LA INGENIERÍA DE SISTEMAS DE PROCESOS Y SUS APLICACIONES EN LAS INDUSTRIAS QUÍMICA Y PETROQUÍMICA
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Abubakar, M.Y., Reuben, P., Adeshewa, T.O., y Adam, A.B. (2024). Recent advances in sustainable chemical process design in green chemistry- a review. International Research Journal of Chemistry, 44, 1782.
Acar, E., Bayrak, G., Jung, Y., Lee, I., Ramu, P., y Ravichandran, S. S. (2021). Modeling, analysis, and optimization under uncertainties: a review. Structural and Multidisciplinary Optimization, 64(5), 2909-2945. https://doi.org/10.1007/s00158-021-03026-7
Ahang, M., Charter, T., Abbasi, M., Khadivi, M., Ogunfowora, O., y Najjaran, H. (2024). Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies. arXiv preprint.
https://doi.org/10.48550/arXiv.2401.10266
Aristizábal-Marulanda, V., y Cardona Alzate, C.A. (2019). Methods for designing and assessing biorefineries: Review, Biofuels, Bioproducts and Biorefining, 13(3), 789–808. https://doi.org/10.1002/bbb.1961
Baldea, M. (2015). From process integration to process intensification. Computers & Chemical Engineering, 81, 104-114. https://doi.org/10.1016/j.compchemeng.2015.03.011
Banerjee, S., y Agarwal, R.K. (2017). Review of recent advances in process modeling and computational fluid dynamics simulation of chemical-looping combustion. International Journal of Energy for a Clean Environment, 18, 1-37. https://doi.org/10.1615/InterJEnerCleanEnv.2017019433
Bernal Neira, D.E., Gómez-Castro, F.I., del-Río-Chanona, E.A. y Rico-Ramírez, V. (2025). Future insights for optimization in chemical engineering. En F.I. Gómez-Castro, V. Rico-Ramírez (Eds.). Optimization in Chemical Engineering: Deterministic, Meta-Heuristic and Data-Driven Techniques (pp. 425-444). De Gruyter.
Bouchouit, K., y Elhamma, A. (2025). The carbon footprint as a key indicator for environmental management controllers: Towards environmental performance – A literature review. European Scientific Journal, 21(4), 109-132. https://doi.org/10.19044/esj.2025.v21n4p109
Boy, O.C., Nogueira, I.B.R., Di Caprio, U., y Leblebici, M.E. (2025). AI-aided process intensification of structures. Chemical Engineering and Processing - Process Intensification, 216, 110406. doi: https://doi.org/10.1016/j.cep.2025.110406
Cao, L., Su, J., Conde, E., Siang, L.C., Cao, Y., Gopaluni, B. (2025). A novel automated soft sensor design tool for industrial applications based on machine learning. Control Engineering Practice, 160, 106322. https://doi.org/10.1016/j.conengprac.2025.106322
Chau-Chyun, C., Gorensek, M.B., Scott, J.K. y Palou-Rivera, I. (2020). Process Modeling of Intensified Chemical Processes. Chemical Engineering Progress, March 2020. https://www.aiche.org/resources/publications/cep/2020/march/process-modeling-intensified-chemical-processes
Chen, K., Ji, J., Wang, H., Liu, Y., Song, Z. (2011). Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes. Chemical Engineering Research and Design, 89(11), 2117–2124. https://doi.org/10.1016/j.cherd.2011.01.032
Daoutidis, P., Lee, J.H., Rangarajan, S., Chiang, L., Gopaluni, B., Schweidtmann, A.M., Harjunkoski, I., Mercangöz, M., Mesbah, A., Boukouvala, F., Lima, F.V., del Río Chanona, A., y Georgakis, C. (2024). Machine learning in process systems engineering: Challenges and opportunities. Computers & Chemical Engineering, 181, 108523. https://doi.org/10.1016/j.compchemeng.2023.108523
De Prada, C., Galán-Casado, S., Pitarch, J.L., Sarabia, D., Galán, A. y Gutiérrez, G. (2022). Gemelos Digitales en la Industria de Procesos. Revista Iberoamericana de Automática e Informática Industrial, 19(3), 285–296. https://doi.org/10.4995/riai.2022.16901
Dutta, D., y Upreti, S.R. (2021). Artificial intelligence-based process control in chemical, biochemical, and biomedical engineering. The Canadian Journal of Chemical Engineering, 99 (11), 2467-2504. https://doi.org/10.1002/cjce.24246
El-Halwagi, M.M. (2006). Process Integration, Vol. 7. Academic Press.
