In reaction or process design, the goal with Bayesian optimization is to determine the optimal operating conditions to maximize the product yields, minimize the emissions per product, achieve the highest energy efficiency, etc. Optimization of reaction conditions has been demonstrated multiple times, from multi-objective reaction optimization with both discrete and continuous variables which makes it probably the most well-developed field of active machine learning in chemical engineering . Shields et al. applied Bayesian optimization to optimize the reaction conditions for a Mitsunobu reaction, and found an optimal yield (>99%) for several non-intuitive reaction conditions after 40 experiments, beating the standard reaction yield of 60% . With active learning the goal is to acquire reaction knowledge which can be used for reactor and catalyst design, process control or retrosynthesis. Eyke et al. demonstrated the potential of active learning for DoE in reaction design by predicting reaction yields for combinations of catalysts and solvents with a minimum of available data . Recently, a DoEtool for the study of chemical reactions has been developed and validated on the catalytic pyrolysis of plastic waste by Ureel et al .