10.1007/s40089-021-00356-8

Predicting carbon dioxide adsorption capacity on types 13X and 5A zeolites using artificial neural network modeling

  • Hojatollah Moradi1,
  • Hedayat Azizpour2,
  • Kamran Keynejad*2,
  • Zahra Nasrollahi2,
  • Hossein Bahmanyar1,
  • Ensieh Soltani1,
  1. Surface Phenomenon and Liquid-Liquid Extraction Research Lab, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IR
  2. Department of Chemical Engineering, Fouman Faculty of Engineering, College of Engineering, University of Tehran, Tehran, IR

Published in Issue 2021-11-05

How to Cite

Moradi, H., Azizpour, H., Keynejad, K., Nasrollahi, Z., Bahmanyar, H., & Soltani, E. (2021). Predicting carbon dioxide adsorption capacity on types 13X and 5A zeolites using artificial neural network modeling. International Nano Letters, 12(1 (March 2022). https://doi.org/10.1007/s40089-021-00356-8
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Abstract

Abstract This study used artificial neural network modeling to predict carbon dioxide adsorption capacity on two zeolite adsorbents, 13X (MS 544HP) and 5A (MS 522). Temperature and pressure were used as the system inputs and adsorption capacity as the output. To determine the network training algorithm, the optimal transfer functions in the hidden and output layers and the optimal number of neurons, the coefficient of the determination, and the root mean square error were calculated. To find the best network training algorithm, eight different algorithms, including TRAINBFG, TRAINRP, TRAINCGP, TRAINCGF, TRAINR, TRAINCBG, TRAINBR, and TRAINLM were compared. After modeling the experimental data, the Levenberg–Marquardt backpropagation (BP) algorithm was used to train the network for both zeolites. The optimal number of neurons for both 13X and 5A zeolites was obtained as 10. Finally, the results of artificial neural network modeling and the Toth model, obtained by Wang, were compared. Coefficients of determination for artificial neural network and Toth have been obtained for 13X adsorbent, as 0.9974, and 0.9918 and they were determined as 0.9941 and 0.9923 for 5A adsorbent, respectively. These R 2 values show the high accuracy of the artificial neural network compared with the Toth model.

Keywords

  • Adsorbents,
  • Zeolites,
  • Carbon dioxide removal (CDR),
  • Artificial neural network (ANN) modeling,
  • Levenberg–Marquardt

References

  1. Wang and LeVan (2009) Adsorption equilibrium of carbon dioxide and water vapour on zeolites 5A and 13X and silica gel: pure components 54(10) (pp. 2839-2844) https://doi.org/10.1021/je800900a
  2. Luzzi et al. (2021) Mechanically coherent zeolite 13X/Chitosan aerogel beads for effective CO2 capture 13(17) (pp. 20728-20734) https://doi.org/10.1021/acsami.1c04064
  3. Cavenati et al. (2004) Adsorption equilibrium of methane, carbon dioxide, and nitrogen on zeolite 13X at high pressures 49(4) (pp. 1095-1101) https://doi.org/10.1021/je0498917
  4. Gholipour and Mofarahi (2016) Adsorption equilibrium of methane and carbon dioxide on zeolite 13X: experimental and thermodynamic modeling (pp. 47-54) https://doi.org/10.1016/j.supflu.2016.01.008
  5. Hotchkiss et al. (2015) Sources of and processes controlling CO2 emissions change with the size of streams and rivers 8(9) (pp. 696-699) https://doi.org/10.1038/ngeo2507
  6. Heslop et al. (2000) Absolute determination of the composition of binary gas mixtures by admixture of known components 78(8) (pp. 1061-1065) https://doi.org/10.1205/026387600528328
  7. Bae and Snurr (2011) Development and evaluation of porous materials for carbon dioxide separation and capture 50(49) (pp. 11586-11596) https://doi.org/10.1002/anie.201101891
  8. Ghaedi and Vafaei (2017) Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: a review (pp. 20-39) https://doi.org/10.1016/j.cis.2017.04.015
  9. Nematollahi et al. (2014) Organoclay maleated natural rubber nanocomposite. Prediction of abrasion and mechanical properties by artificial neural network and adaptive neuro-fuzzy inference (pp. 187-199) https://doi.org/10.1016/j.clay.2014.05.027
  10. Moradi, H., Azizpour, H., Bahmanyar, H., Mohammad, E.: Molecular dynamic simulation of carbon dioxide, methane, and nitrogen adsorption on Faujasite zeolite. Chin. J. Chem. Eng. 2021.
  11. https://doi.org/10.1016/j.cjche.2021.05.034
  12. Tanzifi et al. (2018) Adsorption of Amido Black 10B from aqueous solution using polyaniline/SiO2 nanocomposite: experimental investigation and artificial neural network modeling (pp. 246-261) https://doi.org/10.1016/j.jcis.2017.09.055
  13. Asl et al. (2013) Artificial neural network (ANN) approach for modeling of Cr (VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA) 19(3) (pp. 1044-1055) https://doi.org/10.1016/j.jiec.2012.12.001
  14. Dashti et al. (2018) Rigorous prognostication and modeling of gas adsorption on activated carbon and Zeolite-5A (pp. 58-68) https://doi.org/10.1016/j.jenvman.2018.06.091
  15. Hoseinian et al. (2017) The nickel ion removal prediction model from aqueous solutions using a hybrid neural-genetic algorithm (pp. 311-317) https://doi.org/10.1016/j.jenvman.2017.09.011
  16. Franco et al. (2020) Analysis of indium (III) adsorption from leachates of LCD screens using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANIFS) https://doi.org/10.1016/j.jhazmat.2019.121137
  17. Li et al. (2016) Modeling of adsorption in rotating packed bed using artificial neural networks (ANN) (pp. 89-95) https://doi.org/10.1016/j.cherd.2016.08.013
  18. Pakravan et al. (2015) Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using polyethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube 5(1) (pp. 47-59) https://doi.org/10.1007/s13203-014-0077-7
  19. Moradi et al. (2019) Investigation of adsorption of methane, carbon dioxide and N2 on zeolite 13X using artificial neural network 30(113) (pp. 1-3)
  20. Schaap and Bouten (1996) Modeling water retention curves of sandy soils using neural networks 32(10) (pp. 3033-3040) https://doi.org/10.1029/96WR02278