Derechos de autor 2025 Investigación e Innovación en Ingenierías

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Desarrollo de un sensor virtual de penetración para el asfalto generado en una unidad de destilación de crudos
Corresponding Author(s) : Giovanni Morales Medina
Investigación e Innovación en Ingenierías,
Vol. 13 Núm. 1 (2025): Enero - junio
Resumen
Objetivo: El objetivo del presente trabajo corresponde a desarrollar un sensor virtual basado en redes neuronales artificiales para la predicción de la penetración de los asfaltos generados en una unidad de destilación en una refinería nacional. Metodología: Los datos de proceso de la unidad de destilación fueron recolectados en una ventana de 4 años, incluyendo 47 variables de las torres atmosférica y de vacío. La arquitectura feedforward fue utilizada para las redes, según el Toolbox nntool de MATLAB. Los hiperparámetros evaluados fueron funciones de transferencia (Logsig y Tansig) y número de neuronas ocultas, considerando los estadísticos de desempeño MSE y R2 para las etapas entrenamiento, validación y prueba. El algoritmo input perturbation fue aplicado para determinar la influencia de las variables de entrada en la predicción de la red de mejor desempeño. Resultados: La arquitectura de red denominada 47:40:1–Tansig (47 entradas, 40 neuronas ocultas con función Tansig y 1 variable de predicción) reportó el mejor desempeño en la predicción de la penetración de los asfaltos, con MSE=0,008 y R2=0,86. También, esta red mostró resiliencia respecto a datos atípicos. Por otra parte, el procedimiento input perturbation aplicado a la red 47:40:1–Tansig reveló que las variables con mayor influencia fueron el flujo del reflujo inferior y el vapor de despojo. Conclusión: El sensor virtual desarrollado en el presente documento constituye una herramienta para la predicción semicuantitativa de la penetración del asfalto producido en la unidad de destilación nacional.
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- [1] INTERNATIONAL ASTM D5-06, Método de Ensayo Estándar para Penetración de Materiales Bituminosos. ASTM. 2011.
- [2] L. Fortuna, S. Graziani, and M. G. Xibilia, “Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 8, pp. 2444–2451, Aug. 2009, doi: https://doi.org/10.1109/tim.2009.2016386.
- [3] R. Watkins, Petroleum Refinery Distillation. 2nd edition. Gulf publishing Co. 1979.
- [4] J. Wauquier, El Refino Del Petróleo: Petróleo crudo, Productos Petrolíferos, Esquemas de Fabricación. España: Ediciones Díaz de Santos. 2004.
- [5] M. Riazi, Characterization and Properties of Petroleum Fractions. ASTM international. 2005.
- [6] INVIAS. Materiales y mezclas asfálticas y prospección de pavimentos. Secciones 700 y 800. 2012. https://caminoscomunitarios.invias.gov.co/docs/caja-herramientas/anexos-modulo-tecnico/normas-ensayo/normas_ensayo_700_800_1.pdf. (Accesed on may 18 2024).
- [7] J. R. Hilera-Gonzáles and V. J. Martinez-Hernando, Redes Neuronales Artificiales Fundamentos, Modelos y Aplicaciones. Madrid España: Alfaomega Grupo Editor, S.A. 2000.
- [8] R. Robert, Neural virtual sensor for the inferential prediction of product quality from process variables. Universitat Rovira i Virgili. 2002.
- [9] M. Dam and D. N. Saraf, “Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products,” Computers & chemical engineering, vol. 30, no. 4, pp. 722–729, Feb. 2006, doi: https://doi.org/10.1016/j.compchemeng.2005.12.001.
- [10] A. Rogina, I. Šiško, I. Mohler, Ž. Ujević, and N. Bolf, “Soft sensor for continuous product quality estimation (in crude distillation unit),” Chemical Engineering Research and Design, vol. 89, no. 10, pp. 2070–2077, Oct. 2011, doi: https://doi.org/10.1016/j.cherd.2011.01.003.
