  Bayesian optimization (BO) is a powerful technology for optimizing noisy
expensive-to-evaluate black-box functions, with a broad range of real-world
applications in science, engineering, economics, manufacturing, and beyond. In
this paper, we provide an overview of recent developments, challenges, and
opportunities in BO for design of next-generation process systems. After
describing several motivating applications, we discuss how advanced BO methods
have been developed to more efficiently tackle important problems in these
applications. We conclude the paper with a summary of challenges and
opportunities related to improving the quality of the probabilistic model, the
choice of internal optimization procedure used to select the next sample point,
and the exploitation of problem structure to improve sample efficiency.
