Abstract
While classical heuristics can be applied to decide on the preferred reactor concept for simple reaction schemes, more complex reaction networks require more sophisticated methods, such as the multilevel reactor design approach or superstructure optimization. Based on an analysis of the existing methods a nonlinear programming framework for a superstructure-based reactor network synthesis is presented, emphasizing numerical robustness and flexible network representation without relying on integer decisions. The approach, which is implemented in GAMS, allows for the combination of continuous stirred-tank and cross-flow reactor models. An exemplary application for the classical Van de Vusse reaction is first shown for validation, prior to the application to an enzymatic cascade based on the Weimberg pathway. Assuming fast co-factor regeneration, the performance of the resulting PFR cascade, which can also be interpreted as a sequence of batch reactions, is compared with a commonly applied single batch reactor. The results show that the two-reactor configuration consistently achieves higher product formation by mitigating inhibition effects, demonstrating the potential of reactor network synthesis for complex reaction systems like enzymatic cascades.