Skip to main content

Advertisement

SpringerLink
Log in

Predicting hydrogen storage capacity of metal–organic frameworks using group method of data handling

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Due to their unique properties, metal–organic frameworks have exhibited excellent performance for hydrogen storage purposes in the last decade. In this regard, model development to predict the hydrogen storage in metal–organic frameworks is of a vital importance for designing and developing of efficient processes based on these new synthetic material. The objective of the present study is to develop a new model to predict the hydrogen storage capacity in metal–organic frameworks. The group method of data handling-type polynomial neural networks is implemented as a soft computing approach for model building. As an advantage, only 40% of data points are used for model development and the rest of data (60%) are designated for testing of the model. The results show that the proposed model has reasonable accuracy in which the root mean square error for the proposed model is 0.28. The model can acceptably predict effects of surface area and pressure on hydrogen storage capacity of MOFs demonstrating good ability of the proposed model for tracing physically expected trend for hydrogen storage. Additionally, the leverage measure demonstrates that the proposed model is statistically acceptable and valid. It should be noted that an artificial neural network is also developed for comparison with GMDH-PNN model, in which the results confirm that both of the models have approximately same performance and accuracy. However, due to simple mathematical structure of GMDH-PNN, it is significantly more appropriate for engineering applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Taken from Ref. [39]

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Abbreviations

tzi:

5-Tetrazolylisophthalate

btt:

1,3,5-Benzenetristetrazolate

ndc:

2,6-Naphthalenedicarboxylate

diPyNI:

N,N′-di-(4-pyridyl)-1,4,5,8-Naphthalenetetracarboxydiimide

bdt:

1,4-Benzenebistetrazolate

btatb:

4,4′,4″,4‴-Benzene-1,2,4,5-tetrayltetrabenzoate

bpdc:

4,4′-Biphenyldicarboxylate

bpy:

4,4′-Bipyridine

sip:

5-Sulfoisophthalate

dccptp:

3,5-Dicyano-4-(4-carboxyphenyl)-2,2′:6′,4″-terpyridine

aobtc:

Azoxybenzene-3,3′,5,5′-tetracarboxylate

sbtc:

Trans-stilbene-3,3′,5,5′-tetracarboxylic acid

bhtc:

Biphenyl-3,4′,5-tricarboxylate

ttpm:

Tetrakis(4-tetrazolylphenyl)methane

tbip:

5-t-Butyl isophthalate

dmf:

N,N′-Dimethylformamide

dhtp:

2,5-Dihydroxyterephthalic acid

2-pymo:

2-Pyrimidinolate

References

  1. Zhao D, Yuan D, Zhou H-C (2008) The current status of hydrogen storage in metal–organic frameworks. Energy Environ Sci 1:222. https://doi.org/10.1039/b808322n

    Article  Google Scholar 

  2. DoE Office of Energy Efficiency and Renewable Energy Hydrogen, Fuel Cells & Infrastructure Technologies Program, “Grand Challenge” for Basic and Applied Research in Hydrogen Storage Solicitation. http://www.eere.energy.gov/hydrogenandfuelce. Accessed 20 Jan 2017

  3. DoE Office of Energy Efficiency and Renewable Energy Hydrogen, Fuel Cells & Infrastructure Technologies Program Multi-Year Research, Development and Demonstration Plan. http://www.eere.energy.gov/hydrogenandfuelcells/mypp. Accessed 20 Jan 2017

  4. Wong-Foy AG, Matzger AJ, Yaghi OM (2006) Exceptional H2 saturation uptake in microporous metal-organic frameworks. J Am Chem Soc 128:3494–3495. https://doi.org/10.1021/ja058213h

    Article  Google Scholar 

  5. Wong-Foy AG, Lebel O, Matzger AJ (2007) Porous crystal derived from a tricarboxylate linker with two distinct binding motifs. J Am Chem Soc 129:15740–15741. https://doi.org/10.1021/ja0753952

