(591f) Model-Based Dynamic Optimization of Feeder Refill Strategy Used in Continuous Pharmaceutical Manufacturing Process

The process model is in the heart of 'digital twin initiative' of the continuous pharmaceutical manufacturing process [1-3]. The process model can be used to optimize the process and design and tune the control system. The feeder is the first and one of the most crucial unit operations of the continuous manufacturing of pharmaceutical products. It directly influences the drug content of the tablets and thereby, patient safety. The feeder needs to be refilled very frequently for continuous manufacturing, and its performance depends significantly on the refill strategy followed. The feeder refill strategy involves three operational parameters that need to be identified before using the feeder for continuous manufacturing. These parameters are ‘feeder mass at which the refill needs to be started,’ ‘refill size,’ and ‘refill rate.’ If the refill strategy is not correctly optimized, then the overall quality of the product will be compromised. Presently, all the settings involved in refill strategies are being experimentally determined using a heuristic approach, which is time and resource-intensive and often leads to suboptimal solution. Therefore, a systematic dynamic optimization framework is needed through which the feeder refill strategy can be optimized.

In this work, a systematic framework including the methods and tools has been developed for dynamic optimization of the feeder refill strategies. The feeder unit operation has been modeled using ‘gPROMS’ FormulatedProducts software (PSE),’ and the optimization method has been developed in MATLAB (Mathworks). Both tools are communicating through the gO:MATLAB toolbox (PSE). The deviation of the outlet mass flow of the feeder from the targeted flow rate has been minimized to obtain the optimum value of the feeder refill parameters. Once optimal refill strategy has been obtained then it has been transferred to distributed control system (DCS) to implement it into the continuous pharmaceutical manufacturing pilot-plant [4]. The material properties also affect the refill strategy meaning that the feeder refill strategy need to be frequently optimized if there are any changes in the materials and plant. Therefore, the developed feeder model and dynamic optimization tool can save the time and recourses significantly. The heuristic approach currently used in industry could also lead to suboptimal refill strategy and thereby poor feeder performance and poor product quality. The proposed method can save the time and resources needed for continuous manufacturing as well as can improve the product quality significantly.

The objective of this presentation is two-fold; first to highlight the developed systematic framework for model-based dynamic optimization of the feeder refill strategy and then demonstrate its application for continuous pharmaceutical manufacturing process.

References

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2. Singh, R. (2018). System engineering for a novel continuous pharmaceutical manufacturing process. Pharma. Issue 30. https://www.pharmafocusasia.com/manufacturing/system-engineering-pharmac....
3. Barros, F. N., Bhaskar, A., Singh, R. (2017). A validated model for design and evaluation of control architectures for continuous tablet compaction process. Processes Journal, 5(4), 76. doi:10.3390/pr5040076
4. Singh, R., Sahay, A., Fernando Muzzio, Ierapetritou, M., Ramachandran, R. (2014). A systematic framework for onsite design and implementation of the control system in continuous tablet manufacturing process. Computers & Chemical Engineering Journal, 66, 186-200. http://dx.doi.org/10.1016/j.compchemeng.2014.02.029