SpringerLink
Log in

Prediction of Catalytic Hydrogen Generation by Water–Gas Shift Reaction Using a Neural Network Approach

  • Published:
Catalysis Letters Aims and scope Submit manuscript

Abstract

Hydrogen (H2) is an environmentally-safe power source and its demands is continuously growing worldwide. The most important approach for its generation is water–gas shift (WGS) reaction through various catalysts. This work investigates feasibility of neural network method named Multilayer Perceptron Neural Network (MLP-NN) to estimate CO conversion in WGS reactions based on different active phase compositions and various supports. The approach considers the intrinsic parameters of the catalyst to estimate reaction performance. This research investigates the most influential variables by conducting a sensitivity analysis study on the predictions of the implemented method. The results of the modeling study revealed that the MLP-NN method can accurately approximate the experimental CO conversion values. The sensitivity analysis study revealed temperature and H2 feed concentration are the most crucial parameters on the reaction performance. The reliability of neural network methods is proved such as the MLP-NN to accurately estimate the CO conversion values in WGS reaction.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Chestnut LG, Mills DM (2005) A fresh look at the benefits and costs of the US acid rain program. J Environ Manage 77(3):252–266

    Article  CAS  PubMed  Google Scholar 

  2. Kemper N (2008) Veterinary antibiotics in the aquatic and terrestrial environment. Ecol Ind 8(1):1–13

    Article  CAS  Google Scholar 

  3. Chumachenko Y, Buluchevskiy E, Fedorova E, Nepomnyashchii A, Gulyaeva T, Trenikhin M, Izmailov R, Mironenko R (2019) Hydrodeoxygenation of sorbitol to gasoline-range hydrocarbons over Pt, Pd, Rh, Ru, Ni catalysts supported on tungstated alumina. Biomass Convers Biorefinery 11:1–11

    Google Scholar 

  4. Hájek M, Skopal F, Vávra A, Kocík J (2017) Transesterification of rapeseed oil by butanol and separation of butyl ester. J Clean Prod 155:28–33

    Article  Google Scholar 

  5. Dong Q, Yang F, Liang F, Zhang Y, **a D, Zhao W, Wu L, Liu X, Jiang Z, Sun C (2021) Silver particle on BiVO4 nanosheet plasmonic photocatalyst with enhanced photocatalytic oxidation activity of sulfadiazine. J Mol Liquids 331:115751

    Article  CAS  Google Scholar 

  6. Kim K-H, Ihm S-K (2011) Heterogeneous catalytic wet air oxidation of refractory organic pollutants in industrial wastewaters: a review. J Hazard Mater 186(1):16–34

    Article  CAS  PubMed  Google Scholar 

  7. Rj BS, Loganathan M, Shantha MS (2010) A review of the water gas shift reaction kinetics. Int J Chem Reactor Eng. https://doi.org/10.2202/1542-6580.2238

    Article  Google Scholar 

  8. Song C (2010) Introduction to hydrogen and syngas production and purification technologies. Hydrogen and syngas production and purification technologies. Wiley, Hoboken, pp 1–13

    Google Scholar 

  9. Polychronopoulou K, Kalamaras C, Efstathiou A (2011) Ceria-based materials for hydrogen production via hydrocarbon steam reforming and water-gas shift reactions. Recent Patents Mater Sci 4(2):122–145

    CAS  Google Scholar 

  10. Hush DR, Horne BG (1993) Progress in supervised neural networks. IEEE Signal Process Mag 10(1):8–39

    Article  Google Scholar 

  11. Haykin S, Lippmann RJ (1994) Neural networks, a comprehensive foundation. Int J Neural Syst 5(4):363–364

    Article  Google Scholar 

  12. Ghasemzadeh K, Aghaeinejad-Meybodi A, Basile A (2018) Hydrogen production as a green fuel in silica membrane reactor: experimental analysis and artificial neural network modeling. Fuel 222:114–124

    Article  CAS  Google Scholar 

  13. Cavalcanti FM, Schmal M, Giudici R, Alves RMB (2019) A catalyst selection method for hydrogen production through water–gas shift reaction using artificial neural networks. J Environ Manage 237:585–594

    Article  CAS  PubMed  Google Scholar 

  14. Barati-Harooni A, Najafi-Marghmaleki A (2016) An accurate RBF-NN model for estimation of viscosity of nanofluids. J Mol Liq 224:580–588

