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Numerical Research on the Pressure Swing Adsorption Process

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Numerical Research on the Pressure Swing Adsorption Process ( numerical-research-pressure-swing-adsorption-process )

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Processes 2022, 10, 812 19 of 19 66. Sun, W.; Shen, Y.; Zhang, D.; Yang, H.; Ma, H. A systematic simulation and proposed optimization of the pressure swing adsorption process for N2/CH4 separation under external disturbances. Ind. Eng. Chem. Res. 2015, 54, 7489–7501. [CrossRef] 67. Ding, Z.; Han, Z.; Fu, Q.; Shen, Y.; Tian, C.; Zhang, D. Optimization and analysis of the VPSA process for industrial-scale oxygen production. Adsorption 2018, 24, 499–516. [CrossRef] 68. Leperi, K.T.; Yancy-Caballero, D.; Snurr, R.Q.; You, F. 110th Anniversary: Surrogate Models Based on Artificial Neural Networks To Simulate and Optimize Pressure Swing Adsorption Cycles for CO2 Capture. Ind. Eng. Chem. Res. 2019, 58, 18241–18252. [CrossRef] 69. Moustapha, M.; Bourinet, J.-M.; Guillaume, B.; Sudret, B. Comparative study of Kriging and support vector regression for structural engineering applications. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2018, 4, 4018005. [CrossRef] 70. Cozad, A.; Sahinidis, N.V.; Miller, D.C. Learning surrogate models for simulation-based optimization. AIChE J. 2014, 60, 2211–2227. [CrossRef] 71. Zhang, N.; Bénard, P.; Chahine, R.; Yang, T.; Xiao, J. Optimization of pressure swing adsorption for hydrogen purification based on Box-Behnken design method. Int. J. Hydrogen Energy 2021, 46, 5403–5417. [CrossRef] 72. Shen, Y.; Shi, W.; Zhang, D.; Na, P.; Fu, B. The removal and capture of CO2 from biogas by vacuum pressure swing process using silica gel. J. CO2 Util. 2018, 27, 259–271. [CrossRef] 73. Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A.; et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2021, 57, 101994. [CrossRef] 74. Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent manufacturing in the context of industry 4.0: A review. Engineering 2017, 3, 616–630. [CrossRef] 75. Gholami, A.; Bonakdari, H.; Ebtehaj, I.; Mohammadian, M.; Gharabaghi, B.; Khodashenas, S.R. Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing. Measurement 2018, 121, 294–303. [CrossRef] 76. Luan, J.; Yao, Z.; Zhao, F.; Song, X. A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization. Math. Comput. Simul. 2019, 156, 294–309. [CrossRef] 77. Urich, M.D.; Vemula, R.R.; Kothare, M. V Multivariable model predictive control of a novel rapid pressure swing adsorption system. AIChE J. 2018, 64, 1234–1245. [CrossRef] 78. Hui, P.; Ping, W.; Weihua, L. Model Based Fractional Order PID Controller Design and Simulation of Pressure Swing Adsorption. In Proceedings of the 2019 Chinese Control Conference (CCC), Guangzhou, China, 27–30 July 2019; pp. 2880–2883. 79. Akulinin, E.I.; Ishin, A.A.; Skvortsov, S.A.; Dvoretsky, D.S.; Dvoretsky, S.I. Optimization of adsorption processes with cyclic variable pressure in gas mixture separation. Adv. Mater. Technol. 2017, 3, 51–60. [CrossRef] 80. Han, Z.-Y.; Xing, R.; Zhang, D.-H.; Shen, Y.-H.; Fu, Q.; Ding, Z.-Y.; Tian, C.-X. Vacuum pressure swing adsorption system for N2/CH4 separation under uncertainty. Chem. Eng. Res. Des. 2019, 142, 245–256. [CrossRef] 81. Rumbo Morales, J.Y.; López López, G.; Alvarado Martínez, V.M.; de J. Sorcia Vázquez, F.; Brizuela Mendoza, J.A.; Martínez García, M. Parametric study and control of a pressure swing adsorption process to separate the water-ethanol mixture under disturbances. Sep. Purif. Technol. 2020, 236, 116214. [CrossRef]

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