A robust model for estimating thermal conductivity of liquid alkyl halides

References

• P.M. Kesselman and T.V. Vasil’Tsova, On prediction of the thermal conductivity of liquids, J. Eng. Phy. Thermophys. 68 (1995), pp. 659–662. doi:10.1007/BF00858067
   Google Scholar
• I. Yang, D. Kim, and S. Lee, Construction and preliminary testing of a guarded hot plate apparatus for thermal conductivity measurements at high temperatures, Int. J. Heat Mass Tran. 122 (2018), pp. 1343–1352. doi:10.1016/j.ijheatmasstransfer.2018.02.072
   Web of Science ®Google Scholar
• Y. Tada, M. Harada, M. Tanigaki, and W. Eguchi, Laser flash method for measuring thermal conductivity of liquids—Application to low thermal conductivity liquids, Rev. Sci. Instrum. 49 (1978), pp. 1305–1314. doi:10.1063/1.1135573
   PubMed Web of Science ®Google Scholar
• T. Boumaza and J. Redgrove, Use of the transient plane source technique for rapid multiple thermal property measurements, Int. J. Thermophys. 24 (2003), pp. 501–512. doi:10.1023/A:1022928206859
   Web of Science ®Google Scholar
• S. Kwon, J. Lee, and D.H. Kim, Reliability of thermal conductivity measurement of liquids by using transient hot-wire, photon-correlation spectroscopy and the laser flash method, J. Korean Phys. Soc. 68 (2016), pp. 1145–1155. doi:10.3938/jkps.68.1145
   Web of Science ®Google Scholar
• L.A. Robbins and L.L. Kingrea, Correlation of thermal conductivity for the liquid substances hydrocarbon, Int. J. Thermophys. 41 (1962), pp. 133–139. doi:10.1023/B:IJOT.0000004097.46622.1a
   Google Scholar
• C. Baroncini, P.D. Filippo, and G. Latini, Thermal conductivity estimation of the organic and inorganic refrigerants in the saturated liquid state, Int. J. Refrig. 6 (1983), pp. 60–62. doi:10.1016/0140-7007(83)90036-1
   Google Scholar
• M. Nagvekar and T.E. Daubert, A group contribution method for liquid thermal conductivity, Ind. Eng. Chem. Res. 26 (1987), pp. 1362–1365. doi:10.1021/ie00067a017
   Web of Science ®Google Scholar
• F.A. Missenard, Prediction of thermal conductivity for the liquid substances by thermal conductivity at 0°C, Rev. Gen. Thermodyn. 141 (1973), pp. 751–768.
   Google Scholar
• R. Krauss and K. Stephan, Thermal conductivity of refrigerants in a wide range of temperature and pressure, J. Phys. Chem. Ref. Data 18 (1989), pp. 47–76. doi:10.1007/BF02081288
   Web of Science ®Google Scholar
• D.M. Klaas and D.S. Viswanath, A correlation for the prediction of thermal conductivity of liquids, Ind. Eng. Chem. Res. 37 (1998), pp. 2064–2068. doi:10.1021/ie9706830
   Web of Science ®Google Scholar
• A.Z. Hezave, S. Raeissi, and M. Lashkarbolooki, Estimation of thermal conductivity of ionic liquids using a perceptron neural network, Ind. Eng. Chem. Res. 51 (2012), pp. 9886–9893. doi:10.1021/ie202681b
   Web of Science ®Google Scholar
• M. Lashkarbolooki, A.Z. Hezave, M. Bayat, M.H. Khademi, and B. Vaferi, A simple correlation for prediction of thermal conductivity of liquid hydrocarbons and aromatics, J. Theor. Comp. Chem. 15 (2016), pp. 1650065. doi:10.1142/S0219633616500656
   Web of Science ®Google Scholar
• M. Lashkarbolooki, A.Z. Hezave, and M. Bayat, Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method, Chinese J. Chem. Eng. 25 (2017), pp. 547–554. doi:10.1016/j.cjche.2016.08.025
   Web of Science ®Google Scholar
• T. Engel and E.J. Gasteiger, Applied Chemoinformatics: Achievements and Future Opportunities, John Wiley & Sons, Weinheim, Germany, 2018.
   Google Scholar
• S. Ghosh, P.K. Ojha, and K. Roy, Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs, Chemosphere 228 (2019), pp. 545–555. doi:10.1016/j.chemosphere.2019.04.124
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and R.N. Das, Statistical Methods in QSAR/QSPR, Springer International Publishing, Cham, 2015.
   Google Scholar
• G.W. Kauffman and P.C. Jurs, Prediction of surface tension, viscosity, and thermal conductivity for common organic solvents using quantitative structure−property relationships, J. Chem. Inf. Comp. Sci. 41 (2001), pp. 408–418. doi:10.1021/ci000139t
   PubMedGoogle Scholar
• A. Khajeh and H. Modarress, Quantitative structure–Property relationship prediction of liquid thermal conductivity for some alcohols, Struct. Chem. 22 (2011), pp. 1315–1323. doi:10.1007/s11224-011-9828-6
   Web of Science ®Google Scholar
• W.Q. Liu, H.X. Lu, C.Z. Cao, Y.C. Jiao, and G.F. Chen, An improved quantitative structure property pelationship model for predicting thermal conductivity of liquid aliphatic alcohols, J. Chem. Eng. Data 63 (2018), pp. 4735–4740. doi:10.1021/acs.jced.8b00764
   Web of Science ®Google Scholar
• N.B. Vargaftik, Handbook of Thermal Conductivity of Liquids and Gases, CRC Press, Boca Raton FL, 1994.
