A novel framework for the identification of complex feasible space

Feasibility analysis is suitable to determine the combinations of input parameters that satisfy all operating and quality constraints, i.e., the process design space. In the presence of disjoint feasible regions and for computationally expensive/nonconvex problems, the use of surrogate-based approaches has been increasingly adopted. However, the choice and prediction accuracy of a suitable surrogate depend on the process of interest, and on the available dataset. In this study, we propose a novel workflow which combines different mathematical tools to (i) investigate how to identify the process feasible space relying on the available training dataset, (ii) assess whether there is a minimum number of sampling points that are necessary to uncover the complexity of the original feasibility function, and (iii) support proper surrogate model selection, while improving accuracy. The proposed methodology is tested on analytical problems and a pharmaceutical process, showing its effectiveness in the identification of complex feasible regions.