ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Bryand Javier Carcia Sigales
Universidad Autónoma del Carmen
0009-0006-2166-3868
José A. Ruz Hernandez
Universidad Autónoma del Carmen
0000-0001-8332-4980
José Luis Rullan Lara
Universidad Autónoma del Carmen
0000-0002-6007-1025
Mario Antonio Ruz Canul
Universidad de Guadalajara
0000-0003-0872-062X
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Juan Carlos González Gómez
Universidad Autónoma del Carmen
0000-0002-5515-1530
Acerca de
The separation of Natural Gas/CO₂ using hollow fiber membranes is essential for industrial applications, particularly in the oil and gas sector. Traditional modeling approaches, such as «black-box» models based on artificial neural networks (ANNs), offer high accuracy in parameter estimation but lack physical interpretability, making them unreliable for large-scale applications with variable operating conditions. Conversely, «white-box» models, grounded in physical laws and conservation principles, provide better scalability and physical insight but often require complex derivations and exhibit high uncertainty when experimental data is insufficient or when key parameters, such as gas permeabi- lity, must be inferred from empirical correlations.
To address these limitations, this study introduces a «gray-box» approach using an Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines the data-driven adaptability of ANNs with the interpretability of fuzzy logic systems. The proposed white-box model is enhanced with ANFIS-based optimization to improve CO₂/CH₄ separation performance in hollow fiber membranes.
This study underscores the critical role of neuro-fuzzy systems in bridging physical modeling and machine learning, providing a robust and efficient approach for industrial gas separation. Integrating ANFIS with physical models enhances model reliability. It lays the foundation for scalable, real-world applications in the oil and gas industry, offering a more adaptive and data-efficient alternative to conventional approaches.
Referencias
Chen, B., Dai, Y., Ruan, X., Xi, Y., & He, G. (2018). Integration of molecular dynamic simulation and free volume theory for modeling membrane VOC/gas separation. Frontiers of Chemical Science and Engineering, 12, 296-305. https://doi.org/10.1007/s1170 5-018-
1701-3
Lu, H., Guo, L., Azimi, M., & Huang, K. (2019). Oil and Gas 4.0 era: A systematic review and outlook. Computers in Industry, 111, 68-90. https://doi.org/10.1016/j.com- pind.2019.06.007
Gbadamosi, A. O., Junin, R., Manan, M. A., Agi, A., & Yusuff, A. S. (2019). An overview of chemical enhanced oil recovery: recent advances and prospects. International Nano Let- ters. https://doi.org/10.1007/s40089-019-0272-8
Schwenzer, M., Ay, M., Bergs, T., & Abel, D. (2021). Review on model predictive control: An engineering perspective. The International Journal of Advanced Manufacturing Tech- nology, 117(5-6), 1327-1349. https://doi.org/10.1007/s00170-021-07682-3
Ruz-Hernandez, J. A., Salazar-Mendoza, R., de la C, G. J., Garcia-Hernandez, R., & Shelo- mov, E. (2010). An approach based on neural networks for gas lift optimization. Soft Com- puting for Recognition Based on Biometric, Book Chapter, 207-224, Springer. https://doi.org/10.1007/978-3-642-15111-8_13
Castro-Peñaloza, Ulises., Pitalua-Diaz, Nun., Ruz-Hernández, José & Lagunas-Jiménez, Rubén. (2009). Introducción a los Sistemas Inteligentes. Universidad Autónoma de Baja California and Universidad de Sonora.
Ng, E. L. H., Lau, K. K., Lau, W. J., Ahmad, F., & others. (2021). Holistic review on the recent development in mathematical modelling and process simulation of hollow fiber mem- brane contactor for gas separation process. Journal of Industrial and Engineering Chemistry. https://doi.org/10.1016/j.jiec.2021.08.028.
Zendehboudi, S., Rezaei, N., & Lohi, A. (2018). Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review. Applied Energy, 228, 2539–2566. https://doi.org/10.1016/j.apenergy.2018.06.051.
Asghari, M., Dashti, A., Rezakazemi, M., Jokar, E., & Halakoei, H. (2018). Application of neural networks in membrane separation. Reviews in Chemical Engineering. https://doi.org/10.1515/revce-2018-0011.
Cheng, X., Liao, Y., Lei, Z., Li, J., Fan, X., & Xiao, X. (2023). Multi-scale design of MOF- based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning, and process modeling and simulation. Journal of Membrane Science, 672, 121430. https://doi.org/10.1016/j.memsci.2023.121430.
Chu, Y., Lindbråthen, A., Lei, L., He, X., & Hillestad, M. (2019). Mathematical modeling and process parametric study of CO2 removal from natural gas by hollow fiber mem- branes. Chemical Engineering Research and Design, 148, 45-55.
Medi, B., Vesali-Naseh, M., & Haddad-Hamedani, M. (2022). A generalized numerical framework for solving cocurrent and counter-current membrane models for gas separa- tion. Heliyon, 8(3).
Khalilpour, R., Abbas, A., Lai, Z., & Pinnau, I. (2013). Analysis of hollow fibre membrane systems for multicomponent gas separation. Chemical Engineering Research and De- sign, 91(2), 332-347.
Chen, B., Ruan, X., Xiao, W., Jiang, X., & He, G. (2015). Synergy of CO2 removal and light hydrocarbon recovery from oil-field associated gas by dual-membrane process. Jour- nal of Natural Gas Science and Engineering, 26, 1254-1263.
Ruz-Hernandez, J. A., Matsumoto, Y., Arellano-Valmaña, F., Pitalúa-Díaz, N., Cabanillas- López, R. E., Abril-García, J. H., … & Velázquez-Contreras, E. F. (2019). Meteorological variables’ influence on electric power generation for photovoltaic systems located at differ- ent geographical zones in Mexico. applied sciences, 9(8), 1649.
Pitalúa-Díaz, N., Arellano-Valmaña, F., Ruz-Hernandez, J. A., Matsumoto, Y., Alazki, H., Herrera-López, E. J., … & Velázquez-Contreras, E. F. (2019). An ANFIS-based modeling comparison study for photovoltaic power at different geographical places in Mexico. Ener- gies, 12(14), 2662.
Arellano-Valmaña, F., Ruz-Hernandez, j. A., & Pitalúa-díaz, n. (2019). Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico Intelligent fore- cast model for electrical power generation in a photovoltaic system. Revista de Inge- niería, 3(10), 22-29.
Garcia-Sigales, B. J., Ruz-Hernandez, J. A., Rullán-Lara, J.-L., Alanis, A. Y., Ruz Canul,
M. A., & Salazar-Mendoza, R. (in press). An approach based on neural networks and bioin- spired systems for oil production systems optimization. In Springer-Verlag. Presented at the World Automation Congress (WAC) 2024, Cancún, Mexico.