Bayesian optimization as a flexible and efficient design framework for sustainable process systems

Optimization of expensive, noisy, black-box functions commonly occurs in designing sustainable process systems; we review some motivating applications in Section 2 below. In principle, one can apply any type of derivativefree optimization (DFO) method [2] to tackle such problems; however, these methods may require a large number of evaluations to converge. When evaluations of f are expensive, we desire an intelligent sample selection strategy that accounts for all available information to select future samples. The BO framework provides a systematic and versatile way to identify highly informative design candidates using minimal function evaluations. This article reviews recent advances in BO methods and highlights their relevance to design of next-generation sustainable energy and process systems. We also offer some perspectives on future research directions and associated challenges.