    Article  Google Scholar 

  6. Rowsell JLC, Yaghi OM (2006) Effects of functionalization, catenation, and variation of the metal oxide and organic linking units on the low-pressure hydrogen adsorption properties of metal organic frameworks. J Am Chem Soc 128:1304–1315

    Article  Google Scholar 

  7. Navarro JAR, Barea E, Salas JM et al (2013) H2, N2, CO, and CO2 sorption properties of a series of robust sodalite-type microporous coordination polymers. J Am Chem Soc 45:19–21. https://doi.org/10.1029/2004GB002243

    Article  Google Scholar 

  8. Liu Y, Kravtsov VC, Larsen R, Eddaoudi M (2006) Molecular building blocks approach to the assembly of zeolite-like metal-organic frameworks (ZMOFs) with extra-large cavities. Chem Commun (Camb) 14:1488–1490. https://doi.org/10.1039/b600188m

    Article  Google Scholar 

  9. Liu Y, Kabbour H, Brown CM et al (2008) Increasing the density of adsorbed hydrogen with coordinatively unsaturated metal centers in metal–organic frameworks increasing the density of adsorbed hydrogen with coordinatively unsaturated metal centers in metal–organic frameworks. Langmuir 24:4772–4777. https://doi.org/10.1021/la703864a

    Article  Google Scholar 

  10. Gadzikwa T, Lu G, Stern CL et al (2008) An example of node-based postassembly elaboration of a hydrogen-sorbing, metal-organic framework material. Inorg Chem 47:10223–10225. https://doi.org/10.1039/b805101a

    Article  Google Scholar 

  11. Dincă M, Han WS, Liu Y et al (2007) Observation of Cu2+ –H2 interactions in a fully desolvated sodalite-type metal-organic framework. Angew Chem Int Ed 46:1419–1422. https://doi.org/10.1002/anie.200604362

    Article  Google Scholar 

  12. Murray LJ, Dinca M, Long JR (2009) Hydrogen storage in metal–organic frameworks. Chem Soc Rev 38:1294–1314. https://doi.org/10.1039/b802256a

    Article  Google Scholar 

  13. Yıldız Z, Uzun H (2015) Prediction of gas storage capacities in metal organic frameworks using artificial neural network. Microporous Mesoporous Mater 208:50–54. https://doi.org/10.1016/j.micromeso.2015.01.037

    Article  Google Scholar 

  14. Xamana FX, Gacson J (2013) Metal organic frameworks as heterogeneous catalysts. Royal Society of Chemistry

  15. Nagarkar SS, Saha T, Desai AV et al (2014) Metal-organic framework based highly selective fluorescence turn-on probe for hydrogen sulphide. Sci Rep 4:7053–7058. https://doi.org/10.1038/srep07053

    Article  Google Scholar 

  16. Kreno LE, Leong K, Farha OK et al (2011) Metal–organic framework materials as chemical sensors. Chem Rev 112:1105–1125. https://doi.org/10.1021/cr200324t

    Article  Google Scholar 

  17. Wu P, Liu Y, Liu Y et al (2015) Cadmium-based metal-organic framework as a highly selective and sensitive ratiometric luminescent sensor for mercury(II). Inorg Chem 54:11046–11048. https://doi.org/10.1021/acs.inorgchem.5b01758

    Article  Google Scholar 

  18. Wei Z, Gu Z, Arvapally RK et al (2014) Rigidifying fluorescent linkers by MOF formation for fluorescence blue shift and quantum yield enhancement rigidifying fluorescent linkers by MOF formation for fluorescence blue shift and quantum yield enhancement. J Am Ceram Soc 136:8269–8276

    Google Scholar 

  19. Qiu S, Xue M, Zhu G (2014) Metal–organic framework membranes: from synthesis to separation application. Chem Soc Rev 43:6116–6140. https://doi.org/10.1039/C4CS00159A

    Article  Google Scholar 

  20. Li B, Wen H-M, Zhou W, Chen B (2014) Porous metal−organic frameworks for gas storage and separation: What, how, and why? J Phys Chem C 5:3468–3479. https://doi.org/10.1021/jz501586e

