Volume 68

Machine learning-driven multi-objective optimization of wet overhead gas recompression for carbon capture X. H. Chen1, Q. Hong1, J. X. Lin1,2, L. Jiang 1*

Abstract

The high energy penalty of chemical-absorption carbon capture limits its large-scale deployment. Overhead gas recompression (OGR) reduces this penalty; however, conventional OGR generates high interstage temperatures, and applying deep inter-cooling causes latent heat loss, diminishing recovery benefits. To address this, a continuous in-stage spray cooling process using downstream condensate is designed to lower temperatures and maximize heat recovery. Because the spray reflux alters the fluid mass across compression stages, conventional equal-pressure-ratio strategies are inapplicable, requiring multi-dimensional optimization of pressure ratios and spray diversion ratios. Since mechanistic optimization of flowsheets with complex recycle loops is computationally prohibitive, a boundary-sensitivity analysis is first conducted to thermodynamically decouple the OGR module from the upstream absorption-desorption process. Using Latin Hypercube Sampling and automated Aspen Plus simulations, a dataset is generated to train a deep neural network (DNN) surrogate model. Coupled with the NSGA-II algorithm under a thermo-mechanical safety limit, the framework optimizes specific heat consumption, specific power consumption, and total annualized cost (TAC) to determine the optimal parameters. Finally, SHAP and thermodynamic path analyses are applied to interpret the black-box model, confirming that the optimized wet compression balances evaporative cooling and mass-flow penalties. Compared to the conventional OGR baseline, the optimized configuration reduces specific power consumption by 12.7% (to 131.9 kWh/tCO2) and lowers the TAC by 3.6% (to 87.80 million USD/yr), increasing the equivalent coefficient of performance (COPeq) from 6.13 to 9.94.

Keywords absorption-based carbon capture; overhead gas recompression; wet compression; machine learning; multi-objective optimization