    Article  CAS  Google Scholar 

  15. Zamaniyan A, Joda F, Behroozsarand A, Ebrahimi H (2013) Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant. Int J Hydrog Energy 38(15):6289–6297

    Article  CAS  Google Scholar 

  16. Serra JM, Corma A, Chica A, Argente E, Botti V (2003) Can artificial neural networks help the experimentation in catalysis? Catal Today 81(3):393–403

    Article  CAS  Google Scholar 

  17. Wei J, Chu X, Sun XY, Xu K, Deng HX, Chen J, Wei Z, Lei M (2019) Machine learning in materials science. InfoMat 1(3):338–358

    Article  CAS  Google Scholar 

  18. Ross JR (2011) Heterogeneous catalysis: fundamentals and applications. Elsevier, Amsterdam

    Google Scholar 

  19. Corma A, Serra J, Serna P, Moliner M (2005) Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models. J Catal 232(2):335–341

    Article  CAS  Google Scholar 

  20. Holeňa M, Baerns M (2003) Feedforward neural networks in catalysis: a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction. Catal Today 81(3):485–494

    Article  Google Scholar 

  21. Omata K, Kobayashi Y, Yamada M (2007) Artificial neural network aided virtual screening of additives to a Co/SrCO3 catalyst for preferential oxidation of CO in excess hydrogen. Catal Commun 8(1):1–5

    Article  CAS  Google Scholar 

  22. Basile A, Curcio S, Bagnato G, Liguori S, Jokar S, Iulianelli A (2015) Water gas shift reaction in membrane reactors: theoretical investigation by artificial neural networks model and experimental validation. Int J Hydrog Energy 40(17):5897–5906

    Article  CAS  Google Scholar 

  23. Takassi M, Gharibi Kharaji A, Esfandyari M, KoolivandSalooki M (2013) Neuro-fuzzy prediction of Fe–V2O5-promoted γ-alumina catalyst behavior in the reverse water–gas–shift reaction. Energ Technol 1(2–3):144–150

    Article  CAS  Google Scholar 

  24. Akubo K, Nahil MA, Williams PT (2019) Pyrolysis-catalytic steam reforming of agricultural biomass wastes and biomass components for production of hydrogen/syngas. J Energy Inst 92(6):1987–1996

    Article  CAS  Google Scholar 

  25. Smith A, Keane A, Dumesic JA, Huber GW, Zavala VM (2020) A machine learning framework for the analysis and prediction of catalytic activity from experimental data. Appl Catal B 263:118257

    Article  CAS  Google Scholar 

  26. Buitrago R, Ruiz-Martínez J, Silvestre-Albero J, Sepúlveda-Escribano A, Rodríguez-Reinoso F (2012) Water gas shift reaction on carbon-supported Pt catalysts promoted by CeO2. Catal Today 180(1):19–24

    Article  CAS  Google Scholar 

  27. de Farias AMD, Barandas AP, Perez RF, Fraga MA (2007) Water–gas shift reaction over magnesia-modified Pt/CeO2 catalysts. J Power Sources 165(2):854–860

    Article  Google Scholar 

  28. Dufour J, Martos C, Ruiz A, Ayuela F (2013) Effect of the precursor on the activity of high temperature water gas shift catalysts. Int J Hydrog Energy 38(18):7647–7653

    Article  CAS  Google Scholar 

  29. Fu Q, Deng W, Saltsburg H, Flytzani-Stephanopoulos M (2005) Activity and stability of low-content gold–cerium oxide catalysts for the water–gas shift reaction. Appl Catal B 56(1–2):57–68

    Article  CAS  Google Scholar 

  30. Hwang K-R, Ihm S-K, Park S-C, Park J-S (2013) Pt/ZrO2 catalyst for a single-stage water-gas shift reaction: Ti addition effect. Int J Hydrog Energy 38(14):6044–6051

    Article  CAS  Google Scholar 

  31. Hwang K-R, Lee C-B, Park J-S (2011) Advanced nickel metal catalyst for water–gas shift reaction. J Power Sources 196(3):1349–1352

    Article  CAS  Google Scholar 

  32. Jacobs G, Graham UM, Chenu E, Patterson PM, Dozier A, Davis BH (2005) Low-temperature water–gas shift: impact of Pt promoter loading on the partial reduction of ceria and consequences for catalyst design. J Catal 229(2):499–512

