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

Autores/as

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

Palabras clave:

Punto de inflamación, Mezclas multicomponentes, Modelo Liaw-UNIFAC, Red neuronal artificial, Seguridad de procesos, No idealidad

Resumen

El punto de inflamación es un parámetro de seguridad crítico que indica la temperatura más baja a la que una mezcla líquida inflamable puede encenderse. La estimación precisa del punto de inflamación es esencial para la prevención de riesgos en el procesamiento químico y el manejo de combustibles; sin embargo, la determinación experimental es laboriosa, costosa y peligrosa. Este estudio presenta una metodología combinada de termodinámica y aprendizaje automático para predecir los puntos de inflamación de mezclas orgánicas binarias. Se utilizó un modelo termodinámico Liaw-UNIFAC para generar entradas de presión de vapor y coeficiente de actividad, que luego se utilizaron para entrenar una red neuronal artificial (RNA) para la predicción del punto de inflamación. El modelo de RNA, configurado con cuatro capas ocultas (10-20-10-5 neuronas), captura relaciones no lineales complejas entre la composición de la mezcla, las propiedades moleculares y el punto de inflamación. La evaluación del modelo comparándolo con datos de la literatura para ocho mezclas binarias diversas (que incluyen alcoholes, alcanos, aromáticos y cetonas) demuestra una alta precisión: las predicciones del punto de inflamación de la red neuronal artificial (RNA) muestran errores cuadráticos medios (ECM) inferiores a 0,1 y un coeficiente de determinación (R²) superior a 0,99 en la mayoría de los casos, coincidiendo estrechamente con los resultados experimentales y el modelo Liaw-UNIFAC. El enfoque de la RNA ofrece una fiabilidad comparable al modelo mecanicista de Liaw, a la vez que mejora significativamente la eficiencia computacional y la adaptabilidad. Estos hallazgos resaltan el potencial del modelado termodinámico híbrido con RNA para mejorar la seguridad de los procesos, al permitir una estimación rápida y precisa del punto de inflamación para mezclas complejas sin necesidad de ensayos físicos exhaustivos.

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