A surrogate‐based framework for feasibility‐driven optimization of expensive simulations

Abstract

AbstractSurrogate‐based optimization approaches have been widely adopted in industrial problems due to their potential to reduce the number of simulation runs required in the optimization process. The surrogate‐based optimization framework has been extended to feasibility analysis in pharmaceutical manufacturing to characterize the design space. Most surrogate‐based approaches for feasibility analysis are limited to the construction of a regression model for the feasibility function. In this work, we developed a framework with the feasibility problem considered as a classification problem, and additional stages introduced to improve local exploitation and global exploration. We illustrate the efficiency of the proposed framework on three test problems and implement it in a realistic case study describing the production of solid‐based drugs using wet granulation, aimed to reduce the operation cost, improve product quality, and increase process flexibility and robustness.