    Article  CAS  Google Scholar 

  33. Jacobs G, Ricote S, Davis BH (2006) Low temperature water-gas shift: type and loading of metal impacts decomposition and hydrogen exchange rates of pseudo-stabilized formate over metal/ceria catalysts. Appl Catal A 302(1):14–21

    Article  CAS  Google Scholar 

  34. Jeong D-W, Jang W-J, Shim J-O, Han W-B, Roh H-S, Jung UH, Yoon WL (2014) Low-temperature water–gas shift reaction over supported Cu catalysts. Renewable Energy 65:102–107

    Article  CAS  Google Scholar 

  35. Jiang L, Li C, Li Z, Zhang S (2013) Effect of additives on the activity of CuO/Ce0.6Zr0.4O2 catalysts for the water–gas shift reaction. Chem Eng Technol 36(11):1891–1898

    Article  CAS  Google Scholar 

  36. Kalamaras CM, Petallidou KC, Efstathiou AM (2013) The effect of La3+-do** of CeO2 support on the water-gas shift reaction mechanism and kinetics over Pt/Ce1−xLaxO2−δ. Appl Catal B 136:225–238

    Article  Google Scholar 

  37. Kim CH, Thompson LT (2005) Deactivation of Au/CeOx water gas shift catalysts. J Catal 230(1):66–74

    Article  CAS  Google Scholar 

  38. Li Y, Fu Q, Flytzani-Stephanopoulos M (2000) Low-temperature water–gas shift reaction over Cu-and Ni-loaded cerium oxide catalysts. Appl Catal B 27(3):179–191

    Article  Google Scholar 

  39. Liang S, Veser G (2012) Mixed lanthana/ceria nanorod-supported gold catalysts for water–gas-shift. Catal Lett 142(8):936–945

    Article  CAS  Google Scholar 

  40. Lin J, Wang A, Qiao B, Liu X, Yang X, Wang X, Liang J, Li J, Liu J, Zhang T (2013) Remarkable performance of Ir1/FeOx single-atom catalyst in water gas shift reaction. J Am Chem Soc 135(41):15314–15317

    Article  CAS  PubMed  Google Scholar 

  41. Mellor J, Coville N, Sofianos A, Copperthwaite R (1997) Raney copper catalysts for the water-gas shift reaction: I. Preparation, activity and stability. Appl Catal A 164(1–2):171–183

    Article  CAS  Google Scholar 

  42. Phillips C, Patt J, Moon DJ, Thompson LT (2000) Molybdenum carbide catalysts for water–gas shift

  43. Rhodes C, Hutchings G, Ward A (1995) Water-gas shift reaction: finding the mechanistic boundary. Catal Today 23(1):43–58

    Article  CAS  Google Scholar 

  44. Rhodes C, Williams BP, King F, Hutchings GJ (2002) Promotion of Fe3O4/Cr2O3 high temperature water gas shift catalyst. Catal Commun 3(8):381–384

    Article  CAS  Google Scholar 

  45. Shinde VM, Madras G (2012) Water gas shift reaction over multi-component ceria catalysts. Appl Catal B 123:367–378

    Article  Google Scholar 

  46. Shinde VM, Madras G (2013) Nanostructured Pd modified Ni/CeO2 catalyst for water gas shift and catalytic hydrogen combustion reaction. Appl Catal B 132:28–38

    Article  Google Scholar 

  47. Shinde VM, Madras G (2013) Synthesis of nanosized Ce0.85M0.1Ru0.05O2−δ (M = Si, Fe) solid solution exhibiting high CO oxidation and water gas shift activity. Appl Catal B 138:51–61

    Article  Google Scholar 

  48. Wang X, Gorte RJ, Wagner J (2002) Deactivation mechanisms for Pd/ceria during the water–gas-shift reaction. J Catal 212(2):225–230

    Article  CAS  Google Scholar 

  49. Zhang Y, Chen C, Lin X, Li D, Chen X, Zhan Y, Zheng Q (2014) CuO/ZrO2 catalysts for water–gas shift reaction: nature of catalytically active copper species. Int J Hydrog Energy 39(8):3746–3754

