A novel nested loop optimization problem based on deep neural networks and feasible operation regions definition for simultaneous material screening and process optimization

•Material screening and simultaneous processes optimization.•Deep learning neural networks providing deep insights about the process behavior.•A nested loop is proposed to provide the integration of the level within the optimization framework.•Uncertainty assessment of the optimal points provides a map of the feasible operating regions. The present work proposes a novel strategy for simultaneous material screening and process optimization. This strategy is based on the capacities of deep neural networks to extract knowledge of a database. It makes use of a nested optimization loop which is designed to couple the process and material points of view simultaneously. The optimization problem results are analyzed by a Fisher–Snedecor test, designed to assess the optimal points uncertainties, building the process’s feasible operating regions. This methodology describes the processes’ possible operating points that lead to optimal conditions, considering the material type as a decision variable. The methodology shows that this complex problem can be better understood when the uncertainties are taken into consideration. On the other hand, the proposed optimization problem can provide a way to address the issues related to optimizing adsorption processes considering the material screening.