Energies Media. (2025). AI in oil and gas: preventing equipment failures before they cost millions. https://energiesmedia.com
Furda, P., Variny, M., Labovská, Z., y Cibulka, T. (2020). Process drive sizing methodology and multi-level modeling linking MATLAB® and Aspen Plus® environment. Processes, 8(11), 1495. https://doi.org/10.3390/pr8111495
Gani, R., Bernal Neira, D.E., Pistikopoulos, E.N., y Georgakis, C. (2026). Data, models, algorithms, AI and the role of PSE – the generation next. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2026.109564
García-Maza, S., Acosta-Esalas, L. J., y González-Delgado, A. D. (2025). Mass integration of a large-scale gas oil hydrocracking process using pinch analysis. Chemical Engineering Transactions, 119, 421-426. https://doi.org/10.3303/CET25119071
Global Market Insights. (2025). Digital Twin Market Size & Share, Growth Analysis 2025-2034. https://www.gminsights.com
Grossmann, I.E. y Westerberg, A.W. (2000). Research challenges in process systems engineering. AIChE Journal, 46(9), 1700-1703. https://doi.org/10.1002/aic.690460902
Haldurai, L., Madhubala, T., y Rajalakshmi, R. (2016). A study on genetic algorithm and its applications. International Journal of Computer Sciences and Engineering, 4(10), 139-143.
Hartung, T., Franks, B.J., Michels, T. Wagner, D., Liznerski, P., Reithermann, S., Fellenz, S., Jirasek, F., Rudolph, M., Neider, D., Leitte, H., Song, C., Kloepper, B., Mandt, S., Bortz, M., Burger, J., Hasse, H., y Kloft, M. (2023). Deep anomaly detection on Tennessee Eastman process data. Chemie Ingenieur Technik, 95(7), 1077-1082. https://doi.org/10.1002/cite.202200238
Häussling Löwgren, B., Hoffmann, C., Vijver, M.G., Steubing, B., y Cardellini, G. (2025). Towards sustainable chemical process design: Revisiting the integration of life cycle assessment. Journal of Cleaner Production, 491, 144831. https://doi.org/10.1016/j.jclepro.2025.144831
Hernández-Betancur, J. D., Chea, J.D., Perez, D., y Ruiz-Mercado, G.J. (2026). Integrating data engineering and process systems engineering for end-of-life chemical flow analysis. Computers & Chemical Engineering, 204, 109414. https://doi.org/10.1016/j.compchemeng.2025.109414
Hu, Z., Li, P., y Liu, Y. (2022). Enhancing the performance of evolutionary algorithm by differential evolution for optimizing distillation sequence. Molecules, 27(12), 3802. https://doi.org/10.3390/molecules27123802
IAIA (1999). Principles of environmental impact assessment best practice. https://iaia.org/wp-content/uploads/2025/02/BEST-PRACTICE-Principles-of-EIA.pdf
IBM (2025). What is environment, health and safety (EHS)? https://www.ibm.com/think/topics/environmental-health-safety
Jamaludin, N.F., Shuhaimi, N.A., Mohamed, O.Y., y Hadipornama, M.F. (2023). Quantification of carbon footprint in petroleum refinery process. Chemical Engineering Transactions, 106, 97-102. https://doi.org/10.3303/CET23106017
Kang, S., Li, K., y Wang, R. (2024). A survey on Pareto front learning for multi-objective optimization. Journal of Membrane Computing, 7, 128-134. https://doi.org/10.1007/s41965-024-00170-z
Kongsberg Digital (2025). 12 Game-Changing Ways Digital Twins Can Boost the Chemical. Industry. https://kongsbergdigital.com/blog/12-game-changing-ways-digital-twins-can-boost-the-chemical-industry
Kramer, O. (2017). Genetic Algorithm Essentials. Springer.
Li, Y., Li, Z., Ren, J., Du, W., y Shen, W. (2025). A high-accuracy deep learning framework for digital twin model development of actual chemical processes. Engineering Applications of Artificial Intelligence, 159, 111780. https://doi.org/10.1016/j.engappai.2025.111780
Lin, M. H., Tsai, J. F., y Yu, C. S. (2012). A review of deterministic optimization methods in engineering and management. Mathematical Problems in Engineering, 2012(1), 756023. https://doi.org/10.1155/2012/756023
Liñán, D. A., y Ricardez-Sandoval, L. A. (2025). Trends and perspectives in deterministic MINLP optimization for integrated planning, scheduling, control, and design of chemical processes. Reviews in Chemical Engineering, 41(5), 451-472. https://doi.org/10.1515/revce-2024-0064
Melo, A., Câmara, M.M., y Pinto, J.C. (2024). Data-driven process monitoring and fault diagnosis: A comprehensive survey. Processes, 12(2), 251. https://doi.org/10.3390/pr12020251
Muñoz de Cote Orozco, J. (2010). Las bebidas alcohólicas en la historia de la humanidad. AAPAUNAM Academia, Ciencia y Cultura, Febrero 2010, 42-52.
