Efficient Bayesian Optimization of Industrial-Scale Pressure-Vacuum Swing Adsorption Processes for CO<sub>2</sub> Capture
نویسندگان
چکیده
The design of adsorption systems for separation CO2/N2 in carbon capture applications is notoriously challenging because it requires constrained multiobjective optimization to determine appropriate combinations a moderately large number system operating parameters. status quo the literature use nondominated sorting genetic algorithm II (NSGA-II) solve problem. This approach 1000s time-consuming process simulations find Pareto front problem, meaning can take days computational time obtain solution. As an alternative approach, we have employed Bayesian algorithm, Thompson sampling efficient (TSEMO). For productivity/energy usage optimization, that TSEMO able essentially identical solution problem as found using NSGA-II, while requiring 14 times less time. We used postcombustion 1000 MW coal fired power plant two adsorbent materials, zeolite 13X and ZIF-36-FRL. Although ZIF-36-FRL showed promising process-scale performance previous studies, industrial-scale inferior benchmark 13X, 21% greater cost per tonne CO2 captured. Finally, also tested framework when coupled with data-driven machine learning modeling framework. In this instance, incumbent NSGA-II offers better than by factor 3.
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ژورنال
عنوان ژورنال: Industrial & Engineering Chemistry Research
سال: 2022
ISSN: ['0888-5885', '1520-5045']
DOI: https://doi.org/10.1021/acs.iecr.2c02313