Artificial intelligence applied to solar collectors for domestic water heating
DOI:
https://doi.org/10.30973/inventio/2022.18.45/1Keywords:
artificial intelligence, thermal solar collectors, renewable energies, mathematics simulationsAbstract
Most researchers have promoted the use of renewable energies to reduce fossil fuels. The sun is the cleanest renewable source. One way to harness it is through parabolic solar collectors PTC. This paper shows the development of two mathematical simulation models to predict the output temperature of a PTC system by applying the artificial intelligence water heating technique. The models showed satisfactory results, allowing to control the process in an efficient and intelligent way. The developed methodology shows the potential to be implemented in other energy systems to predict their patterns.
References
Ajbar, W., Parrales, A., Silva-Martínez, S., Bassam, A., Jaramillo, O. A. y Hernández, J. A. (2021). Identification of the Relevant Input Variables for Predicting the Parabolic Trough Solar Collector’s Outlet Temperature Using an Artificial Neural Network and a Multiple Linear Regression Model. Journal of Renewable and Sustainable Energy, 13(4), 043701. https://doi.org/10.1063/5.0055992
Bakos, G. C. (2006). Design and construction of a two-axis Sun tracking system for parabolic trough collector (PTC) efficiency improvement. Renewable Energy, 31(15), 2411-2421. https://doi.org/10.1016/j.renene.2005.11.008
Behar, O., Khellaf, A. y Mohammedi, K. (2013). A review of studies on central receiver solar thermal power plants. Renewable and Sustainable Energy Reviews, 23, 12-39. https://doi.org/10.1016/j.rser.2013.02.017
Bellos, E., Tzivanidis, C., Antonopoulos, K. A. y Gkinis, G. (2016). Thermal enhancement of solar parabolic trough collectors by using nanofluids and converging-diverging absorber tube. Renewable Energy, 94, 213-222. https://doi.org/10.1016/j.renene.2016.03.062
Cocco, D. y Cau, G. (2015). Energy and economic analysis of concentrating solar power plants based on parabolic trough and linear Fresnel collectors. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 229(6), 677-88. https://doi.org/10.1177/0957650915587433
Demuth, H. B., Beale, M. H. y Hagan, M. T. (2015). Neural Network Toolbox Reference. MathWorks.
Evangelisti, L., De Lieto V., R. y Asdrubali, F. (2019). Latest advances on solar thermal collectors: A comprehensive review. Renewable and Sustainable Energy Reviews, 114, 109318. https://doi.org/10.1016/j.rser.2019.109318
Fadaei, N., Yan, W., Tafarroj, M. M. y Kasaeian, A. (2018). The application of artificial neural networks to predict the performance of solar chimney filled with phase change materials. Energy, Conversion and Management, 171, 1255-1262. https://doi.org/10.1016/j.enconman.2018.06.055
Fathabadi, H. (2020). Novel low-cost parabolic trough solar collector with TPCT heat pipe and solar tracker: Performance and comparing with commercial flat-plate and evacuated tube solar collectors. Solar Energy, 195, 210-222. https://doi.org/10.1016/j.solener.2019.11.057
Jaber, J., Badran, O. O. y Abu-Shikhah, N. (2004). Sustainable energy and environmental impact: Role of renewables as clean and secure source of energy for the 21st century in Jordan. Clean Technologies and Environmental Policy, 6, 174-186. https://doi.org/10.1007/s10098-003-0232-9
Kincaid, N., Mungas, G., Kramer, N., Wagner, M. y Zhu, G. (2018). An optical performance comparison of three concentrating solar power collector designs in linear Fresnel, parabolic trough, and central receiver. Applied Energy, 231, 1109-1121. https://doi.org/10.1016/j.apenergy.2018.09.153
Maurer, C., Cappel, C. y Kuhn, T. E. (2017). Progress in building-integrated solar thermal systems. Solar Energy, 154, 158-186. https://doi.org/10.1016/j.solener.2017.05.065
Reyes-Téllez, E. D., Parrales, A., Ramírez-Ramos, G. E., Hernández, J. A., Urquiza, G., Heredia, M. I., Sierra, F. Z. (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. https://doi.org/10.5004/dwt.2020.26063
Solís-Pérez, J. E., Hernández, J. A., Parrales, A., Gómez-Aguilar, J. F., Huicochea, A. (2022). Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes. Neural Networks, 152, 44-56. https://doi.org/10.1016/j.neunet.2022.04.016
Zarza Moya, E. (2017). Innovative working fluids for parabolic trough collectors. En: Manuel J. Blanco, M. J. y L. Ramirez Santigosa (2017), Advances in Concentrating Solar Thermal Research and Technology (pp. 75-106). Woodhead Publishing. Woodhead Publishing Series in Energy. https://doi.org/10.1016/B978-0-08-100516-3.00005-8
Downloads
Published
Issue
Section
License
Copyright (c) 2005 José Alfredo Hernández Pérez, Wassila, Arianna Parrales Bahena, Armando Huicochea, David Juárez-Romero
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Esta revista proporciona acceso abierto inmediato a su contenido, con base en el principio de ofrecer al público un acceso libre a las investigaciones para contribuir a un mayor intercambio global de conocimientos. Se distribuye bajo una licencia Creative Commons Reconocimiento-NoComercial 4.0 Internacional License.
