Latent Space Simulation for Carbon Capture Design Optimization


  • Brian Bartoldson Lawrence Livermore National Laboratory
  • Rui Wang University of California San Diego Lawrence Livermore National Laboratory
  • Yucheng Fu Pacific Northwest National Laboratory
  • David Widemann Lawrence Livermore National Laboratory
  • Sam Nguyen Lawrence Livermore National Laboratory
  • Jie Bao Pacific Northwest National Laboratory
  • Zhijie Xu Pacific Northwest National Laboratory
  • Brenda Ng Lawrence Livermore National Laboratory



AI Surrogate Simulation, Carbon Capture, Design Optimization


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.




How to Cite

Bartoldson, B., Wang, R., Fu, Y., Widemann, D., Nguyen, S., Bao, J., Xu, Z., & Ng, B. (2022). Latent Space Simulation for Carbon Capture Design Optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12447-12453.