(26g) Evaluation of Local Solvers for Flexibility Analysis of Dynamic Pharmaceutical Processes
We have demonstrated that flexibility analysis is a practical tool for analyzing the design space of steady-state pharmaceutical processes. We developed two tailored flexibility analysis approaches and compared them with the traditional Monte Carlo sampling approach in terms of computational cost and quality of the computed probabilistic design space. In this presentation, we will show that such flexibility analysis approaches can reduce computational time by as much as two orders of magnitude in certain cases. We wish to extend these concepts to dynamic models and analyze the practicality of utilizing similar techniques to mitigate the large cost of identifying the design space of dynamic pharmaceutical processes.
In this presentation, an example of batch pharmaceutical process will be analyzed to identify the probabilistic design space. Batch systems inherently require the consideration of process dynamics which increases the model scale significantly. Moreover, providing solutions with provable guarantees requires global solution of the flexibility analysis formulations, and all of these methods become computationally challenging on large-scale problems. To overcome these computational challenges, we extend these approaches to include a progressive refinement strategy based on relaxations that produces a sequence of inner and outer bounding regions. We compare this approach with both Monte Carlo techniques and local solution strategies coupled with simulation-based testing on boundaries of the design space.