Latent Space Simulation for Carbon Capture Design Optimization

TL;DR

This paper develops neural network surrogates based on Deep Fluids to rapidly and accurately estimate interfacial areas in solvent-based carbon capture systems, significantly accelerating design optimization.

Contribution

It introduces an optimized neural network surrogate approach that achieves 4000x speedup with low error, addressing CFD simulation bottlenecks in CCS design.

Findings

Neural network surrogates achieve 4000x speedup over CFD.

Surrogates maintain low 4% error on unseen configurations.

Limitations of transferability are identified and discussed.

Abstract

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. Fortunately, previous works such as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are achievable by replacing CFD simulators with neural network (NN) surrogates that faithfully mimic the CFD simulation process. This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization. Thus, here, we build on the DF approach to develop surrogates that can successfully be applied to our complex carbon-capture CFD simulations. Our optimized DF-style surrogates produce large speedups (4000x) while obtaining IA relative errors as low as 4% on unseen CCS configurations that lie within the range of training configurations. This hints at the promise of NN surrogates for our CCS design optimization problem. Nonetheless, DF has inherent limitations with respect to CCS design (e.g., limited transferability of trained models to new CCS packings). We conclude with ideas to address these challenges.