University of Fribourg, Switzerland,Switzerland
Dr. Arianna Parrales Bahena is a dedicated and competent researcher in the field of chemical and energy engineering with expertise in simulation, process optimization, and neural networks. Her recognition by the SNI system and full-time role at CIICAp under CONAHCyT demonstrates strong national standing in Mexico. Her interdisciplinary approach involving AI and energy processes is promising for sustainable engineering solutions.
Publication Profile
Academic Background:
Dr. Arianna Parrales Bahena earned her Ph.D. in Engineering and Applied Sciences with a specialization in Chemical Technology from the Research Center in Engineering and Applied Sciences (CIICAp) at the Autonomous University of the State of Morelos (UAEM) between 2012 and 2016 (Professional License No. 10538006). She previously completed a Master’s degree in Engineering and Applied Sciences at the same institution (CIICAp-UAEM) from 2010 to 2012, also with a specialization in Chemical Technology (Professional License No. 09663681). Her academic path began with a Bachelor’s degree in Chemical Engineering with a specialization in Environmental Engineering from the Instituto Tecnológico de Zacatepec (2005–2009), where she obtained Professional License No. 6849961.
Professional Experience:
Since 2017, Dr. Parrales Bahena has served as a Full-Time Research Professor at the CIICAp-UAEM, under the Cátedras program of the National Council of Science and Technology (CONAHCyT). Earlier in her career, she worked as a Production Assistant at Industrias Químicas Falcón de México S.A. de C.V. in 2010 and was part of the production department at Dr. Reddy’s Laboratories in Jiutepec, Morelos.
Research Interests:
Her primary research areas include two-phase flows, energy transfer processes, design and optimization of heat exchangers, as well as the application of neural networks and process simulation in engineering.
Honors and Recognitions:
Dr. Parrales Bahena has been recognized by Mexico’s National System of Researchers (SNI), initially being appointed as a Candidate from 2018 to 2020. She was subsequently promoted to Level I, a distinction she has held for the 2021–2023 and 2024–2028 periods.
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Ajbar, W., Parrales, A., Cruz-Jacobo, U., Conde-Gutiérrez, R. A., Bassam, A., … (2021). The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector. Applied Thermal Engineering, 189, 116651. Citations: 70
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Ajbar, W., Parrales, A., Huicochea, A., Hernández, J. A. (2022). Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review. Renewable and Sustainable Energy Reviews, 156, 111947. Citations: 62
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Ajbar, W., Hernández, J. A., Parrales, A., Torres, L. (2023). Thermal efficiency improvement of parabolic trough solar collector using different kinds of hybrid nanofluids. Case Studies in Thermal Engineering, 42, 102759. Citations: 42
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Parrales, A., Colorado, D., Díaz-Gómez, J. A., Huicochea, A., Álvarez, A., … (2018). New void fraction equations for two-phase flow in helical heat exchangers using artificial neural networks. Applied Thermal Engineering, 130, 149–160. Citations: 36
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Solís-Pérez, J. E., Hernández, J. A., Parrales, A., Gómez-Aguilar, J. F., … (2022). Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes. Neural Networks, 152, 44–56. Citations: 35
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Reyes-Téllez, E. D., Parrales, A., Ramírez-Ramos, G. E., Hernández, J. A., … (2020). Analysis of transfer functions and normalizations in an ANN model that predicts the transport of energy in a parabolic trough solar collector. Desalination and Water Treatment, 200, 23–41. Citations: 30
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Parrales, A., Hernández-Pérez, J. A., Flores, O., Hernandez, H., … (2019). Heat transfer coefficients analysis in a helical double-pipe evaporator: Nusselt number correlations through artificial neural networks. Entropy, 21(7), 689. Citations: 24