模型预测控制
过程分析技术
医药制造业
关键质量属性
设计质量
工艺工程
过程(计算)
自动化
质量(理念)
过程控制
工程类
计算机科学
制造工程
可靠性工程
在制品
控制(管理)
运营管理
机械工程
人工智能
生物信息学
哲学
认识论
生物
操作系统
下游(制造业)
作者
Morgane Jelsch,Yves Roggo,Mark D. Brewer,Zsolt-Adam Géczi,Philipp Heger,Peter Kleinebudde,Markus Krumme
标识
DOI:10.1016/j.powtec.2023.118936
摘要
Pharmaceutical continuous manufacturing provides the appropriate tools (e.g. the understanding of process dynamics and appropriate and adaptable control strategy) in order to deal with Quality-by-Design expectations and even to the future smart manufacturing described by Quality-by-Control. Those tools form part of the given framework of the regulatory agencies led by an effective quality risk management. Soft sensors and control algorithms such as model predictive control are stepping stones for more agile processes and increased robustness by keeping the quality attributes of the final drug product in their acceptable ranges and by mitigating undesired events. The implementation of a model predictive control (MPC) system on a pharmaceutical continuous manufacturing plant for the wet granulation process is described. The control objectives and strategy are presented as well as the selected variables, the process dynamics identification, the MPC performance and its specific tuning where a commercial software has been used, setting the framework of this study. MPCs have been applied successfully on two pharmaceutical drug products (Diclofenac and Paracetamol): an accurate control of the API content and of the LOD was achieved in order to produce a constant quality of tablets on both drug products. In addition, some of the process parameters have been identified as mandatory to be step tested for each change of drug product, leading to a simplified MPC implementation.
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