Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

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.

Integrated Continuous Crystallization–-Spherical Agglomeration (CCSA) Process for the Intensified Manufacturing of Atorvastatin Calcium

“…This change results in the creation of new surfaces and more contact points between particles, leading to improved interparticle bonding and, consequently, enhanced strength. ,, The tablets start expanding in size after ejection from the die due to viscoelastic relaxation of the tablet. Thus, elastic recovery was calculated by using the expression ϵ = ρ in‐ die − ρ tablet ρ in‐ die where ρ in‑die is the in-die relative density of the tablet and ρ tablet is the out-of-die relative density of the tablet. The elastic recovery of the tablets, as shown in Figure b, is lower for the agglomerates than for the crystals.…”

Expanding the Horizons of Manufacturing, towards Wide Integration, Smart System, and Tools

“…The paper by M. Ziyan Sheriff et al [2] also examines the transition from batch to continuous processes in the pharmaceutical industry. In order to enable the quality-bycontrol (QbC) paradigm to move forward, this work developed and presented a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework to accomplish the dual requirement of accurate estimation and efficient control.…”

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

“…The related methodologies can roughly be divided into two categories of model-based control (MBC) and model-free control (MFC) depending on whether predictive models are used. As a typical representative of MBC, model predictive control (MPC) has found broad application in PQC studies for various pharmaceutical process units, such as crystallization, blending, , hot-melt extrusion, , continuous tableting, − etc. However, such studies generally rely on process mechanism models, and the real-time decision making are seriously hampered by whose modeling complexities and analytical solutions.…”