和XAI等先进技术的进一步研究有必要,以优化制药喷雾干燥.

关键词:

在 CFD 交易中,我们可以看到 CFD.美国FDA/EMA的指导方针在XAI,XAI就是XAI.数字双胞胎数字双胞胎是什么意思药物输送是药物输送的过程.混合ML模型的混合ML模型机器学习是机器学习.单滴滴建模单滴滴建模喷雾干燥喷雾干燥转移学习转移学习

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