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探索人工智能的潜力,作为一个促进工具,在流化床处理器的配方开发:一个全面的审查

Aachal A Gosavi¹, Tanaji D Nandgude², Rakesh K Mishra³

¹Department of Pharmaceutics, Dr. D. Y. Patil Institute of Pharmaceutical Sciences and Research, Pimpri, Pune, India.

AAPS PharmSciTech

|May 13, 2024

概括

人工智能 (AI) 通过提高理解,降低成本和提高质量来增强流化床过程 (FBP) 配方开发. 挑战包括数据,可解释性和法规,但人工智能提供了显著的效率增长.

科学领域:

制药科学制药科学
化学工程是化学工程的重要组成部分.
计算科学计算科学

背景情况:

流化床过程 (FBP) 是复杂的,有许多变量使得配方开发和优化具有挑战性.
传统的方法很难有效地导航FBP的复杂参数空间.

研究的目的:

探索人工智能 (AI) 在流化床过程 (FBP) 中简化配方开发的应用.
在FBP中确定用于智能系统开发的AI技术和策略.
评估将人工智能整合到FBP制定中的好处和挑战.

主要方法:

审查各种人工智能技术,包括机器学习,神经网络,遗传算法和模糊逻辑.
人工智能与实验设计,过程建模和优化策略的整合.
分析了在FBP中展示AI应用的案例研究.

主要成果:

人工智能集成带来了更好的流程理解,减少了开发时间和成本,提高了产品质量.
在决策,过程结果预测和扩展方面展示了成功的AI应用.
主要优势包括强大的配方优化和提高效率.

结论:

人工智能为提高 FBP 配方开发的效率,质量和成本效益提供了巨大的潜力.
解决与数据可用性,模型解释性和监管合规性相关的挑战对于成功实施人工智能至关重要.
未来的研究应该专注于充分利用人工智能来推进FBP的制定.

关键词:

人工智能的人工智能是人工智能.人工神经网络的人工神经网络流体床处理器流体床处理器过程分析技术过程分析技术

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