Autoignition temperature: comprehensive data analysis and predictive models

References

• C.E. Frank and A.U. Blackham, Reaction processes leading to the spontaneous ignition of hydrocarbons, Ind. Eng. Chem. 46 (1954), pp. 212–217. doi:10.1021/ie50529a063.
   Google Scholar
• D.E. Swarts and M. Orchin, Spontaneous ignition temperature of hydrocarbons, Ind. Eng. Chem. 49 (1957), pp. 432–436. doi:10.1021/ie51392a042.
   Google Scholar
• L.M. Egolf and P.C. Jurs, Estimation of autoignition temperatures of hydrocarbons, alcohols, and esters from molecular structure, Ind. Eng. Chem. Res. 31 (1992), pp. 1798–1807. doi:10.1021/ie00007a027.
   Web of Science ®Google Scholar
• A. Dashti, M. Jokar, F. Amirkhani, and A.H. Mohammadi, Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds, J. Mol. Liq. 300 (2020), pp. 111797. doi:10.1016/j.molliq.2019.111797.
   Web of Science ®Google Scholar
• C.-C. Chen, H.-J. Liaw, and Y.-Y. Kuo, Prediction of autoignition temperatures of organic compounds by the structural group contribution approach, J. Hazard. Mater. 162 (2009), pp. 746–762. doi:10.1016/j.jhazmat.2008.05.137.
   PubMed Web of Science ®Google Scholar
• T. Suzuki, Quantitative structure—property relationships for auto‐ignition temperatures of organic compounds, Fire Mater. 18 (1994), pp. 81–88. doi:10.1002/fam.810180204.
   Google Scholar
• J. Tetteh, E. Metcalfe, and S.L. Howells, Optimisation of radial basis and backpropagation neural networks for modelling auto-ignition temperature by quantitative-structure property relationships, Chemom. Intell. Lab. Syst. 32 (1996), pp. 177–191. doi:10.1016/0169-7439(95)00088-7.
   Web of Science ®Google Scholar
• C.E. Frank and A.U. Blackham, Spontaneous ignition of organic compounds, Ind. Eng. Chem. 44 (1952), pp. 862–867. doi:10.1021/ie50508a044.
   Google Scholar
• W.A. Affens, J.E. Johnson, and H.W. Carhart, Effect of chemical structure on spontaneous ignition of hydrocarbons, J. Chem. Eng. Data 6 (1961), pp. 613–619. doi:10.1021/je60011a041.
   Google Scholar
• C. Morley, A fundamentally based correlation between alkane structure and octane number, Combust. Sci. Technol. 55 (1987), pp. 115–123. doi:10.1080/00102208708947074.
   Web of Science ®Google Scholar
• B.E. Mitchell and P.C. Jurs, Prediction of autoignition temperatures of organic compounds from molecular structure, J. Chem. Inf. Comput. Sci. 37 (1997), pp. 538–547. doi:10.1021/ci960175l.
   Google Scholar
• J. Tetteh, S. Howells, E. Metcalfe, and T. Suzuki, Optimisation of radial basis function neural networks using biharmonic spline interpolation, Chemom. Intell. Lab. Syst. 41 (1998), pp. 17–29. doi:10.1016/S0169-7439(98)00035-5.
   Web of Science ®Google Scholar
• Y.S. Kim, S.K. Lee, J.H. Kim, J.S. Kim, and K. Tai, Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure–property relationship, J. Chem. Soc. Perkin Trans. 2 (2002), pp. 2087–2092. doi:10.1039/B207203C.
   Google Scholar
• T. Albahri, Flammability characteristics of pure hydrocarbons, Chem. Eng. Sci. 58 (2003), pp. 3629–3641. doi:10.1016/S0009-2509(03)00251-3.
   Web of Science ®Google Scholar
• T.A. Albahri and R.S. George, Artificial neural network investigation of the structural group contribution method for predicting pure components auto ignition temperature, Ind. Eng. Chem. Res. 42 (2003), pp. 5708–5714. doi:10.1021/ie0300373.