- [11] L. Fortuna, S. Graziani, and M. G. Xibilia, “Soft sensors for product quality monitoring in debutanizer distillation columns,” Control Engineering Practice, vol. 13, no. 4, pp. 499–508, Apr. 2005, doi: https://doi.org/10.1016/j.conengprac.2004.04.013.
- [12] N. Bolf, M. Ivandic, and G. Galinec, “Soft sensors for crude distillation unit product properties estimation and control,” 16th Mediterranean Conference on Control and Automation, Ajaccio, France. Jun. 2008, doi: https://doi.org/10.1109/med.2008.4602099.
- [13] J. D. Olden, M. K. Joy, and R. G. Death, “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data,” Ecological Modelling, vol. 178, no. 3–4, pp. 389–397, Nov. 2004, doi: https://doi.org/10.1016/j.ecolmodel.2004.03.013.
- [14] A. Khamis, Z. Ismail, K. Haron, and A. T. Mohamm, “The Effects of Outliers Data on Neural Network Performance,” Journal of Applied Sciences, vol. 5, no. 8, pp. 1394–1398, Jul. 2005, doi: https://doi.org/10.3923/jas.2005.1394.1398.
- [15] B. Zou, M. Chibawe, B. Hu, and Y. Deng, “A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction,” Archives of Civil Engineering, vol 69, no. 2, pp 503–515, Jun. 2023, doi: https://doi.org/10.24425/ace.2023.145281.
- [16] T. C. Nguyen, A. N. Do, V. V. Pham, and G. Alexandr, “Multiple linear regression analysis model and artificial neural network model to calculate and estimate the blast induced area of the tunnel face. A case study Deo Ca tunnel,” Journal of Mining and Earth Sciences, vol. 63, no. 3, pp. 43–52, Jun. 2022, doi: https://doi.org/10.46326/jmes.2022.63(3).06.
- [17] A. Ekiugbo, P. Amiolemhen, and G. O. Ariavie, “Performance of Multiple Linear Regression and Artificial Neural Network in Predicting Risk Index,” NIPES - Journal of Science and Technology Research, vol. 3, no 4, Dec. 2021. https://doi.org/10.37933/nipes/3.4.2021.24
- [18] C. M. Rebello et al., “Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning,” Processes, vol. 10, no. 2, pp. 250–250, Jan. 2022, doi: https://doi.org/10.3390/pr10020250
- [19] J. Hu, M. Wu, L. Chen, W. Cao, and W. Pedrycz, “Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process,” Journal of Process Control, vol. 111, pp. 97–105, Mar. 2022, doi: https://doi.org/10.1016/j.jprocont.2022.02.002
- [20] G. Morales and S. Reyes Angarita, “Detection of operational failures with artificial neural networks: application to the tennessee eastman process”, Revista Ingenierías Universidad De Medellín, vol. 23, no. 44, Apr. 2024. https://doi.org/10.22395/rium.v23n44a1
- [21] A. Nag, Hydrocarbon Processing and Refining. 2022. doi: https://doi.org/10.1201/9781003268246
Referencias
[1] INTERNATIONAL ASTM D5-06, Método de Ensayo Estándar para Penetración de Materiales Bituminosos. ASTM. 2011.
[2] L. Fortuna, S. Graziani, and M. G. Xibilia, “Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 8, pp. 2444–2451, Aug. 2009, doi: https://doi.org/10.1109/tim.2009.2016386.
[3] R. Watkins, Petroleum Refinery Distillation. 2nd edition. Gulf publishing Co. 1979.
[4] J. Wauquier, El Refino Del Petróleo: Petróleo crudo, Productos Petrolíferos, Esquemas de Fabricación. España: Ediciones Díaz de Santos. 2004.
[5] M. Riazi, Characterization and Properties of Petroleum Fractions. ASTM international. 2005.
[6] INVIAS. Materiales y mezclas asfálticas y prospección de pavimentos. Secciones 700 y 800. 2012. https://caminoscomunitarios.invias.gov.co/docs/caja-herramientas/anexos-modulo-tecnico/normas-ensayo/normas_ensayo_700_800_1.pdf. (Accesed on may 18 2024).
