Computational fluid dynamics (CFD) has become an important tool for reactor, optimization and trouble shooting. Bayesian optimization allows to find an optimal reactor configuration with a minimum of computationally intensive CFD simulations. Park et al. demonstrated the power of multi-objective Bayesian optimization by maximizing the gas-holdup and minimizing the power consumption of a stirred tank reactor . Clearly integrating active machine learning in CFD allows for a faster and more efficient reactor design.