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The pharmaceutical industry is looking for new and innovative ways of manufacturing to improve product quality and reduce process complexity. In manufacturing oral solid dosage products, blending is a crucial step in ensuring the homogeneity of active pharmaceutical ingredients (APIs) in the final product. Currently, batch and continuous blending are the two commonly used modes for blending in the industry. However, these methods have limitations in terms of blending time, manual intervention, and flexibility in handling multiple ingredients. To address these limitations, this study aims to explore the feasibility and benefits of using a semicontinuous blending mode in the pharmaceutical industry. A case study is conducted using a binary blend of microcrystalline cellulose and acetaminophen to compare the performance of the semicontinuous mode of blending with the batch and continuous blending modes. The results show that the semicontinuous blending setup can produce blends with good blend uniformity and homogeneity and that the output can be used for both batch and continuous downstream operations. The effect of variation in the three most important process parameters, impeller rotation per minute, blending time, and fill level on the blend uniformity, is also investigated. The semicontinuous blending mode had a higher line rate of 12.5 kg/hour than a similarly sized batch blender at 3.6 kg/hour and less than that of a continuous blender. The benefits of the new blending mode include reduced blending time, minimal manual intervention, flexibility in blending multiple ingredients, easier scale-up, and a smaller footprint. Overall, this study highlights the relative advantages of using this new semicontinuous blending mode in pharmaceutical manufacturing and its potential as a good alternative to the existing blending modes. The semicontinuous mode is well placed between the batch blending and continuous blending mode, with many benefits over the former mode and performance comparable to the latter continuous mode. 

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.