A Neural Networks Model for Accurate Prediction of the Flash Point of Chemical Compounds

Document Type : Research Article


1 Department of Mechanical Engineering, West Tehran Branch, Islamic Azad University, Tehran, I.R. IRAN

2 Department of Chemical Engineering, South Tehran Branch, Islamic Azad University, Tehran, I.R. IRAN


Flashpoint is one of the most important flammability characteristics of chemical compounds. In the present study, we developed a neural network model for accurate prediction of the flashpoint of chemical compounds, using the number of hydrogen and carbon atoms, critical temperature, normal boiling point, acentric factor, and enthalpy of formation as model inputs. Using a robust strategy to efficiently assign neural network parameters and evaluate the authentic performance of the neural networks, we could achieve an accurate model that yielded average absolute relative errors of 0. 97, 0. 96, 0.99 and 1.0% and correlation coefficients of 0.9984, 0.9985, 0.9981 and 0.9979 for the overall, training, validation and test sets, respectively.  These results are among the most accurate ever reported ones, to date.


Main Subjects

[1] Tarjomannejad, A., Prediction of the Liquid-Vapor Pressure Using the Artificial Neural Network-Group Contribution Method. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 34(4): 97-111 (2015).
[2] Mirshahvalad, H., et al., A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons. Molecular Informatics, 38(4): 1800094 (2019).
[3] Alibakhshi A., Mirshahvalad H., Alibakhshi S., A Modified Group Contribution Method for Accurate Prediction of Flash Points of Pure Organic Compounds, Industrial & Engineering Chemistry Research., 54: 11230-5 (2015).
[4] Alibakhshi, A., Mirshahvalad H., Alibakhshi S., Investigating the Mechanism of Effect of Carbon Nanotubes on Flame Spread Over Liquid Fuels, Fire Technology, 51(4): 759-770 (2015).
[5] Degroote E., Control Parameters of Flame Spreading in a Fuel Container, Journal of Thermal Analysis and Calorimetry, 87: 149-151 (2007).
[6] Tashtoush G., Saito K., Cremers C., Gritzo L., Study of Flame Spread over JP8 Using 2-D Holographic Interferometry, Journal of Fire Sciences., 16: 437-457 (1998).
[7] Hshieh T.T., Hshieh F.-Y., Closed-Cup Flash Points and Flammability Properties of Selected Chemical Compounds, Journal of Fire Sciences, 23: 157-71 (2005).
[8] Akbarzade K., Danaee I., Nyquist Plots Prediction Using Neural Networks in Corrosion Inhibition
of Steel by Schiff Base
, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 37(3): 135-143 (2018).
[9] Raquel Guiné, Christophe Gonçalves, Susana Matos, Fernando Gonçalves, Daniela V.T.A Costa,
Mateus Mendes, Modeling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 37(2): 193-212 (2018).
[10] Daryasafar A., Shahbazi K., Prediction Of Dynamic Viscosity of N-Alkanes at High Pressures Using
a Rigorous Approach
, Petroleum Science and Technology, 36: 333-337 (2018).
[11] Pan B., Zhu Y., Wang C., Su S., A Process Neural Network Model for Calculation of Heavy Oil Viscosity in High Water Cut Stage, Petroleum Science and Technology, 36: 313-318 (2018).
[12] Fernanda M. de Oliveira, Luciene S. de Carvalho, Leonardo S. G. Teixeira, Cristiano H. Fontes,
Kássio M. G. Lima, Anne B. F. Câmara, Heloise O. M. Araújo, Rafael V. Sales, Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel–Biodiesel Blend Using an Artificial Neural Network Model. Energy & Fuels., 31: 3913-20 (2017).
[13] Alqaheem S.S., Riazi M.-R., Flash Points of Hydrocarbons and Petroleum Products: Prediction and Evaluation of Methods, Energy & Fuels.; 31: 3578-84 (2017).
[16] Pan Y., Jiang J., Wang Z., Prediction of the Flash Points of Alkanes by Group Bond Contribution Method Using Artificial Neural Networks, Frontiers of Chemical Engineering in China, 1: 390-4 (2007).
[17] Alibakhshi A., Mirshahvalad H., Alibakhshi S., Prediction of Flash Points of Pure Organic Compounds: Evaluation of the DIPPR Database, Process Safety and Environmental Protection,, 105: 127-133 (2017).
[18] Carroll F.A., Lin C.-Y., Quina F.H., Improved Prediction of Hydrocarbon Flash Points From Boiling Point Data, Energy & Fuels, 24: 4854-6 (2010).
[19] Serat F.Z., Benkouider A.M., Yahiaoui A., Bagui F., Nonlinear Group Contribution Model for the Prediction of Flash Points Using Normal Boiling Points, Fluid Phase Equilibria. (2017).
[20] Cardoso S., Gomes J., Borges L., Hollauer E., Predictive QSPR Analysis of Corrosion Inhibitors for Super 13% Cr Steel in Hydrochloric Acid, Brazilian Journal of Chemical Engineering, 24: 547-59 (2007).
