测试连续生产的美芬胺酸-通过模拟和过程优化设计实验的设计
Kai Eivind Wu1, Cameron J Brown2, Murray Robertson2
1School of Electrical and Electronic Engineering, University of Sheffield, Sheffield, United Kingdom.
概括
这项研究优化了使用进化算法和替代模型的连续制药制造,比传统的胺酸生产方法取得了58%的更好的结果.
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.
- 工艺系统工程 工艺系统工程
- 机器学习 机器学习
背景情况:
- 连续制造在制药中比批量生产具有优势,包括灵活性和质量.
- 在连续制造中同时优化多个子流程的研究仍然不足.
- 通过湿 (WM) 和混合悬浮混合产品清除 (MSMPR) 生产美胺酸作为一个案例研究.
研究的目的:
- 探索和优化连续制药生产流程,特别是胺酸合成.
- 将数据驱动的进化优化算法应用于多目标优化问题 (MaOPs).
- 开发一个强大的框架,用于制药过程优化,使用集成的高保真和替代模型.
主要方法:
- 使用通用流程建模系统 (gPROMS) 进行高保真模型生成的数据.
- 开发了基于辐射基函数神经网络 (RBFNN) 的替代模型,以实现更快的模拟.
- 采用进化优化算法,用于WM和MSMPR子进程的基于模型的流程优化.
主要成果:
- 证明了整合高保真和替代模型的可行性,以优化流程.
- 获得了近似的解决方案,这些解决方案平均比拉丁式超立方体采样更好58%.
- 在未来的实验活动中确定了适合参数设置的最佳解决方案.
结论:
- 使用代理模型的多目标优化 (MaOP) 方法对于连续制药生产是有效的.
- 这项研究为优化复杂的制药制造流程提供了一个新的框架.
- 研究结果强调了机器学习在提高制药生产效率和质量方面的潜力.
相关概念视频
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