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Prediction of lower flammability limits of blended gases based on quantitative structure–property relationship

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Abstract

This work focuses on developing predictive quantitative structure−property relationship (QSPR) models for lower flammability limits (LFLs) of gas mixtures. Experimental LFLs of 86 blended gases were extracted from a single reference, and their mixture descriptors were calculated solely from individual molecular structure by Gaussian 09. Multiple linear regression (MLR) analysis was employed to develop the models, and three different external validation methods were carried out to check the predictive capabilities of the models. The validations have shown that these models possess great predictive power with excellent goodness of fit and internal robustness; hence, they are deemed to be qualified to predict LFLs for other blended gases with no experimental LFL data available. The applicability domains (AD) of the models were defined as well, and all the points were within the AD area. The main advantages of the established models are their simplicity and possibility of extending them for the determination of LFLs of other gas mixtures, while the LFLs of the individuals do not need to be provided.

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References

  1. Pan Y, Jiang J, Wang R, Cao H, Cui Y. A novel QSPR model for prediction of lower flammability limits of organic compounds based on support vector machine. J Hazard Mater. 2009;168(2–3):962–9.

    Article  CAS  Google Scholar 

  2. Ma T, Wang Q, Larrañaga MD. Correlations for estimating flammability limits of pure fuels and fuel-inert mixtures. Fire Saf J. 2013;56(1):9–19.

    Article  CAS  Google Scholar 

  3. Xu W, Li J, Liu F, Jiang Y, Li Z, Li L. Study on the thermal decomposition kinetics and flammability performance of a flame-retardant leather. J Therm Anal Calorim. 2017;128(2):1107–16.

    Article  CAS  Google Scholar 

  4. Xu Q, Jin C, Griffin GJ, Matala A, Hostikka S. A PMMA flammability analysis using the MCC. J Therm Anal Calorim. 2016;126(3):1831–40.

    Article  CAS  Google Scholar 

  5. Unnikrishnan L, Mohanty S, Nayak SK. Evaluation of flammability and shear performance of layered-silicate-reinforced styrenic polymer. J Therm Anal Calorim. 2016;125(1):187–97.

    Article  CAS  Google Scholar 

  6. Ma T, Wang Q, Larrañaga MD. From ignition to suppression, a thermal view of flammability limits. Fire Technol. 2014;50(3):525–43.

    Article  Google Scholar 

  7. Gharagheizi F. A new group contribution-based model for estimation of lower flammability limit of pure compounds. J Hazard Mater. 2009;170(2–3):595–604.

    Article  CAS  Google Scholar 

  8. Jiao L, Zhang X, Qin Y, Wang X, Li H. QSPR study on the flash point of organic binary mixtures by using electrotopological state index. Chemometr Intell Lab. 2016;156:211–6.

    Article  CAS  Google Scholar 

  9. Wang Q, Mannan MS. Prediction of thermochemical properties for gaseous ammonia oxide. J Chem Eng Data. 2010;55(11):5128–32.

    Article  CAS  Google Scholar 

  10. Wang Q, Rogers WJ, Mannan MS. Thermal risk assessment and ranking for reaction hazards in process safety. J Therm Anal Calorim. 2009;98:225–33.

    Article  CAS  Google Scholar 

  11. Zhou L, Wang B, Jiang J, Pan Y, Wang Q. Quantitative structure–property relationship (QSPR) study for predicting gas-liquid critical temperatures of organic compounds. Thermochim Acta. 2017;655:112–6.

    Article  CAS  Google Scholar 

  12. Wang B, Zhou L, Xu K, Wang Q. Fast prediction of minimum ignition energy from molecular structure using simple QSPR model. J Loss Prev Proc. 2017;50:290–4.

    Article  CAS  Google Scholar 

  13. Gharagheizi F. Quantitative structure–property relationship for prediction of the lower flammability limit of pure compounds. Energ Fuel. 2008;22(5):3037–9.

    Article  CAS  Google Scholar 

  14. Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz’min VE. Existing and developing approaches for QSAR analysis of mixtures. Mol Inf. 2012;31(3–4):202–21.

    Article  CAS  Google Scholar 

  15. Kondo S, Takizawa K, Takahashi A, Tokuhashi K, Sekiya A. A study on flammability limits of fuel mixtures. J Hazard Mater. 2008;155(3):440–8.