   Web of Science ®Google Scholar
• Y. Pan, J. Jiang, R. Wang, H. Cao, and J. Zhao, Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices, J. Hazard. Mater. 157 (2008), pp. 510–517. doi:10.1016/j.jhazmat.2008.01.016.
   PubMed Web of Science ®Google Scholar
• Y. Pan, J. Jiang, R. Wang, and H. Cao, Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds, Chemom. Intell. Lab. Syst. 92 (2008), pp. 169–178. doi:10.1016/j.chemolab.2008.03.002.
   Web of Science ®Google Scholar
• Y. Pan, J. Jiang, R. Wang, H. Cao, and Y. Cui, Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine, J. Hazard. Mater. 164 (2009), pp. 1242–1249. doi:10.1016/j.jhazmat.2008.09.031.
   PubMed Web of Science ®Google Scholar
• J.A. Lazzús, Autoignition temperature prediction using an artificial neural network with particle swarm optimization, Int. J. Thermophys. 32 (2011), pp. 957. doi:10.1007/s10765-011-0956-4.
   Web of Science ®Google Scholar
• C. Hansch, K.H. Kim, and R.H. Sarma, Structure-activity relation in benzamides inhibiting alcohol dehydrogenase, J. Am. Chem. Soc. 95 (1973), pp. 6447–6449. doi:10.1021/ja00800a049.
   PubMed Web of Science ®Google Scholar
• J.G. Topliss and R.J. Costello, Chance correlations in structure-activity studies using multiple regression analysis, J. Med. Chem. 15 (1972), pp. 1066–1068. doi:10.1021/jm00280a017.
   PubMed Web of Science ®Google Scholar
• F. Gharagheizi, An accurate model for prediction of autoignition temperature of pure compounds, J. Hazard. Mater. 189 (2011), pp. 211–221. doi:10.1016/j.jhazmat.2011.02.014.
   PubMed Web of Science ®Google Scholar
• M. Bagheri, T.N.G. Borhani, and G. Zahedi, Estimation of flash point and autoignition temperature of organic sulfur chemicals, Energ. Convers. Manage. 58 (2012), pp. 185–196. doi:10.1016/j.enconman.2012.01.014.
   Web of Science ®Google Scholar
• M.H. Keshavarz, F. Gharagheizi, and M. Ghanbarzadeh, A simple correlation for prediction of autoignition temperature of various classes of hydrocarbons, J. Iran. Chem. Soc. 10 (2013), pp. 545–557. doi:10.1007/s13738-012-0192-2.
   Web of Science ®Google Scholar
• T.N.G. Borhani, A. Afzali, and M. Bagheri, QSPR estimation of the auto-ignition temperature for pure hydrocarbons, Proc. Saf. Environ. 103 (2016), pp. 115–125. doi:10.1016/j.psep.2016.07.004.
   Web of Science ®Google Scholar
• I. Baskin and A. Varnek, Fragment descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening, in Chemoinformatics Approaches to Virtual Screening, A. Varnek and A. Tropsha, eds., RSC Publisher, Cambridge, 2008, pp. 1–43.
   Google Scholar
• I. Baskin and A. Varnek, Building a chemical space based on fragment descriptors, Comb. Chem. High T. Scr. 11 (2008), pp. 661–668.
   PubMed Web of Science ®Google Scholar
• A. Varnek, D. Fourches, F. Hoonakker, and V. Solov’ev, Substructural fragments: AAn universal language to encode reactions, molecular and supramolecular structures, J. Comput. Aid. Mol. Des. 19 (2005), pp. 693–703. doi:10.1007/s10822-005-9008-0.
