在上游的持续过程验证4.0应用:在皮奇亚牧羊细胞工厂的低氧生物过程中,由AI管理的适应性实现
Arnau Gasset1, Joeri Van Wijngaarden2, Ferran Mirabent2
1Department of Chemical, Biological, and Environmental Engineering, School of Engineering, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain.
Frontiers in bioengineering and biotechnology
|October 17, 2024
概括
这项研究表明,人工智能和物联网等工业4.0技术如何实现生物过程验证的自适应控制. 一个人工智能辅助的控制策略显著提高了实时生物过程监测的准确性和精度.
科学领域:
- 生物技术和生物工艺工程
- 工业生物技术 工业生物技术
- 过程分析技术 (PAT) 是一种分析技术.
背景情况:
- 工业4.0技术,包括云计算,物联网 (IoT),边缘计算和人工智能 (AI),对于现代化生物制药制造至关重要.
- 持续过程验证 (CPV) 对于确保在过程验证 (PV) 的最后阶段保持一致的产品质量至关重要.
- 传统的控制策略往往缺乏复杂生物过程所需的适应性.
研究的目的:
- 开发和验证人工智能辅助的适应比例控制 (AI-APC) 战略,用于实时生物过程控制.
- 评估AI-APC与手动启发式控制 (MHC) 和布尔逻辑控制器 (BLC) 策略的性能.
- 展示工业4.0技术在生物制药上游运营中的适应性视觉的整合.
主要方法:
- 一个数字双胞胎 (DT) 创建了一个 *Pichia pastoris* 生物过程产生 *Candida rugosa* 脂酶 1.
- 实施了在线传感器,以弥合微生物和人工智能模型的边缘和云预测.
- 人工智能模型被用来模拟微生物代谢,并根据呼吸系数 (RQ) 控制关键过程参数.
主要成果:
- 人工智能-APC策略的准确性 (平均相对误差) 低于4%,超过了MHC (10%) 和BLC (5%).
- 精度,以根平均平方偏差 (RMSD) 衡量,是AI-APC的最高,表明性能优越.
- 与MHC.相比,AI-APC策略的复制性得到了改善.
- 数字双胞胎和人工智能模型在低氧,碳有限的养批次条件下成功模拟了生物过程代谢.
结论:
- 人工智能,物联网和边缘计算的整合使生物制药上游过程中的有效自适应控制成为可能.
- 人工智能APC策略在传统控制方法上取得了显著的进步,提高了准确性,精度和可重复性.
- 这项研究验证了工业4.0原则的应用,用于生物制造中的自适应过程验证和控制.
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