[7] J. R. Hilera-Gonzáles and V. J. Martinez-Hernando, Redes Neuronales Artificiales Fundamentos, Modelos y Aplicaciones. Madrid España: Alfaomega Grupo Editor, S.A. 2000.
[8] R. Robert, Neural virtual sensor for the inferential prediction of product quality from process variables. Universitat Rovira i Virgili. 2002.
[9] M. Dam and D. N. Saraf, “Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products,” Computers & chemical engineering, vol. 30, no. 4, pp. 722–729, Feb. 2006, doi: https://doi.org/10.1016/j.compchemeng.2005.12.001.
[10] A. Rogina, I. Šiško, I. Mohler, Ž. Ujević, and N. Bolf, “Soft sensor for continuous product quality estimation (in crude distillation unit),” Chemical Engineering Research and Design, vol. 89, no. 10, pp. 2070–2077, Oct. 2011, doi: https://doi.org/10.1016/j.cherd.2011.01.003.
[11] L. Fortuna, S. Graziani, and M. G. Xibilia, “Soft sensors for product quality monitoring in debutanizer distillation columns,” Control Engineering Practice, vol. 13, no. 4, pp. 499–508, Apr. 2005, doi: https://doi.org/10.1016/j.conengprac.2004.04.013.
[12] N. Bolf, M. Ivandic, and G. Galinec, “Soft sensors for crude distillation unit product properties estimation and control,” 16th Mediterranean Conference on Control and Automation, Ajaccio, France. Jun. 2008, doi: https://doi.org/10.1109/med.2008.4602099.
[13] J. D. Olden, M. K. Joy, and R. G. Death, “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data,” Ecological Modelling, vol. 178, no. 3–4, pp. 389–397, Nov. 2004, doi: https://doi.org/10.1016/j.ecolmodel.2004.03.013.
[14] A. Khamis, Z. Ismail, K. Haron, and A. T. Mohamm, “The Effects of Outliers Data on Neural Network Performance,” Journal of Applied Sciences, vol. 5, no. 8, pp. 1394–1398, Jul. 2005, doi: https://doi.org/10.3923/jas.2005.1394.1398.
[15] B. Zou, M. Chibawe, B. Hu, and Y. Deng, “A comparative analysis of artificial neural network predictive and multiple linear regression models for ground settlement during tunnel construction,” Archives of Civil Engineering, vol 69, no. 2, pp 503–515, Jun. 2023, doi: https://doi.org/10.24425/ace.2023.145281.
[16] T. C. Nguyen, A. N. Do, V. V. Pham, and G. Alexandr, “Multiple linear regression analysis model and artificial neural network model to calculate and estimate the blast induced area of the tunnel face. A case study Deo Ca tunnel,” Journal of Mining and Earth Sciences, vol. 63, no. 3, pp. 43–52, Jun. 2022, doi: https://doi.org/10.46326/jmes.2022.63(3).06.
[17] A. Ekiugbo, P. Amiolemhen, and G. O. Ariavie, “Performance of Multiple Linear Regression and Artificial Neural Network in Predicting Risk Index,” NIPES - Journal of Science and Technology Research, vol. 3, no 4, Dec. 2021. https://doi.org/10.37933/nipes/3.4.2021.24
[18] C. M. Rebello et al., “Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning,” Processes, vol. 10, no. 2, pp. 250–250, Jan. 2022, doi: https://doi.org/10.3390/pr10020250
[19] J. Hu, M. Wu, L. Chen, W. Cao, and W. Pedrycz, “Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process,” Journal of Process Control, vol. 111, pp. 97–105, Mar. 2022, doi: https://doi.org/10.1016/j.jprocont.2022.02.002
[20] G. Morales and S. Reyes Angarita, “Detection of operational failures with artificial neural networks: application to the tennessee eastman process”, Revista Ingenierías Universidad De Medellín, vol. 23, no. 44, Apr. 2024. https://doi.org/10.22395/rium.v23n44a1
[21] A. Nag, Hydrocarbon Processing and Refining. 2022. doi: https://doi.org/10.1201/9781003268246