A Hybrid Thermodynamic–Machine Learning Approach for Flash Point Prediction of Binary Organic Mixtures
Un enfoque híbrido termodinámico-aprendizaje automático para la predicción del punto de inflamación de mezclas orgánicas binarias
Uma abordagem híbrida de termodinâmica e aprendizado de máquina para a previsão do ponto de fulgor de misturas orgânicas binárias
| 2026 | |
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Punto de inflamación Mezclas multicomponentes Modelo Liaw-UNIFAC Red neuronal artificial Seguridad de procesos No idealidad Flash point Multicomponent mixtures Liaw–UNIFAC model Artificial neural network Process safety Non-ideality Ponto de fulgor Misturas multicomponentes Modelo Liaw-UNIFAC Rede neural artificial Segurança de processos Não idealidade |
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| Español | |
| Universidad de Montevideo | |
| REDUM | |
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https://revistas.um.edu.uy/index.php/ingenieria/article/view/1983
https://hdl.handle.net/20.500.12806/3450 |
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| Acceso abierto | |
| Atribución 4.0 Internacional |
| Sumario: | 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. |
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