Efficient Bayesian Optimization of Industrial-Scale Pressure-Vacuum Swing Adsorption Processes for CO2 Capture

The design of adsorption systems for separation of CO2/N-2 in carbon capture applications is notoriously challenging because it requires constrained multiobjective optimization to determine appropriate combinations of a moderately large number of system operating parameters. The status quo in the literature is to use the nondominated sorting genetic algorithm II (NSGA-II) to solve the design problem. This approach requires 1000s of timeconsuming process simulations to find the Pareto front of the problem, meaning it can take days of computational time to obtain a solution. As an alternative approach, we have employed a Bayesian optimization algorithm, the Thompson sampling efficient multiobjective optimization (TSEMO). For constrained productivity/energy usage optimization, we find that the TSEMO algorithm is able to find an essentially identical solution to the design problem as that found using NSGA-II, while requiring 14 times less computational time. We have used the TSEMO algorithm to design a postcombustion carbon capture system for a 1000 MW coal fired power plant using two adsorbent materials, zeolite 13X and ZIF-36-FRL. Although ZIF-36-FRL showed promising process-scale performance in previous studies, we find that the industrial scale performance is inferior to the benchmark zeolite 13X, requiring a 21% greater cost per tonne of CO2 captured. Finally, we have also tested the performance of the Bayesian design framework when coupled with a data-driven machine learning process modeling framework. In this instance, we find that the incumbent NSGA-II offers better computational performance than the Bayesian approach by a factor of 3.

Automated summary

The design of adsorption systems for CO2/N-2 separation in carbon capture applications is challenging and requires multiobjective optimization. Traditional methods using NSGA-II are time-consuming, while a Bayesian optimization algorithm called TSEMO can provide the same solution with significantly less computational time. The use of TSEMO algorithm has led to the design of an efficient carbon capture system. However, when coupled with a data-driven machine learning framework, NSGA-II outperforms the Bayesian approach in terms of computational performance.