A Hybrid Thermodynamic–Machine Learning Approach for Flash Point Prediction of Binary Organic Mixtures

Nadia Khan; Ahmed Saleem; Aisha Jilani; Asad A. Zaidi

doi:10.36561/ING.30.13

Authors

Nadia Khan NED University of Engineering and Technology, ¨Pakistán https://orcid.org/0009-0009-9029-0091
Ahmed Saleem NED University of Engineering and Technology, Pakistán https://orcid.org/0009-0004-4095-7074
Aisha Jilani NED University of Engineering and Technology, Pakistán https://orcid.org/0009-0009-5904-6680
Asad A. Zaidi NED University of Engineering and Technology, Pakistán https://orcid.org/0000-0001-5457-5684

DOI:

https://doi.org/10.36561/ING.30.13

Keywords:

Flash point, Multicomponent mixtures, Liaw–UNIFAC model, Artificial neural network, Process safety, Non-ideality

Abstract

Flash point is a critical safety parameter indicating the lowest temperature at which a flammable liquid mixture can ignite. Accurate flash point estimation is essential for hazard prevention in chemical processing and fuel handling, yet experimental determination is time-consuming, costly, and hazardous. This study presents a combined thermodynamic and machine learning methodology to predict flash points of binary organic mixtures. A Liaw–UNIFAC thermodynamic model was used to generate vapor pressure and activity coefficient inputs, which were then used to train an Artificial Neural Network (ANN) for flash point prediction. The ANN model, configured with four hidden layers (10-20-10-5 neurons), captures complex non-linear relationships between mixture composition, molecular properties, and flash point. Model evaluation against literature data for eight diverse binary mixtures (including alcohols, alkanes, aromatics, and ketones) demonstrates high accuracy: the ANN’s flash point predictions show mean squared errors (MSE) below 0.1 and R2 above 0.99 in most cases, closely matching both experimental results and the Liaw–UNIFAC model. The ANN approach offers comparable reliability to the mechanistic Liaw model while significantly improving computational efficiency and adaptability. These findings highlight the potential of hybrid thermodynamic ANN modeling to enhance process safety by enabling rapid, accurate flash point estimation for complex mixtures without exhaustive physical testing.

Downloads

Download data is not yet available.

References

C. Ding et al., "Experimental study and hazard analysis on the flash point of flammable liquids at high altitudes," Journal of Fire Sciences, vol. 31, pp. 469-477, 08/25 2013.

D. C. Do Nascimento, N. D. D. Carareto, A. M. B. Neto, V. Gerbaud, and M. C. da Costa, "Flash point prediction with UNIFAC type models of ethylic biodiesel and binary/ternary mixtures of FAEEs," Fuel, vol. 281, p. 118717, 2020.

H. Xiao, W. Wang, H. Bao, F. Li, and L. Zhou, "Biodiesel-diesel blend optimized via leave-one cross-validation based on kinematic viscosity, calorific value, and flash point," Industrial Crops and Products, vol. 191, p. 115914, 2023.

J. Fu, "Flash points measurements and prediction of biofuels and biofuel blends with aromatic fluids," Fuel, vol. 241, pp. 892-900, 04/01 2019.

B. Nazari, M. H. Keshavarz, and A. Hassanzadeh, "Reliable prediction of the flash point of organic compounds containing hazardous peroxide functional groups as compared to the best available methods," Process Safety and Environmental Protection, vol. 132, pp. 134-141, 2019.

H.-J. Liaw, W.-C. Hsu, and K. Lakzian, "Exploration of two types of maximum–minimum flash point behavior of ternary mixtures," Journal of Loss Prevention in the Process Industries, vol. 80, p. 104915, 2022.

Z. Heidari and M. A. Sobati, "New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters," Chemometrics and Intelligent Laboratory Systems, vol. 240, p. 104877, 2023.

F. H. B. Sosa, F. A. Temoteo, R. M. Dias, D. C. do Nascimento, A. M. B. Neto, and M. C. da Costa, "Flash point of binary and ternary mixture of biojet blends: Experimental data and modeling," Fluid Phase Equilibria, vol. 576, p. 113940, 2024.

S. M. Santos, D. C. Nascimento, M. C. Costa, A. M. B. Neto, and L. V. Fregolente, "Flash point prediction: Reviewing empirical models for hydrocarbons, petroleum fraction, biodiesel, and blends," Fuel, vol. 263, p. 116375, 2020/03/01/ 2020.

H.-J. Liaw and C.-A. Yang, "Maximum flash point behavior of ternary mixtures with single and two maximum flash point binary constituents," Process Safety and Environmental Protection, vol. 143, pp. 293-303, 2020.

K. Lakzian, S. Hosseiniallahchal, H. Jalaei Salmani, and A. Sanjarifard, "Flash point prediction of binary totally and partially miscible water-alcohol mixtures by cubic-plus-association (CPA) equation of state," Thermochimica Acta, vol. 691, p. 178719, 2020/09/01/ 2020.

