TY - JOUR AU - Bartoldson, Brian AU - Wang, Rui AU - Fu, Yucheng AU - Widemann, David AU - Nguyen, Sam AU - Bao, Jie AU - Xu, Zhijie AU - Ng, Brenda PY - 2022/06/28 Y2 - 2024/03/29 TI - Latent Space Simulation for Carbon Capture Design Optimization JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - IAAI Technical Track on Emerging Applications of AI DO - 10.1609/aaai.v36i11.21511 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21511 SP - 12447-12453 AB - 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. ER -