This study takes an interesting approach to optimizing flow reactors using machine learning, but a couple of things could use more clarification. While the improved plug flow performance is great, how do these designs hold up when scaled for industrial use? Are there any limitations that might make real-world implementation tricky? Also, the paper uses multi-fidelity Bayesian optimization, but were there any cases where low-fidelity simulations gave misleading results? If so, how did the authors handle that to keep the design predictions reliable?