    Article  CAS  Google Scholar 

  16. The PubChem Project. https://pubchem.ncbi.nlm.nih.gov/#opennewwindow.

  17. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Mennucci B, Petersson GA, Nakatsuji H, Caricato M, Li X, Hratchian HP, Izmaylov AF, Bloino J, Zheng G, Sonnenberg JL, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Keith T, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Rega N, Millam JM, Klene M, Knox JE, Cross JB, Bakken V, Adamo C, Jaramillo J, Gomperts R, Stratmann RE, Yazyev O, Austin AJ, Cammi R, Pomelli C, Ochterski JW, Martin RL, Morokuma K, Zakrzewski VG, Voth GA, Salvador P, Dannenberg JJ, Dapprich S, Daniels AD, Farkas O, Foresman JB, Ortiz JV, Cioslowski J, Fox DJ. Gaussian, Inc., Wallingford; 2010.

  18. Wang Q, Wang J, Larrañaga MD. Simple relationship for predicting thermal hazards of nitro compounds. J Therm Anal Calorim. 2013;111(2):1033–7.

    Article  CAS  Google Scholar 

  19. Wang Q, Ng D, Mannan MS. Study on the reaction mechanism and kinetics of the thermal decomposition of Nitroethane. Ind Eng Chem Res. 2009;48(18):8745–51.

    Article  CAS  Google Scholar 

  20. Lee C, Yang W, Parr RG. Development of the colle-salvetti correlation-energy formula into a functional of the electron density. Phys Rev B. 1988;37(2):785–9.

    Article  CAS  Google Scholar 

  21. Hehre WJ, Ditchfield R, Pople JA. Self-consistent molecular orbital methods. XII. Further extensions of gaussian-type basis sets for use in molecular orbital studies of organic molecules. J Chem Phys. 1972;56:2257–61.

    Article  CAS  Google Scholar 

  22. Krishnan R, Binkley JS, Seeger R, Pople JA. Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions. J Chem Phys. 1980;72(1):650–4.

    Article  CAS  Google Scholar 

  23. Lu Y, Ng D, Mannan MS. Prediction of the reactivity hazards for organic peroxides using the QSPR approach. Ind Eng Chem Res. 2011;50(3):1515–22.

    Article  CAS  Google Scholar 

  24. Wang B, Yi H, Xu K, Wang Q. Prediction of the self-accelerating decomposition temperature of organic peroxides using QSPR models. J Therm Anal Calorim. 2017;128(1):399–406.

    Article  CAS  Google Scholar 

  25. Zhou L, Wang B, Jiang J, Pan Y, Wang Q. Predicting the gas-liquid critical temperature of binary mixtures based on the quantitative structure property relationship. Chemometr Intell Lab. 2017;167:190–5.

    Article  CAS  Google Scholar 

  26. Wang B, Zhou L, Xu K, Wang Q. Prediction of minimum ignition energy from molecular structure using quantitative structure–property relationship (QSPR) models. Ind Eng Chem Res. 2017;56(1):47–51.

    Article  Google Scholar 

  27. Roy K, Ambure P, Aher RB. How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models? Chemometr Intell Lab. 2017;162:44–54.

    Article  CAS  Google Scholar 

  28. Golbraikh A, Tropsha A. Beware of q2! J Mol Graph Model. 2002;20(4):269–76.

    Article  CAS  Google Scholar 

  29. Nie NH, Hull CH, Jenkins JG, Steinbrenner K, Bent DH. SPSS: statistical package for the social sciences. 2nd ed. New York: McGraw-Hill; 1975.

    Google Scholar 

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Acknowledgements

This research was financially supported by China Scholarship Council (CSC). The authors would like to thank the High Performance Computing Center at Oklahoma State University for software support and computing time. The author Q. Wang also appreciates the support from the Dale F. Janes Endowed Professorship at Oklahoma State University.

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Authors and Affiliations

  1. School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Beibei Wang & Kaili Xu

  2. Department of Fire Protection and Safety, Oklahoma State University, Stillwater, OK, 74078, USA

    Beibei Wang, Haejun Park & Qingsheng Wang

  3. Department of Chemical Engineering, Oklahoma State University, Stillwater, OK, 74078, USA

    Qingsheng Wang

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  1. Beibei Wang
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  2. Haejun Park
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Correspondence to Qingsheng Wang.

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Wang, B., Park, H., Xu, K. et al. Prediction of lower flammability limits of blended gases based on quantitative structure–property relationship. J Therm Anal Calorim 132, 1125–1130 (2018). https://doi.org/10.1007/s10973-017-6941-9

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  • DOI: https://doi.org/10.1007/s10973-017-6941-9

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