Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors
This work addresses the computationally expensive and nonlinear optimization problem for chemical reactor design, which is incremental as it applies existing methods to a new domain.
The paper tackles the optimization of chemical reactor geometries using deep Gaussian processes in a multi-fidelity Bayesian optimization setting, resulting in reduced computational effort to achieve good solutions by leveraging five discrete mesh fidelities.
New manufacturing techniques such as 3D printing have recently enabled the creation of previously infeasible chemical reactor designs. Optimizing the geometry of the next generation of chemical reactors is important to understand the underlying physics and to ensure reactor feasibility in the real world. This optimization problem is computationally expensive, nonlinear, and derivative-free making it challenging to solve. In this work, we apply deep Gaussian processes (DGPs) to model multi-fidelity coiled-tube reactor simulations in a Bayesian optimization setting. By applying a multi-fidelity Bayesian optimization method, the search space of reactor geometries is explored through an amalgam of different fidelity simulations which are chosen based on prediction uncertainty and simulation cost, maximizing the use of computational budget. The use of DGPs provides an end-to-end model for five discrete mesh fidelities, enabling less computational effort to gain good solutions during optimization. The accuracy of simulations for these five fidelities is determined against experimental data obtained from a 3D printed reactor configuration, providing insights into appropriate hyper-parameters. We hope this work provides interesting insight into the practical use of DGP-based multi-fidelity Bayesian optimization for engineering discovery.