Huang Yan-Shu, Sheriff M Ziyan, Bachawala Sunidhi, Gonzalez Marcial, Nagy Zoltan K, Reklaitis Gintaras V
Davidson School of Chemial Engineering, Purdue University, West Lafayette, IN 47907, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Int Symp Process Syst Eng. 2022;49:2149-2154. doi: 10.1016/b978-0-323-85159-6.50358-4.
Active control strategies play a vital role in modern pharmaceutical manufacturing. Automation and digitalization are revolutionizing the pharmaceutical industry and are particularly important in the shift from batch operations to continuous operation. Active control strategies provide real-time corrective actions when departures from quality targets are detected or even predicted. Under the concept of Quality-by-Control (QbC), a three-level hierarchical control structure can be applied to achieve effective setpoint tracking and disturbance rejection in the tablet manufacturing process through the development and implementation of a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework. When MHE is coupled with NMPC, historical data in the past time window together with real-time data from the sensor network enable model parameter updating and control. The adaptive model in the NMPC strategy compensates for process uncertainties, further reducing plant-model mismatch effects. The frequency and constraints of parameter updating in the MHE window should be determined cautiously to maintain control robustness when sensor measurements are degraded or unavailable. The practical applicability of the proposed MHE-NMPC framework is demonstrated via using a commercial scale tablet press, Natoli NP-400, to control tablet properties, where the nonlinear mechanistic models used in the framework can predict the essential powder properties and provide physical interpretations.
主动控制策略在现代制药生产中起着至关重要的作用。自动化和数字化正在彻底改变制药行业，并且在从间歇操作向连续操作的转变中尤为重要。当检测到甚至预测到偏离质量目标时，主动控制策略会提供实时纠正措施。在控制质量（QbC）概念下，可以应用三级分层控制结构，通过开发和实施基于移动时域估计的非线性模型预测控制（MHE-NMPC）框架，在片剂制造过程中实现有效的设定值跟踪和干扰抑制。当MHE与NMPC相结合时，过去时间窗口中的历史数据以及来自传感器网络的实时数据可实现模型参数更新和控制。NMPC策略中的自适应模型可补偿过程不确定性，进一步降低工厂模型失配效应。在传感器测量值下降或不可用时，应谨慎确定MHE窗口中参数更新的频率和约束条件，以保持控制鲁棒性。通过使用商业规模的压片机Natoli NP-400来控制片剂特性，证明了所提出的MHE-NMPC框架的实际适用性，其中框架中使用的非线性机理模型可以预测基本的粉末特性并提供物理解释。
