适应性闭环控制系统,用于优化平板电脑制造工艺
Xiaorong Luo1,2,3, Zhijian Zhong2,3, Pan Deng2,3
1China Resources Jiangzhong Pharmaceutical Group Co., Ltd., Nanchang 330096, China.
Pharmaceutics
|December 31, 2025
概括
一个新的代学习模型预测控制 (IL-MPC) 系统通过使用历史数据来改进重量和硬度控制来增强平板电脑制造. 这种数据驱动的方法优化了制药制造业的生产一致性和效率.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 工艺控制工程 工艺控制工程
- 数据驱动优化数据驱动优化
背景情况:
- 平板电脑制造面临着动态过程参数合,材料变化和延迟质量控制的挑战,影响平板电脑的重量稳定性.
- 高速生产加剧了药片重量和硬度的变化,由于传统静态优化方法的局限性,影响了效率.
研究的目的:
- 开发一个数据驱动的闭环控制系统,以提升平板电脑制造工艺.
- 为满足在高速生产环境中改善平板电脑重量和硬度稳定性的关键需求.
主要方法:
- 提出了一种新的代学习模型预测控制 (IL-MPC) 架构,将代学习约束集成到模型预测控制 (MPC) 框架中.
- 启用了历史批量数据的系统利用,同时通过约束嵌入保持实时优化功能.
- 通过结合学习和优化,通过降低计算负载实现了增强的批量对批量性能一致性.
主要成果:
- 通过模拟和工业生产数据验证了IL-MPC算法的可行性.
- 证明了对动态工艺变化的有效管理,显著提高了对平板电脑重量和硬度的控制精度.
- 与传统的控制方法相比,在管理平板电脑质量属性方面表现出卓越的性能.
结论:
- 克服了平板电脑制造动态调节的技术限制.
- 建立了一个可重复的闭环控制范式,用于智能制药制造.
- 促进了行业向数据驱动型号和先进智能药物生产的转变.
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