Residence Time Distribution (RTD)-Based Control System for Continuous Pharmaceutical Manufacturing Process
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During continuous manufacturing, there may be some out of specification tablets that need to be diverted in real time, in order to ensure the quality of the final product. Specifically, the content uniformity of each tablet must be guaranteed before it can be released to market. However, currently, no methods or tools are available that can assure the content uniformity and divert the non-confirming products in real time. The aim of this work is to develop and evaluate a strategy to divert the non-confirming tablets in real time and thereby assure drug concentration of final tablets.
This work has been conducted in silico using a combination of MATLAB and Simulink. A methodology to implement a residence time distribution (RTD)-based control system for drug concentration-based tablet diversion which uses the convolution integral was developed and implemented in MATLAB. Comparisons between the performance of “fixed window” and “RTD-based” approaches for diversion have also been presented and used to assess optimal usability.
In this work, two novel strategies namely, “fixed window approach” and “RTD-based approach” have been developed and evaluated for real-time diversion of non-confirming tablets. The RTD-based control system was designed, developed, and implemented in silico. A framework for its implementation in a real-time system has also been elaborated on. This methodology was compared to an alternative fixed window approach. The proposed control system is analyzed for various manufacturing scenarios, systems, and disturbances.
A comparison of the two proposed strategies suggests that the “RTD-based control system” is more efficient in every simulated scenario. The relative performance is best when the disturbances in the system are characterized by short pulse-like changes.
KeywordsResidence time distribution RTD Control Continuous manufacturing Quality by design Continuous pharmaceutical manufacturing
This work is supported by the Rutgers Research Council, through grant 202342 RC-17-Singh R; the US Food and Drug Administration (FDA), through grant 11695471; and the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through grant NSF-ECC 0540855.
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