Efficient hybrid multiobjective optimization of pressure swing adsorption
نویسندگان
چکیده
Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA a challenging task. To tackle this, we propose hybrid framework (TSEMO + DyOS), which integrates two steps. In first step, Bayesian stochastic algorithm (i.e., TSEMO) searches entire decision space and identifies approximated Pareto front within small number simulations. Within TSEMO, Gaussian process (GP) surrogate models are trained to approximate original full models. second gradient-based deterministic DyOS) initialized at further refine solutions until local optimality. Therein, model used in optimization. The proposed efficient, because it benefits from coarse-to-fine function evaluations stochastic-to-deterministic searching strategy. When result far away optima, TSEMO can efficiently trade-off curve as good commonly evolutional algorithm, i.e., Nondominated Sorting Genetic Algorithm II (NSGA-II), only uses around 1/16th CPU time NSGA-II. This GP-based utilized initial coarse search. near efficiency dramatically decreases, DyOS accelerate by over 10 times. because, proximity exploitation capacity significantly higher than that TSEMO.
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ژورنال
عنوان ژورنال: Chemical Engineering Journal
سال: 2021
ISSN: ['1873-3212', '1385-8947']
DOI: https://doi.org/10.1016/j.cej.2021.130248