@inproceedings{
eskandari2023multifidelity,
title={Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators},
author={Mahdi Eskandari and Lars Puiman and Jakob Zeitler},
booktitle={NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment},
year={2023},
url={https://openreview.net/forum?id=Zj74fKRgCE}
}
A Bayesian optimization approach for maximizing the gas conversion rate in syngas fermentation is presented. We have access to an expensive-to-evaluate, computational fluid dynamic (CFD) reactor model and a cheap ideal-mixing based reactor model. The goal is to maximize the gas conversion rate with respect to the input variables. Due to the high cost of the industrial simulator, a multi-fidelity Bayesian optimization is adopted to solve the optimization problem using both high and low fidelities. We first describe the problem of syngas fermentation followed by our approach to solving simulator optimisation 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.
Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators
Mahdi Eskandari Lars Puiman Jakob Zeitler