1
|
Kim SH, Landa HOR, Ravutla S, Realff MJ, Boukouvala F. Data-Driven Simultaneous Process Optimization and Adsorbent Selection for Vacuum Pressure Swing Adsorption. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
2
|
Pinaeva LG, Noskov AS. Modern Level of Catalysts and Technologies for the Conversion of Natural Gas into Syngas. CATALYSIS IN INDUSTRY 2022. [DOI: 10.1134/s2070050422010081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
3
|
Abstract
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.
Collapse
|