Marion, P., Bernela, B., Piccirilli, A., Estrine, B., Patouillard, N., Guilbot, J. y Jérôme, F. (2017). Sustainable chemistry: how to produce better and more from less? Green Chemistry, 19, 4973-4989. https://doi.org/10.1039/C7GC02006F
Mohammadpoor, M., y Torabi, F. (2020). Big Data analytics in oil and gas industry: An emerging trend. Petroleum, 6(4), 321–328. https://doi.org/10.1016/j.petlm.2018.11.001
Mousa, J., Negny, S. y Ouaret, R. (2025). Hybrid neural networks for improved chemical process modeling: Bridging data-driven insights with physical consistency. Digital Chemical Engineering, 16, 100256. https://doi.org/10.1016/j.dche.2025.100256
Nele, L., Mattera, G., Yap, E.W., Vozza, M., y Vespoli, S. (2024). Towards the application of machine learning in digital twin technology: a multi-scale review. Discover Applied Sciences, 6(10), 502. https://doi.org/10.1007/s42452-024-06206-4
Nikkhah, H., Aghayev, Z., Shahbazi, A., Charitopoulos, V. M., Avraamidou, S., y Beykal, B. (2025). Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems. Digital Chemical Engineering, 14, 100218. https://doi.org/10.1016/j.dche.2025.100218
Pal, P. K., Hens, A., Behera, N. y Lahiri, S. K. (2025). Digital twins: Transforming the chemical process industry—A review. The Canadian Journal of Chemical Engineering, 103(8), 3611-3636. https://doi.org/10.1002/cjce.25611
Palotai, B., Kis, G., Abonyi, J., y Bárkányi, A. (2025). Surrogate-based flowsheet model maintenance for Digital Twins. Digital Chemical Engineering, 15, 100228. https://doi.org/10.1016/j.dche.2025.100228
Pappas, I., Bindlish, R., Ali, M., y Pistikopoulos, E.N. (2023). Optimal operation of an industrial dividing wall column through multiparametric programming. Industrial & Engineering Chemistry Research, 62(37), 15029–15035. https://doi.org/10.1021/acs.iecr.3c00836
Pereira, C.J. (1999). Environmentally friendly processes. Chemical Engineering Science, 54, 1959-1973. https://doi.org/10.1016/S0009-2509(98)00504-1
Peres, F., y Castelli, M. (2021). Combinatorial optimization problems and metaheuristics: Review, challenges, design, and development. Applied Sciences, 11(14), 6449. https://doi.org/10.3390/app11146449
Peterson, L., Gosea, I.V., Benner, P., y Sundmacher, Kai. (2025). Digital twins in process engineering.: An overview on computational and numerical methods. Computers & Chemical Engineering. 193, 108917. https://doi.org/10.1016/j.compchemeng.2024.108917
Pistikopoulos, E.N., Barbosa-Povoa, A., Lee, J.H., Misener, R., Mitsos, A., Reklaitis, G.V., Venkatasubramanian, V., You, F. y Gani, R. (2021). Process systems engineering – The generation next? Computers and Chemical Engineering, 147, 107252. https://doi.org/10.1016/j.compchemeng.2021.107252
Ponce-Ortega, J. M., y Hernández-Pérez, L. G. (2019). Optimization of Process Flowsheets through Metaheuristic Techniques. Springer.
Putri, A.R., Yusoff, M., y Sallehud-din, M.T.M. (2024). A review of predictive analytics models in the oil and gas industries. Sensors, 24(12), 4013.
Raven, D.B., Chikkula, Y., Patel, K.M., Al-Ghazal, A.H., Salloum, H.S., Bakhurji, A.S., y Patwardhan, R.S. (2024). Machine learning & conventional approaches to process control & optimization: Industrial applications & perspectives. Computers & Chemical Engineering, 189, 108789. https://doi.org/10.1016/j.compchemeng.2024.108789
Reay, D., Ramshaw, C., y Harvey, A. (2013). Process intensification: Engineering for Efficiency, Sustainability and Flexibility (2a ed.). Butterworth-Heinemann.
