Dynamical Approach to the Ishitani-Abe Model: S-MPC forImproved CO₂ Artificial Photosynthesis

We propose a dynamical systems approach to enhance CO2 photosynthesis using the Ishitani-Abe five-variable CSTR model, integrating center manifold reduction, renormalization group (RG) methods, and stochastic model predictive control (s-MPC). Simulations show a baseline Open-Loop yield of 9.5% with chaotic dynamics, improved to over 10% by s-MPC, surpassing the 10% target with a peak yield of approximately 10.1%. Detailed analyses are in the supplement.