In the pharmaceutical industry, fulfilling the Food and Drug Administration (FDA) requirements for product quality is one of the main concerns . In order to assure product quality, batch operation is the most used manufacturing mode . During batch production, the quality of the batch subject to operator action and quality is often controlled by statistical sampling and rejection of an entire batch if sampling indicates deviations from specifications. This leads to significant amounts of wasted material and significant overall cost . The cost for manufacturing goods represent a large part of the products sales. For instance, the manufactured goods cost represent twenty-seven percent of the patented products sales, while for generic products the percentage is fifty percent . This cost can be minimized by implementing improvements in the manufacturing process . One of the possible improvements is to evolve from batch operations to continuous processing. Continuous manufacturing and on-line process monitoring have the potential to improve product quality and reduce manufacturing cost; the potential annual savings across the industry are projected to be as high as fifty billion dollars . In 2002 the FDA initiated Process Analytical Technology (PAT). The goal of PAT is to augment the understanding and control the manufacturing process [6,7]. Through the implementation of QbD and PAT, the companies have to demonstrate understanding of how the operating conditions, process design and raw material variability affect the product quality . Control design plays a significant role for continuous manufacturing implementation. It is by implementing appropriate process control strategies that poor quality product manufacturing can be mitigated or prevented . By implementing Quality by Control (QbC), it is possible to reduce the operation cost, reduce the off-spec or segregated material and improve the safety environment.
In this contribution a generic framework is proposed for design and analysis of a hierarchical feedback control scheme for continuous manufacturing. The framework proposes three control levels that have to be considered in the control design. Level 0 includes the built-in control systems of the individual unit operations, in Level 1 PAT is used in simple feedback control schemes (property feedback control – PFC) to control critical quality attributes (CQAs), whereas level three uses dynamic models for prediction and advanced control schemes, such as model predictive control (MPC) for enhanced control performance. Control performance indicators suitable for the comparative analysis of various control schemes are also proposed. The potential impacts of using level 1 property feedback control (PFC) and level 2 model predictive control on product quality and process performance are analyzed using the proposed performance indicators. The proposed generic framework is exemplified using as a model system the feeder and blender unit operations. The control schemes are evaluated under realistic disturbance and measurement scenarios, at high and low active pharmaceutical ingredient (API) content.
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