    Article  CAS  Google Scholar 

  50. Zugic B, Bell DC, Flytzani-Stephanopoulos M (2014) Activation of carbon-supported platinum catalysts by sodium for the low-temperature water-gas shift reaction. Appl Catal B 144:243–251

    Article  CAS  Google Scholar 

  51. Pantoleontos G, Kikkinides ES, Georgiadis MC (2012) A heterogeneous dynamic model for the simulation and optimisation of the steam methane reforming reactor. Int J Hydrogen Energy 37(21):16346–16358

    Article  CAS  Google Scholar 

  52. Ali J Neural networks: a new tool for the petroleum industry? In: European petroleum computer conference, 1994. Society of Petroleum Engineers

  53. Essenreiter R, Karrenbach M, Treitel S (1998) Multiple reflection attenuation in seismic data using backpropagation. IEEE Trans Signal Process 46(7):2001–2011

    Article  Google Scholar 

  54. Heidari E, Sobati MA, Movahedirad S (2016) Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemom Intell Lab Syst 155:73–85

    Article  CAS  Google Scholar 

  55. Ahmadi MA, Shadizadeh SR (2012) New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel 102:716–723

    Article  CAS  Google Scholar 

  56. Sabzevari S, Moosavi M (2014) Density prediction of liquid alkali metals and their mixtures using an artificial neural network method over the whole liquid range. Fluid Phase Equilib 361:135–142

    Article  CAS  Google Scholar 

  57. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks. IEEE, pp 586–591

  58. Hagen M, Demuth H, Beale M (1996) Neural network design. PWS Publishing Co, Boston

    Google Scholar 

  59. Benyekhlef A, Mohammedi B, Hassani D, Hanini S (2021) Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids. Water Sci Technol 84(3):538–551

    Article  CAS  PubMed  Google Scholar 

  60. Mohammadi J, Ataei M, Kakaei RK, Mikaeil R, Haghshenas SS (2018) Prediction of the production rate of chain saw machine using the multilayer perceptron (MLP) neural network. Civil Eng J 4(7):1575–1583

    Article  Google Scholar 

  61. Moosavi SR, Wood DA, Ahmadi MA, Choubineh A (2019) ANN-based prediction of laboratory-scale performance of CO2-foam flooding for improving oil recovery. Nat Resour Res 28(4):1619–1637

    Article  CAS  Google Scholar 

  62. Azizi S, Ahmadloo E (2016) Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network. Appl Therm Eng 106:203–210. https://doi.org/10.1016/j.applthermaleng.2016.05.189

    Article  CAS  Google Scholar 

  63. Tatar A, Barati A, Yarahmadi A, Najafi A, Lee M, Bahadori A (2016) Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models. Int J Greenhouse Gas Control 47:122–136

    Article  CAS  Google Scholar 

  64. Chen G, Fu K, Liang Z, Sema T, Li C, Tontiwachwuthikul P, Idem R (2014) The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process. Fuel 126:202–212

    Article  CAS  Google Scholar 

  65. Chok NS (2010) Pearson's versus Spearman's and Kendall's correlation coefficients for continuous data. University of Pittsburgh

  66. Reddy GK, Smirniotis PG (2019) Water gas shift reaction

Download references

Author information

Authors and Affiliations

  1. Chemical Engineering Department, Tarbiat Modares University, Tehran, Iran

    Ebrahim Tangestani

  2. Department of Chemistry, University of Tehran, Tehran, Iran

    Samira Ghanbarzadeh

  3. IQS-School of Engineering, Ramon Llull University, 08017, Barcelona, Spain

    Javier Fernandez Garcia

  4. Chemical Engineering Department, University College of London, Roberts Building, Gower Street, London, WC1E 6BT, UK

    Javier Fernandez Garcia

Authors
  1. Ebrahim Tangestani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samira Ghanbarzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Fernandez Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ebrahim Tangestani or Javier Fernandez Garcia.

Ethics declarations

Conflict of interest

Authors for this manuscript declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (XLSX 76 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tangestani, E., Ghanbarzadeh, S. & Garcia, J.F. Prediction of Catalytic Hydrogen Generation by Water–Gas Shift Reaction Using a Neural Network Approach. Catal Lett 153, 863–875 (2023). https://doi.org/10.1007/s10562-022-04019-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10562-022-04019-x

Keywords

Search

Navigation