The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector
Por:
Ajbar, Wassila, Parrales, A., Cruz-Jacobo, U., Conde-Gutierrez, R. A., Bassam, A., Jaramillo, O. A., Hernandez, J. A.
Publicada:
5 may 2021
Resumen:
This work focused on presenting a multivariate inverse artificial neural
network (ANNim) by developing two functions coupled to metaheuristic
algorithms to increase a parabolic trough collector (PTC). This work
aims to provide a new method capable of improving the thermal efficiency
of a PTC by determining multiple optimal input variables. At first, two
ANN models carried out to predict the PTC thermal efficiency (eta(t)),
validated, and compared in detail. For that, six input parameters
rim-angle (phi(r)), inlet-temperature (T-in), ambient-temperature
(T-amb), water volumetric flow rate (F-w), direct-solar-radiation (G(b))
and wind-speed (V-v) considered as variables in the input layer. Two
non-linear transfer functions (TANSIG and LOGSIG) in the hidden layer, a
linear function (PURELIN) in the output layer, and the
Levenberg-Marquardt training algorithm were applied. The results showed
that both ANN models achieved satisfactory results with a coefficient of
determination of 0.9511 and a root mean square error of 0.0193. Then, to
get the variable's optimal values: rim-angle, inlet-temperature, and
water volumetric flow rate, both ANN models inverted to acquire the
multivariable objective function that could be resolved with
genetic-algorithms (GA) and particle-swarm-optimization (PSO). The
TANSIG function demonstrated better adaptation to the ANNim model by
finding all the input variables in a random test with an error of 3.96%
with a computational time of 14.39 s applying PSO. The results showed
that by using the ANNim methodology, it is feasible to improve the
performance of the PTC by optimizing from one, two, and three variables
at the same time. In optimizing one variable at a time, it was possible
to increase a random test's performance up to 54.78%, 27.62%, and
51.92% by finding the rim-angle inlet-temperature and water volumetric
flow rate, respectively. In optimizing two variables simultaneously, it
was possible to increase a random test's performance up to 36.73% by
finding the appropriate inlet-temperature and water volumetric flow
rate. In optimizing three variables simultaneously, it was possible to
increase a random experimental test of up to 67.12%. Finally, the new
ANNim method proposed may increase the thermal efficiency of a PTC in
real-time because of the coupling of metaheuristic algorithms that allow
obtaining optimal variables in the shortest possible time. Therefore, it
can be a promising and widely used method for optimizing and controlling
thermal processes.
Filiaciones:
Ajbar, Wassila:
Posgrado en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 60209, Mexico
Univ Autonoma Estado Morelos UAEM, Posgrad Ingn & Ciencias Aplicadas CIICAp, Av Univ 1001, Cuernavaca 60209, Morelos, Mexico
Parrales, A.:
CONACyT - Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Av. Universidad 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 62209, Mexico
Univ Autonoma Estado Morelos, CONACyT Ctr Invest Ingn & Ciencias Aplicadas CIIC, Av Univ 1001, Cuernavaca 62209, Morelos, Mexico
Cruz-Jacobo, U.:
Posgrado en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 60209, Mexico
Univ Autonoma Estado Morelos UAEM, Posgrad Ingn & Ciencias Aplicadas CIICAp, Av Univ 1001, Cuernavaca 60209, Morelos, Mexico
Conde-Gutierrez, R. A.:
Centro de Investigación en Recursos Energéticos y Sustentables, Universidad Veracruzana, Av. Universidad Km 7.5, Col. Santa Isabel, Coatzacoalcos, Veracruz C.P. 96535, Mexico
Univ Veracruzana, Ctr Invest Recursos Energet & Sustentables, Av Univ Km 7-5, Coatzacoalcos 96535, Veracruz, Mexico
Bassam, A.:
Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes S/N, Periférico Norte Apartado Postal 150 Cordemex, Mérida, Yucatán C.P. 97310, Mexico
Univ Autonoma Yucatan, Fac Ingn, Av Ind Contaminantes S-N, Merida 97310, Yucatan, Mexico
Jaramillo, O. A.:
Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Privada Xochicalco S/N, Col. Azteca, Temixco, Morelos C.P. 62580, Mexico
Univ Nacl Autonoma Mexico, Inst Energias Renovables, Privada Xochicalco S-N, Temixco 62580, Morelos, Mexico
Hernandez, J. A.:
Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp-IICBA), Universidad Autónoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 60209, Mexico
Univ Autonoma Estado Morelos UAEM, Ctr Invest Ingn & Ciencias Aplicadas CIICAp IICBA, Av Univ 1001, Cuernavaca 60209, Morelos, Mexico
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