    Article  Google Scholar 

  21. Basdogan Y, Keskin S (2015) Simulation and modelling of MOFs for hydrogen storage. CrystEngComm 17:261–275. https://doi.org/10.1039/C4CE01711K

    Article  Google Scholar 

  22. Abbaspour A, Khalilnejad A, Chen Z (2016) Robust adaptive neural network control for PEM fuel cell. Int J Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2016.09.075

    Article  Google Scholar 

  23. Atashrouz S, Mirshekar H (2014) Phase equilibrium modeling for binary systems containing CO2 using artificial neural networks. Bulg Chem Commun 46:104–116

    Google Scholar 

  24. Hossain MA, Ayodele BV, Cheng CK, Khan MR (2016) Artificial neural network modeling of hydrogen-rich syngas production from methane dry reforming over novel Ni/CaFe2O4 catalysts. Int J Hydrogen Energy 41:11119–11130. https://doi.org/10.1016/j.ijhydene.2016.04.034

    Article  Google Scholar 

  25. Karaci A, Caglar A, Aydinli B, Pekol S (2016) The pyrolysis process verification of hydrogen rich gas (H-rG) production by artificial neural network (ANN). Int J Hydrogen Energy 41:4570–4578. https://doi.org/10.1016/j.ijhydene.2016.01.094

    Article  Google Scholar 

  26. Lopez-Guede JM, Ramos-Hernanz JA, Zulueta E et al (2016) Systematic modeling of photovoltaic modules based on artificial neural networks. Int J Hydrogen Energy 41:12672–12687. https://doi.org/10.1016/j.ijhydene.2016.04.175

    Article  Google Scholar 

  27. Fathinasab M, Ayatollahi S, Hemmati-Sarapardeh A (2015) A rigorous approach to predict nitrogen-crude oil minimum miscibility pressure of pure and nitrogen mixtures. Fluid Phase Equilib 399:30–39. https://doi.org/10.1016/j.fluid.2015.04.003

    Article  Google Scholar 

  28. Atashrouz S, Zarghampour M, Abdolrahimi S et al (2014) Estimation of the viscosity of ionic liquids containing binary mixtures based on the Eyring’s theory and a modified gibbs energy model. J Chem Eng Data 59:3691–3704

    Article  Google Scholar 

  29. Atashrouz S, Pazuki G, Alimoradi Y (2014) Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system. Fluid Phase Equilib 372:43–48. https://doi.org/10.1016/j.fluid.2014.03.031

    Article  Google Scholar 

  30. Atashrouz S, Mozaffarian M, Pazuki G (2015) Modeling the thermal conductivity of ionic liquids and ionanofluids based on a group method of data handling and modified Maxwell model. Ind Eng Chem Res 54:8600–8610. https://doi.org/10.1021/acs.iecr.5b00932

    Article  Google Scholar 

  31. Hemmati-Sarapardeh A, Ghazanfari M-H, Ayatollahi S, Masihi M (2016) Accurate determination of the CO2-crude oil minimum miscibility pressure of pure and impure CO2 streams: a robust modelling approach. Can J Chem Eng 94:253–261. https://doi.org/10.1002/cjce.22387

    Article  Google Scholar 

  32. Atashrouz S, Hemmati Sarapardeh A, Mirshekar H et al (2016) On the evaluation of thermal conductivity of ionic liquids: modeling and data assessment. J Mol Liq 224:648–656. https://doi.org/10.1016/j.molliq.2016.09.106

    Article  Google Scholar 

  33. Atashrouz S, Pazuki G, Kakhki SS (2015) A GMDH-type neural network for prediction of water activity in glycol and Poly(ethylene glycol) solutions. J Mol Liq 202:95–100. https://doi.org/10.1016/j.molliq.2014.12.013

