Abstract
In an era defined by economic competitiveness and environmental awareness, engineering solutions must maximize profitability, efficiency and sustainability, underscoring the relevance of process optimization and the societal impact any contribution in this research field would bring. In chemical reactor engineering, optimization tasks pose significant challenges due to the highly non-linear and non-convex nature of reactor models, often involving differential equations. While conventional approaches have proven to be reliable strategies for solving these complex problems, their application becomes impractical as problem size and complexity increase. This work introduces a novel application of Physics-Informed Neural Networks (PINNs) to address constrained optimization problems in reactor engineering and demonstrates the proposed methodology through two illustrative case studies in chemical reactor design and control. In doing so, we highlight the capability of PINNs to efficiently learn optimal reactor patterns and analyse the strengths and limitations of this novel methodology. These findings pave the way for broader adoption of PINN-based techniques in process engineering optimization.