Z. Ke, H. Wu, C. Geng, G. Wang, Y. Yang, and Y.-W. Li, "A Comparative Study of PC-SAFT EOS and Activity Coefficient Models in Phase Equilibria Calculations for Mixtures Containing Associating and Polar Components," Industrial & Engineering Chemistry Research, vol. 57, 02/06 2018, doi: 10.1021/acs.iecr.7b04758.

V. Koulocheris, M. Panteli, E. Petropoulou, V. Louli, and E. Voutsas, "Modeling of Simultaneous Chemical and Phase Equilibria in Systems Involving Non-reactive and Reactive Azeotropesi," Industrial & Engineering Chemistry Research, vol. 59, no. 18, pp. 8836-8847, 2020/05/06 2020, doi: 10.1021/acs.iecr.0c00468.

M. P. de Omena Souza, D. C. do Nascimento, D. T. Volpatto, G. G. Ribeiro, A. M. B. Neto, and M. C. da Costa, "Modeling flash points of biofuels using neural networks," Fluid Phase Equilibria", vol. 596, p.114439, 2025, doi.org/10.1016/j.fluid.2025.114439.

H. Jalaei Salmani, H. Kalani, M. R. Moradi, E. Mansouri, H.-J. Liaw, and H. Karkhanechi, "A practical hybrid machine learning method for predicting the flash point of complex ternary alcohol-based mixtures," Petroleum Science and Technology, vol. 42, no. 25, pp. 4390-4404, 2024, doi.org/10.1080/10916466.2023.2246489.

B. Aljaman, U. Ahmed, U. Zahid, M. R. Vanteru, M. Sarathy, and A. G. Abdul Jameel, "A comprehensive neural network model for predicting flash point of oxygenated fuels using a functional group approach," Fuel, vol. 317, p. 123428, 06/01 2022, doi.org/10.1016/j.fuel.2022.123428.

A. Álvarez, M. n. Lapuerta, and J. R. Agudelo, "Prediction of flash-point temperature of alcohol/biodiesel/diesel fuel blends," Industrial & Engineering Chemistry Research, vol. 58, no. 16, pp. 6860-6869, 2019, doi: 10.1021/acs.iecr.9b00843.

Y. Pan, J. Cheng, X. Song, G. Li, L. Ding, and J. Jiang, "Flash points measurements and prediction for binary miscible mixtures," Journal of Loss Prevention in the Process Industries, vol. 34, pp. 56-64, 2015, doi.org/10.1016/j.jlp.2015.01.022.

X. Huo, Q. Lu, and J. Wang, "Liaw-UNIFAC flash point model for alcohols-kerosene/diesel fuel blends using average fuel structure," Process Safety and Environmental Protection, 2022, doi.org/10.1016/j.indcrop.2022.115914.

Z. Liu and X. Yang, "Insight of low flammability limit on sustainable aviation fuel blend and prediction by ANN model," Energy and AI, vol. 18, p. 100423, 2024, doi: 10.1016/j.egyai.2024.100423.

H. Mirshahvalad, R. Ghasemiasl, and N. Raoufi, "A Neural Networks Model for Accurate Prediction of the Flash Point of Chemical Compounds," Iranian Journal Of Chemistry & Chemical Engineering-International English Edition, vol. 39, pp. 297-304, 11/10 2020, doi: 10.30492/ijcce.2019.35001.

H.-J. Liaw and Q.-R. Tang, "Flash point prediction of binary mixtures of ionic liquid and flammable solvent," Journal of Loss Prevention in the Process Industries", vol. 96, p. 105631, 2025, doi.org/10.1016/j.jlp.2025.105631.

R. C. Boehm, R. Parker, Z. Yang, S. Dooley, and J. S. Heyne, "Blend Prediction Model for Vapor Pressure of Jet Fuel Range Hydrocarbons," Sustainability, vol. 17, no. 21, p. 9612, 2025, doi.org/10.3390/su17219612.

H.-J. Liaw, "Prediction of the influence of pressure on flash points of liquid fuels at sub-atmospheric pressure," Journal of Loss Prevention in the Process Industries, vol. 91, p. 105373, 2024/10/01/ 2024, doi.org/10.1016/j.jlp.2024.105373.

D. b. Costa do Nascimento, M. c. P. d. O. Souza, L. d. O. Hentges, R. M. Dias, A. Marinho Barbosa Neto, and M. C. a. o. d. Costa, "Mixture flash point calculation: recent advances and a closer look at biodiesel," ACS Chemical Health & Safety, vol. 31, no. 1, pp. 22-43, 2024,doi.org/10.1016/j.fuel.2020.118717.

F. H. B. Sosa, F. A. Temoteo, R. M. Dias, D. Costa do Nascimento, A. M. B. Neto, and M. C. da Costa, "Flash point of binary and ternary mixture of biojet blends: Experimental data and modeling," Fluid Phase Equilibria, vol. 576, p. 113940, 2024/01/01/ 2024, doi.org/10.1016/j.fluid.2023.113940.