   Google Scholar
• R.W. Kennard and L.A. Stone, Computer aided design of experiments, Technometrics 11 (1969), pp. 137–148. doi:10.1080/00401706.1969.10490666
   Web of Science ®Google Scholar
• H.P.H.A. Frisch, R.D. Dennington, T.A.M.J. Keith, A.J.H.A. Nielsen, and J. Hiscocks, GaussView, Semichem. Inc., Wallingford, CT, 2010.
   Google Scholar
• M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, G.A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. Marenich, J. Bloino, B.G. Janesko, R. Gomperts, B. Mennucci, H.P. Hratchian, J.V. Ortiz, A.F. Izmaylov, J.L. Sonnenberg, D. Williams-Young, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V.G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J.A. Montgomery, J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, and E. Brothers, Gaussian 09, Gaussian, Inc., Wallingford CT, 2016.
   Google Scholar
• T. Srl, Dragon Software for Molecular Descriptor Calculation, Srl, Talete, Milano, Italy, 2014.
   Google Scholar
• Q.Y. Tang and C.X. Zhang, Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research, Insect Sci. 20 (2013), pp. 254–260. doi:10.1111/j.1744-7917.2012.01519.x
   PubMed Web of Science ®Google Scholar
• D. Rogers and A.J. Hopfinger, Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships, J. Chem. Inf. Comp. Sci. 34 (1994), pp. 854–866. doi:10.1021/ci00020a020
   Google Scholar
• K. Roy and P. Pratim Roy, Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques, Eur. J. Med. Chem. 44 (2009), pp. 2913–2922. doi:10.1016/j.ejmech.2008.12.004
   PubMed Web of Science ®Google Scholar
• A. Khajeh and H. Modarress, QSPR prediction of flash point of esters by means of GFA and ANFIS, J. Hazard. Mater. 179 (2010), pp. 715–720. doi:10.1016/j.jhazmat.2010.03.060
   PubMed Web of Science ®Google Scholar
• S. Ma, M. Lv, F. Deng, X. Zhang, H. Zhai, and W. Lv, Predicting the ecotoxicity of ionic liquids towards Vibrio fischeri using genetic function approximation and least squares support vector machine, J. Hazard. Mater. 283 (2015), pp. 591–598. doi:10.1016/j.jhazmat.2014.10.011
   PubMed Web of Science ®Google Scholar
• G.R. Famini, C.A. Penski, and L.Y. Wilson, Using theoretical descriptors in quantitative structure activity relationships: Some physicochemical properties, J. Phys. Org. Chem. 5 (1992), pp. 395–408. doi:10.1002/poc.610050704
   Web of Science ®Google Scholar
• D. Baumann and K. Baumann, Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation, J. Cheminf. 6 (2014), pp. 47. doi:10.1186/s13321-014-0047-1
   PubMed Web of Science ®Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77. doi:10.1002/qsar.200390007
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2! J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:10.1016/S1093-3263(01)00123-1
   PubMed Web of Science ®Google Scholar
• P.P. Roy, S. Paul, I. Mitra, and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009). doi:10.3390/molecules14051660
   PubMed Web of Science ®Google Scholar
• I. Mitra, P.P. Roy, S. Kar, P.K. Ojha, and K. Roy, On further application of r as a metric for validation of QSAR models, J. Chemom. 24 (2010), pp. 22–33. doi:10.1002/cem.1268
   Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring rm2 metrics for validation of QSPR models, Chemom. Intell. Lab. 107 (2011), pp. 194–205. doi:10.1016/j.chemolab.2011.03.011
   Web of Science ®Google Scholar
• K. Roy, I. Mitra, S. Kar, P.K. Ojha, R.N. Das, and H. Kabir, Comparative studies on some metrics for external validation of QSPR models, J. Chem. Inf. Model. 52 (2012), pp. 396–408. doi:10.1021/ci200520g
   PubMed Web of Science ®Google Scholar
• K. Roy, R.N. Das, P. Ambure, and R.B. Aher, Be aware of error measures. Further studies on validation of predictive QSAR models, Chemom. Intell. Lab. 152 (2016), pp. 18–33. doi:10.1016/j.chemolab.2016.01.008
   Web of Science ®Google Scholar
• OECD, Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models, OECD, Paris, France, 2004.
   Google Scholar
• W. Klingspohn, M. Mathea, A. ter Laak, N. Heinrich, and K. Baumann, Efficiency of different measures for defining the applicability domain of classification models, J. Cheminf. 9 (2017), pp. 44. doi:10.1186/s13321-017-0230-2
   PubMed Web of Science ®Google Scholar
• L. Cao, P. Zhu, Y. Zhao, and J. Zhao, Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids, J. Hazard. Mater. 352 (2018), pp. 17–26. doi:10.1016/j.jhazmat.2018.03.025
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemom. Intell. Lab. 145 (2015), pp. 22–29. doi:10.1016/j.chemolab.2015.04.013
   Web of Science ®Google Scholar
• J.H.I. Lienhard and J.H.V. Lienhard, A Heat Transfer Textbook, Phlogyston Press, Cambridge, MA, 2011.
   Google Scholar
• D.T. Stanton and P.C. Jurs, Development and use of charged partial surface area structural descriptors in computer-assisted quantitative structure-property relationship studies, Anal. Chem. 62 (1990), pp. 2323–2329. doi:10.1021/ac00220a013
   Web of Science ®Google Scholar
• J.H. Schuur, P. Selzer, and J. Gasteiger, The coding of the three-dimensional structure of molecules by molecular transforms and its application to structure-spectra correlations and studies of biological activity, J. Chem. Inf. Comp. Sci. 36 (1996), pp. 334–344. doi:10.1021/ci950164c
   Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, Wiley-VCH, New Jersey, 2009.
   Google Scholar