  A Bayesian optimization approach for maximizing the gas conversion rate in an
industrial-scale bioreactor for syngas fermentation is presented. We have
access to a high-fidelity, computational fluid dynamic (CFD) reactor model and
a low-fidelity ideal-mixing-based reactor model. The goal is to maximize the
gas conversion rate, with respect to the input variables (e.g., pressure,
biomass concentration, gas flow rate). Due to the high cost of the CFD reactor
model, a multi-fidelity Bayesian optimization algorithm is adopted to solve the
optimization problem using both high and low fidelities. We first describe the
problem in the context of syngas fermentation followed by our approach to
solving simulator optimization using multiple fidelities. We discuss concerns
regarding significant differences in fidelity cost and their impact on fidelity
sampling and conclude with a discussion on the integration of real-world
fermentation data.
