Orthogonal numerical simulation on multi-factor design for rapid pressure swing adsorption

Zheng, Xinliang; Yao, Hang; Huang, Yun

doi:10.1007/s10450-017-9886-1

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…In addition, numerical integration of the DAEs system is complicated and time-consuming, to guarantee the performance accurately and capture the process' dynamic features simultaneously [20], especially dealing with highly nonlinear isotherms, due to numerical dispersion (smearing) and oscillation. All that has also greatly increased the difficulty of process optimization [21].To reduce the computation amounts for simulation and optimization, researchers have proposed a variety of different surrogate models, such as the polynomial surface response model (PRSM) [22], Kriging model [23], proper orthogonal decomposition [24][25][26], polynomial regression model (PNR), support vector regression, and artificial neural network (ANN) model [27,28]. The surrogate model is essentially a black-box model, which is built from a known sample of input-output data points, and can be used to predict the output response at untried points/configurations [25,29].…”

Section: Introductionmentioning

confidence: 99%

A Review of Numerical Research on the Pressure Swing Adsorption Process

et al. 2022

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The pressure swing adsorption (PSA) process has been considered a promising method for gas separation and purification. However, experimental methods are time-consuming, and it is difficult to obtain the detailed changes in variables in the PSA process. This review focuses on the numerical research developed to realize the modelling, optimization and control of the cyclic PSA process. A complete one-dimensional mathematical model, including adsorption bed, auxiliary devices, boundary conditions and performance indicators, is summarized as a general modelling approach. Key simplified assumptions and special treatments for energy balance are discussed for model reliability. Numerical optimization models and control strategies are reviewed for the PSA process as well. Relevant attention is given to the combination of deep-learning technology with artificial-intelligence-based optimization algorithms and advanced control strategies. Challenges to further improvements in the adsorbent database establishment, multiscale computational mass transfer model, large-scale PSA facility design, numerical computations and algorithm robustness are identified.

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