Abstract
The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.
Highlights
Pharmaceutical manufacturing processes have traditionally employed the batch operation mode, in which fixed amounts of raw materials are run through different unit operations to obtain the final drug product
Quality attributes of the final drug product were originally tested at the end of each batch processing step, where quality control essentially followed a quality-by-testing approach (QbT) [1], e.g., mixing uniformity is tested at the conclusion of the blending process
Since there is limited implementation of nonlinear model predictive control strategies for the continuous pharmaceutical manufacturing industry, a main objective of this work is to develop and present a moving horizon estimation-based nonlinear model predictive control (MHE-Nonlinear model predictive control (NMPC)) framework to serve the dual requirement of accurate estimation and effective control
Summary
Pharmaceutical manufacturing processes have traditionally employed the batch operation mode, in which fixed amounts of raw materials are run through different unit operations to obtain the final drug product. Since there is limited implementation of nonlinear model predictive control strategies for the continuous pharmaceutical manufacturing industry, a main objective of this work is to develop and present a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework to serve the dual requirement of accurate estimation and effective control. It is important to note that once non-conforming quality attributes have been identified, a long-term goal is the integration of control frameworks similar to the MHE-NMPC structure with residence time distribution (RTD)-based modeling frameworks that are currently being developed to guide tablet product diversion in the continuous pharmaceutical manufacturing industry and truly enhance and enable smart manufacturing operations [39,40]. The primary objective of this work is to develop and present a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework to serve the dual requirement of accurate estimation and effective control, and to demonstrate its practical applicability by discussing its implementation feasibility in controlling a rotary tablet press.
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