   PubMed Web of Science ®Google Scholar
• A. Varnek, D. Fourches, D. Horvath, O. Klimchuk, C. Gaudin, P. Vayer, V. Solov’ev, F. Hoonakker, I.V. Tetko, and G. Marcou, ISIDA - Platform for virtual screening based on fragment and pharmacophoric descriptors, Curr. Comput. Aid. Drug Des. 4 (2008), pp. 191–198. doi:10.2174/157340908785747465.
   Web of Science ®Google Scholar
• I.I. Baskin, M.I. Skvortsova, I.V. Stankevich, and N.S. Zefirov, On the basis of invariants of labeled molecular graphs, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 527–531. doi:10.1021/ci00025a021.
   Google Scholar
• N.V. Artemenko, I.I. Baskin, V.A. Palyulin, and N.S. Zefirov, Prediction of physical properties of organic compounds using artificial neural networks within the substructure approach, Dokl. Chem. 381 (2001), pp. 317–320. doi:10.1023/A:1012976623974.
   Web of Science ®Google Scholar
• A. Varnek, D. Fourches, V.P. Solov’ev, V.E. Baulin, A.N. Turanov, V.K. Karandashev, D. Fara, and A.R. Katritzky, “In silico” design of new uranyl extractants based on phosphoryl-containing podands: QSPR studies, generation and screening of virtual combinatorial library, and experimental tests, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 1365–1382. doi:10.1021/ci049976b.
   PubMedGoogle Scholar
• I. Oprisiu, E. Varlamova, E. Muratov, A. Artemenko, G. Marcou, P. Polishchuk, and V. Kuz’min, QSPR approaches to predict non-additive properties of mixtures. Application to bubble point temperatures of binary mixtures of liquid, Mol. Inform. 31 (2012), pp. 491–502. doi:10.1002/minf.201200006.
   PubMed Web of Science ®Google Scholar
• H. Drucker, C.J.C. Burges, L. Kaufman, A. Smola, and V. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems, M.C. Mozer, J.I. Jordan, and J.I. Petsche, eds., MIT Press, Cambridge, 1997, pp. 155–161.
   Google Scholar
• C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines, ACM T. Intel. SysT. Tech. 2 (2001), pp. 1–27.
   Google Scholar
• D. Horvath, J.B. Brown, G. Marcou, and A. Varnek, An evolutionary optimizer of libsvm models, Challenges 5 (2014), pp. 450–472. doi:10.3390/challe5020450.
   Google Scholar
• C.M. Bishop, M. Svensén, and C.K.I. Williams, GTM: The generative topographic mapping, Neural Comput. 10 (1998), pp. 215–234. doi:10.1162/089976698300017953.
   Web of Science ®Google Scholar
• N. Kireeva, I.I. Baskin, H.A. Gaspar, D. Horvath, G. Marcou, and A. Varnek, Generative topographic mapping (GTM): Universal tool for data visualization, structure-activity modeling and dataset comparison, Mol. Inform. 31 (2012), pp. 301–312. doi:10.1002/minf.201100163.
   PubMed Web of Science ®Google Scholar
• H.A. Gaspar, I.I. Baskin, G. Marcou, D. Horvath, and A. Varnek, GTM-based QSAR models and their applicability domains, Mol. Inform. 34 (2015), pp. 348–356. doi:10.1002/minf.201400153.
   PubMed Web of Science ®Google Scholar
• H.A. Gaspar, G. Marcou, D. Horvath, A. Arault, S. Lozano, P. Vayer, and A. Varnek, Generative topographic mapping-based classification models and their applicability domain: Application to the biopharmaceutics drug disposition classification system (BDDCS), J. Chem. Inf. Model. 53 (2013), pp. 3318–3325. doi:10.1021/ci400423c.
   PubMed Web of Science ®Google Scholar
• H.A. Gaspar, P. Sidorov, D. Horvath, I.I. Baskin, G. Marcou, and A. Varnek, Generative topographic mapping approach to chemical space analysis, in Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015, R.J. Bienstock, V. Shanmugasundaram, and J. Bajorath, eds., ACS Publications, Washington DC, 2016, pp. 211–241.
   Google Scholar