在干颗粒处理过程中使用灰盒模型整合机器学习模型和约翰逊模型进行规模独立的固体分数预测
Kanta Sato1, Shuichi Tanabe2, Keita Yaginuma2
1Formulation Technology Research Laboratories, Daiichi Sankyo Co., Ltd., 1-12-1, Shinomiya, Hiratsuka 2540014 Kanagawa, Japan; Department of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku 6068501 Kyoto, Japan.
这项研究引入了一种新的灰色盒模型,用于预测滚筒压缩中的固体分数. 该模型克服了约翰逊模型的局限性,通过估计关键的预巩固性质,改善产品质量控制.
科学领域:
- 制药工程 制药工程
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 固体分量的预测对于控制滚筒压缩中的产品质量至关重要.
- 约翰逊模型,一个第一原则的方法,需要不可测量的预凝固特性来进行滚筒紧缩.
- 现有的方法缺乏在不同的滚筒压缩条件下准确预测固体分量的能力.
研究的目的:
- 开发一种新的灰盒 (混合) 模型,用于预测滚筒压缩后的固体分数.
- 通过结合可测量的参数来克服约翰逊模型的局限性.
- 为不同的配方和设备提供一种强大且可扩展的固体分数预测方法.
主要方法:
- 开发了一个统计模型,使用材料和工艺数据预测一种新的预整合参数.
- 将统计模型与约翰逊的第一原则模型集成,创建一个灰色盒子模型.
- 验证了模型在各种滚动速度和条件的性能,包括高吞吐量.
主要成果:
- 统计模型成功地预测了预巩固参数,而不需要滚筒紧缩实验.
- 灰盒模型准确地预测了固体分数,即使在有粉末速度梯度的条件下也是如此.
- 该模型在不同的尺度和配方中展示了稳定性和适用性.
结论:
- 拟议的灰盒模型在预测滚筒压缩过程中的固体分数方面取得了重大进展.
- 这种方法提高了产品质量控制,通过使准确的预测没有复杂的实验设置.
- 该模型的可扩展性和适应性使其成为制药和材料加工的宝贵工具.
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