    Article  Google Scholar 

  34. Atashrouz S, Amini E, Pazuki G (2014) Modeling of surface tension for ionic liquids using group method of data handling. Ionics (Kiel) 21:1595–1603. https://doi.org/10.1007/s11581-014-1347-1

    Article  Google Scholar 

  35. Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 1:30–37. https://doi.org/10.1109/TSMC.1971.4308320

    Article  MathSciNet  Google Scholar 

  36. Ivakhnenko AG (1971) Polynomial theory of complex systems polynomial theory of complex systems. IEEE Trans Syst Man Cybern 4:364–378

    Article  Google Scholar 

  37. Ivakhnenko AG (1968) The group method of data handling—a rival of the method of stochastic approximation. Sov Autom Control 13:43–71

    Google Scholar 

  38. Ayyıldız A (2016) Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network. Proc Inst Mech Eng Part E J Process Mech Eng. https://doi.org/10.1177/0954408916659310

    Article  Google Scholar 

  39. Oh SK, Pedrycz W, Park BJ (2003) Polynomial neural networks architecture: analysis and design. Comput Electr Eng 29:703–725. https://doi.org/10.1016/S0045-7906(02)00045-9

    Article  Google Scholar 

  40. Atashrouz S, Mozaffarian M, Pazuki G (2016) Viscosity and rheological properties of ethylene glycol + water + Fe3O4 nanofluids at various temperatures: experimental and thermodynamics modeling. Korean J Chem Eng 33:2522–2529. https://doi.org/10.1007/s11814-016-0169-4

    Article  Google Scholar 

  41. Nouar F, Eubank JF, Bousquet T et al (2008) Supermolecular building blocks (SBBs) for the design and synthesis of highly porous metal–organic frameworks. J Am Chem Soc 130:1833–1835. https://doi.org/10.1021/ja710123s

    Article  Google Scholar 

  42. Yu AF, Long JR (2006) Microporous metal–organic frameworks incorporating 1,4-benzeneditetrazolate: syntheses, structures, and hydrogen storage properties. J Am Chem Soc 128:8904–8913

    Article  Google Scholar 

  43. Dinca M, Long JR (2005) Strong H2 binding and selective gas adsorption within the microporous coordination solid Mg3(O2C–C10H6–CO2)3. J Am Chem Soc 127:9376–9377. https://doi.org/10.1021/ja0523082

    Article  Google Scholar 

  44. Lee JY, Pan L, Kelly SP et al (2005) Achieving high density of adsorbed hydrogen in microporous metal organic frameworks. Adv Mater 17:2703–2706. https://doi.org/10.1002/adma.200500867

    Article  Google Scholar 

  45. Forster PM, Eckert J, Heiken BD et al (2006) Adsorption of molecular hydrogen on coordinatively unsaturated Ni (II) sites in a nanoporous hybrid material. J Am Chem Soc 128:16846–16850

    Article  Google Scholar 

  46. Yang W, Lin X, Jia J et al (2008) A biporous coordination framework with high H2 storage density. Chem Commun (Camb). https://doi.org/10.1039/b712201b

    Article  Google Scholar 

  47. Wang X, Ma S, Rauch K et al (2008) Metal–organic frameworks based on double-bond- coupled di-isophthalate linkers with high hydrogen and methane uptakes. Chem Mater 1992:2–6

    Google Scholar 

  48. Krawiec P, Kramer M, Sabo M et al (2006) Improved hydrogen storage in the metal–organic framework Cu3(BTC)2. Adv Eng Mater 8:293–296. https://doi.org/10.1002/adem.200500223

    Article  Google Scholar 

  49. Panella B, Hirscher M, Pütter H, Müller U (2006) Hydrogen adsorption in metal-organic frameworks: Cu-MOFs and Zn-MOFs compared. Adv Funct Mater 16:520–524. https://doi.org/10.1002/adfm.200500561

    Article  Google Scholar 

  50. Dincǎ M, Long JR (2008) Hydrogen storage in microporous metal–organic frameworks with exposed metal sites. Angew Chem Int Ed 47:6766–6779. https://doi.org/10.1002/anie.200801163