[21] Diego SaldanaMiranda, Laurie Starck, Pascal Mougin, Bernard Rousseau, Ludivine Pidol, Nicolas Jeuland, Benoit Creton, Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure-Property Relationship (QSPR) Methods, Energy & Fuels, 25: 3900-8 (2011).
[22] Katritzky A.R., Stoyanova-Slavova I.B., Dobchev D.A., Karelson M., QSPR Modeling of Flash Points: an Update, Journal of Molecular Graphics And Modelling, 26: 529-36 (2007).
[24] Tetteh J., Suzuki T., Metcalfe E., Howells S., Quantitative Structure-Property Relationships for the Estimation of Boiling Point and Flash Point Using a Radial Basis Function Neural Network. Journal of Chemical Information and Computer Sciences, 39: 491-507 (1999).
[26] Majhi A., Kukreti V., Sharma D., Sharma S., Sharma Y., Studies on Volatile Characteristics of Middle Distillates and Their Interdependency, Petroleum Science and Technology, 29: 2397-2406 (2011).
[27] Jones J.C., On The Flash Point of Benzoic Acid. Journal of Fire Sciences, 19: 177-80 (2001).
[28] Jones J., Reid Vapour Pressure as a Route to Calculating the Flash Points of Petroleum Fractions, Journal of Fire Sciences, 16: 222-229 (1998).
[29] Patil G., Estimation of Flash Point, Fire and Materials, 12: 127-31 (1988).
[31] Riazi M., Daubert T., Predicting Flash and Pour Points, Hydrocarbon Processing, 66: 81-83 (1987).
[32] Catoire L., Naudet V., A Unique Equation to Estimate Flash Points of Selected Pure Liquids Application
to the Correction of Probably Erroneous Flash Point Values
, Journal of Physical and Chemical Reference Data., 33: 1083-111 (2004).
[33] Gharagheizi F., Ilani-Kashkouli P., Farahani N., Mohammadi A.H., Gene Expression Programming Strategy for Estimation of Flash Point Temperature of Non-Electrolyte Organic Compounds, Fluid Phase Equilibria, 329: 717-77 (2012).
[34] Alibakshi A. Strategies to Develop Robust Neural Network Models: Prediction of Flash Point as a Case Study, Analytica Chimica Acta.,1026: 69-76 (2018).
[35] Demenay A., Glorian J., Paricaud P., Catoire L., Predictions of the Ideal Gas Properties of Refrigerant Molecules, International Journal of Refrigeration, 79: 207-16 (2017).
[36] Wilding W.V., Rowley R.L., Oscarson J.L., DIPPR® Project 801 Evaluated Process Design Data, Fluid Phase Equilibria., 150: 413-420 (1998).
[37] Rowley J.R., Rowley R.L., Wilding W.V., Prediction of Pure‐Component Flash Points for Organic Compounds, Fire and Materials, 35: 343-51 (2011).
[38] Mathieu D. Inductive Modeling of Physico-Chemical Properties: Flash Point of Alkanes, Journal of Hazardous Materials., 179: 1161-1164 (2010).
[39] Keshavarz M.H., Ghanbarzadeh M., Simple Method for Reliable Predicting Flash Points of Unsaturated Hydrocarbons, Journal of Hazardous Materials, 193: 335-341 (2011).
[40] Mathieu D., Alaime T., Insight into the Contribution of Individual Functional Groups to the Flash Point of Organic Compounds, Journal of Hazardous Materials, 267: 169-174 (2014).
[41] Rowley J., Rowley R., Wilding W., Estimation of the Flash Point of Pure Organic Chemicals from Structural Contributions, Process Safety Progress 29: 353-358 (2010).
[42] Hukkerikar A.S., Kalakul S., Sarup B., Young D.M., Sin Gr., Gani R., Estimation of Environment-Related Properties of Chemicals for Design of Sustainable Processes: Development of Group-Contribution+ (GC+) Property Models and Uncertainty Analysis, Journal of Chemical Information and Modeling, 52: 2823-2839 (2012).
[44] Keshavarz M.H., Jafari M., Kamalvand M., Karami A., Keshavarz Z., Zamani A., Rajaee S., A Simple and Reliable Method For Prediction of Flash Point of Alcohols Based on Their Elemental Composition and Structural Parameters, Process Safety and Environmental Protection, 102: 1-8 (2016).
[45] Khajeh A., Modarress H., QSPR Prediction of Flash Point of Esters by Means of GFA and ANFIS, Journal of Hazardous Materials, 179: 715-20 (2010).
[46] Chen C.-C., Liaw H.-J., Tsai Y.-J., Prediction of Flash Point of Organosilicon Compounds Using Quantitative Structure-Property Relationship Approach, Industrial & Engineering Chemistry Research, 49: 12702-1278 (2010).
[47] Katritzky A.R., Petrukhin R., Jain R., Karelson M., QSPR Analysis of Flash Points. Journal of Chemical Information and Computer Sciences, 41: 1521-1530 (2001).
[48] Yazdizadeh M., Nourbakhsh H., Jafari Nasr M.R., A Solution Model for Predicting Asphaltene Precipitation, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 33(1): 93-102 (2014).
[49] Gopinath S., Devan P.K., Optimization and Prediction of Reaction Parameters of Plastic Pyrolysis Oil Production Using Taguchi Method, Iranian Journal of Chemistry and Chemical Engineering (IJCCE),
39(2): 91-103 (2020).