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research-article

Optimization and estimation of the thermal energy of an absorber with graphite disks by using direct and inverse neural network

[+] Author and Article Information
Aldo Márquez-Nolasco

Posgrado del Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp) de la Universidad Autónoma del Estado de Morelos. Av. Universidad No. 1001, Col Chamilpa, CP. 62209, Cuernavaca, Morelos, México
aldo.marquez@uaem.mx

Roberto Conde-Gutiérrez

Posgrado del Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp) de la Universidad Autónoma del Estado de Morelos. Av. Universidad No. 1001, Col Chamilpa, CP. 62209, Cuernavaca, Morelos, México
roberto.conde@uaem.mx

J. Alfredo Hernandez

Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos (UAEM). Av. Universidad No. 1001, Col Chamilpa, CP. 62209, Cuernavaca, Morelos, México
alfredo@uaem.mx

Armando Huicochea

Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos (UAEM). Av. Universidad No. 1001, Col Chamilpa, CP. 62209, Cuernavaca, Morelos, México
huico_chea@uaem.mx

Javier Siqueiros

Secretaría de Innovación, Ciencia y Tecnología de Morelos, Av. Atlacomulco No. 13, Colonia Acapatzingo, C.P. 62440, Cuernavaca, Morelos, México
javier.siqueiros@morelos.gob.mx

Ociel Rodriguez

Posgrado del Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp) de la Universidad Autónoma del Estado de Morelos. Av. Universidad No. 1001, Col Chamilpa, CP. 62209, Cuernavaca, Morelos, México
ociel.rodriguez@uaem.mx

1Corresponding author.

ASME doi:10.1115/1.4036544 History: Received April 01, 2017; Revised April 11, 2017

Abstract

The most critical component of an absorption heat transformer is the absorber, by the exothermic reaction which is carried out, resulting a useful thermal energy. This article proposed a model based on improving the performance of energy for an absorber with disks of graphite during the exothermic reaction, through of an optimal strategy. Two models of artificial neural networks (ANN) were developed to predict the thermal energy, through two important factors: internal heat in the absorber (QAB) and the temperature of the working solution of the absorber outlet (TAB). Confronting the simulated and real data, a satisfactory agreement was appreciated, obtaining a mean absolute percentage error value of 0.24 % to calculate QAB and of 0.17 % to calculate TAB. Furthermore, from these ANN models, the inverse neural network (ANNi) allowed improves the thermal efficiency of the absorber (QAB and TAB). To achieve find the optimal values was necessary to propose an objective function, where the genetic algorithms were indicated. Finally, by applying the ANNi-GAs model, the optimized network configuration was to find an optimal value of concentrated solution of LiBr-H2O and the vapor inlet temperature to the absorber. The results obtained from the optimization allowed to reach a value of QAB from 1.72 kW to 2.44 kW, when a concentrated solution of LiBr-H2O at 59 % was used and increased the value of TAB from 104.73ºC to 109.2°C, when was used a vapor inlet temperature of 73ºC.

Copyright (c) 2017 by ASME
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