Regufe, M.J., Santana, V.V., Ferreira, A.F., Ribeiro, A.M., Loureiro, J.M. y Nogueira, I.B. (2021). A hybrid modeling framework for membrane separation processes: Application to lithium-ion recovery from batteries. Processes, 9(11), 1939. https://doi.org/10.3390/pr9111939
Rodríguez-Ángeles, M.A., Gómez-Castro, F.I., Segovia-Hernández, J.G., y Uribe-Ramírez, A.R. (2015). Mechanical design and hydrodynamic analysis of sieve trays in a dividing wall column for a hydrocarbon mixture. Chemical Engineering and Processing: Process Intensification, 97, 55-65. https://doi.org/10.1016/j.cep.2015.09.002
Sansana, J., Joswiak, M.N., Castillo, I., Wang, Z., Rendall, R., Chiang, L.H. y Reis, M.S. (2021). Recent trends on hybrid modeling for Industry 4.0. Computers & Chemical Engineering, 151, 107365. https://doi.org/10.1016/j.compchemeng.2021.107365
Sharma, S., y Kumar, V. (2022). A comprehensive review on multi-objective optimization techniques: Past, present and future. Archives of Computational Methods in Engineering, 29(7), 5605-5633. https://doi.org/10.1007/s11831-022-09778-9
Sharma, N. y Liu, Y.A. (2022). A hybrid science‐guided machine learning approach for modeling chemical processes: A review. AIChE Journal, 68(5), e17609. https://doi.org/10.1002/aic.17609
Sheldon, R.A. (2007). The E Factor: fifteen years on. Green Chemistry, 9(12), 1273–1283. https://doi.org/10.1039/B713736M
Sinnott, R., y Towler, G. (2020a). Introduction to Design. En R. Sinnott, G. Towler (Eds.). Chemical Engineering Design (1–45). Butterworth-Heinemann.
Sinnott, R., y Towler, G. (2020b). Chemical Engineering Design. Elsevier.
Smith, R. (2005). Chemical Process Design and Integration. John Wiley & Sons Ltd.
Srinivasan, K., Puliyanda, A., Thosar, D., Bhakte, A., Singh, K., Addo, P., Srinivasan, R., y Prasad, V. (2025). Artificial intelligence and machine learning at various stages and scales of process systems engineering. Canadian Journal of Chemical Engineering, 103(3), 1004-1035. https://doi.org/10.1002/cjce.25525
Tian, Y., Demirel, S.E., Hasan, M.M.F., y Pistikopoulos, E. N. (2018). An overview of process systems engineering approaches for process intensification: State of the art. Chemical Engineering and Processing: Process Intensification, 133, 160-210. https://doi.org/10.1016/j.cep.2018.07.014
Torres, A. I., Ferreira, J., y Pedemonte, M. (2025). Machine learning and process systems engineering for sustainable chemical processes – A short review. Current Opinion in Green and Sustainable Chemistry, 51, 100982.
Turton, R., Bailie, R.C., Whiting, W.B., Shaeiwitz, J.A., y Bhattacharyya, D. (2012) Analysis, Synthesis, and Design of Chemical Processes. Prentice Hall.
Van Antwerpen, F.J. (1980). The origins of Chemical Engineering. En W.F. Furter (Ed.). History of Chemical Engineering (pp. 1-14). American Chemical Society.
Vázquez, F.J., Abreu, M., y Lemos, J.M. (2023). A framework for enhancing industrial soft sensor learning models. Digital Chemical Engineering, 7, 100093.
Willis, M.S. (1966). Unit operations to transport phenomena. Chemical Engineering Education, Abril 1966, 46-51.
Xiao, Z., Kordon, A., y Sen, S. (2023). Fault detection and diagnosis in Tennessee Eastman process with deep autoencoder. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3578
Young, D.M., y Cabezas, H. (1999) Designing sustainable processes with simulation: the waste reduction (WAR) algorithm. Computers & Chemical Engineering, 23, 1477–1491. https://doi.org/10.1016/S0098-1354(99)00306-3
Zhai, J., Liu, Y., Li, L., Zhu, Y., Zhong, W., y Sun, L. (2015). Applications of dividing wall column technology to industrial-scale cumene production. Chemical Engineering Research and Design, 102, 138–149. https://doi.org/10.1016/j.cherd.2015.06.020
Zhang, Y., Wang, S., y Ji, G. (2015). A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering, 2015(1), 931256. https://doi.org/10.1155/2015/931256
Zhang, Y., Wang, W., Yang, Q., Tang, X., Ruan, W., Li, Y., Daoerji, S., Zhang, X., Ye, Y., Huang, J., Li, J., Yang, Y., Wu, X., Yang, H., y Cao, T. (2024). Promoting digital twin technology application for process industry: A novel generation modelling platform and its implementations. Digital Twins and Applications, 1(1), 51–74. https://doi.org/10.1049/dgt2.12010
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