Structural Optimization of Translucent Monolith Reactors through Multi-objective Bayesian Optimization

Photochemical monolith reactors offer advantages over microreactors by providing high mixing efficiency and surface area to volume ratio while being scalable. However, optimizing monolith design parameters like channel number, shape, and stacking is critical to maximizing light usage and reactor efficiency. This work proposes using Bayesian optimization and COMSOL Multiphysics simulations to automatically design translucent monoliths for photochemical reactions. The goal is to maximize both photochemical space-time yield and space-time yield. Ray tracing simulations were performed while evaluating five different channel geometries (circular, elliptical, triangular, square, and pentagonal) and optimizing parameters, including channel diameter, vertical stacking, shape rotation, and ellipse axis ratio. Results showed a clear trade-off between Space-Time Yield (STY) and Photochemical Space-Time Yield (PSTY), with optimized elliptical channels achieving up to 15.3% improvement in STY with minimal PSTY increase and 9.0% enhancement in PSTY with negligible STY decrease.