Conclusion
We proposed a process design method that combines Bayesian optimization
and a process simulator to search for design variables that satisfy the
performances of an ethylene oxide plant with a small number of
simulations. We verified the effectiveness of the method by comparing it
with a random search. It was confirmed by the case study that the
candidates for design variables that achieve the plant performances can
be efficiently proposed. The reproducibility of the proposed method was
also confirmed. Moreover, various candidates were obtained by increasing
the number of Bayesian optimization trials. This method is expected to
meet the needs of knowledgeable chemical engineers and to facilitate
process designs of new plants.