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实验和数值研究,以提高药品颗粒的颗粒控制和质量预测

Maroua Rouabah¹, Inès Esma Achouri¹, Sandrine Bourgeois²

¹Group of Research on Technologies and Processes GRTP, Université de Sherbrooke, Sherbrooke, QC J1N 0J8, Canada.

|March 27, 2025

概括

一个新的离散元素方法 (DEM) 模型模拟湿颗粒,预测颗粒质量. 一个人工智能工具增强了分析,提高了制药制造的精度和可重复性.

科学领域:

制药制造业制药制造业制药制造业
化学工程是化学工程的重要组成部分.
材料科学材料科学材料科学

背景情况:

制药行业需要严格控制可复制湿颗粒的产品特性.
开发系统模型对于理解和优化颗粒加工过程至关重要.

研究的目的:

开发和验证一个离散元素方法 (DEM) 模型来模拟湿颗粒.
整合人工智能 (AI) 工具,以加强流程分析和颗粒质量评估.

主要方法:

使用DEM模拟粒子行为,考虑材料特性,设备几何和操作条件.
纳入粒子相互作用和凝聚力来预测颗粒形成和质量.
使用EDEMpy人工智能工具进行聚合物大小分布的处理后分析.

主要成果:

DEM模型准确地复制了来自高剪切颗粒器的实验颗粒生长动力学.
人工智能工具有效地监测和分析颗粒大小分布,以评估过程效率.
验证证实了数值模拟和实验数据之间的强烈一致.

结论:

开发的DEM模型显示了在制药湿颗粒中预测和控制颗粒质量的巨大潜力.
这种方法提高了制药生产的精度和效率.
整合人工智能工具进一步支持流程优化和质量保证.

关键词:

离散元素方法是离散元素方法.高剪切颗粒机的高剪切颗粒机建模建模模型是什么数字颗粒化的数值.颗粒大小分布粒子大小分布制药制造制药制造制药制造制药制造扩大规模的研究.

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