    Article  Google Scholar 

  51. Pan L, Parker B, Huang X et al (2006) Zn(tbip) (H2tbip) 5-butyl isophthalic acid): a highly stable guest-free microporous metal organic framework with unique gas separation capability. J Am Chem Soc 128:4180–4181

    Article  Google Scholar 

  52. Dinca M, Long JR (2007) High-enthalpy hydrogen adsorption in cation-exchanged variants of the microporous metal–organic framework Mn3[(Mn4Cl)3(BTT)8(CH3OH)10]2. J Am Chem Soc 3:11172–11176

    Article  Google Scholar 

  53. Atashrouz S, Mirshekar H, Hemmati-Sarapardeh A et al (2016) Implementation of soft computing approaches for prediction of physicochemical properties of ionic liquid mixtures. Korean J Chem Eng 32:1–15. https://doi.org/10.1007/s11814-016-0271-7

    Article  Google Scholar 

  54. Wall WA, Wiechert L, Comerford A, Rausch S (2010) Towards a comprehensive computational model for the respiratory system. Int J Numer Method Biomed Eng 26:807–827. https://doi.org/10.1002/cnm

    Article  MATH  Google Scholar 

  55. De Rocquigny E, Devictor N, Tarantola S (2008) Uncertainty in industrial practice. Wiley, New York

    Book  Google Scholar 

  56. Saltelli A, Ratto M, Tarantola S, Campolongo F (2005) Sensitivity analysis for chemical models. Chem Rev 105:2811–2828. https://doi.org/10.1017/CBO9781107415324.004

    Article  MATH  Google Scholar 

  57. Saltelli A, Ratto M, Andres T et al (2008) Global sensitivity analysis. The primer. Wiley, New York

    MATH  Google Scholar 

  58. Pianosi F, Sarrazin F, Wagener T (2015) A Matlab toolbox for global sensitivity analysis. Environ Model Softw 70:80–85. https://doi.org/10.1016/j.envsoft.2015.04.009

    Article  Google Scholar 

  59. Santner TJ, Williams BJ, Notz WI (2003) Design and analysis of computer experiments. Springer, Berlin

    Book  Google Scholar 

  60. Campolongo F, Saltelli A, Cariboni J (2011) From screening to quantitative sensitivity analysis. A unified approach. Comput Phys Commun 182:978–988. https://doi.org/10.1016/j.cpc.2010.12.039

    Article  MATH  Google Scholar 

  61. Hosseinzadeh M, Hemmati-Sarapardeh A (2014) Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids. J Mol Liq 200:340–348. https://doi.org/10.1016/j.molliq.2014.10.033

    Article  Google Scholar 

  62. Hemmati-Sarapardeh A, Aminshahidy B, Pajouhandeh A et al (2016) A soft computing approach for the determination of crude oil viscosity: light and intermediate crude oil systems. J Taiwan Inst Chem Eng 59:1–10. https://doi.org/10.1016/j.jtice.2015.07.017

    Article  Google Scholar 

  63. Shateri M, Ghorbani S, Hemmati-Sarapardeh A, Mohammadi AH (2015) Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor. J Taiwan Inst Chem Eng 50:131–141. https://doi.org/10.1016/j.jtice.2014.12.011

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amirkabir University of Technology, Mahshahr Campus, Mahshahr, Iran

    Saeid Atashrouz & Mohammad Rahmani

  2. CleanTech Research Laboratory, Amirkabir University of Technology, Tehran, Iran

    Saeid Atashrouz & Mohammad Rahmani

  3. Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Rahmani

Authors
  1. Saeid Atashrouz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Rahmani.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 43 kb)

Supplementary material 2 (DOCX 123 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Atashrouz, S., Rahmani, M. Predicting hydrogen storage capacity of metal–organic frameworks using group method of data handling. Neural Comput & Applic 32, 14851–14864 (2020). https://doi.org/10.1007/s00521-020-04837-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04837-3

Keywords

Search

Navigation