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Research Papers

Optimization and Estimation of the Thermal Energy of an Absorber With Graphite Disks by Using Direct and Inverse Neural Network

[+] Author and Article Information
A. Márquez-Nolasco, O. R. Pérez

Centro de Investigación en Ingeniería y Ciencias
Aplicadas (CIICAp),
Universidad Autónoma del Estado de Morelos,
Avenida Universidad No. 1001,
Col Chamilpa, CP,
Cuernavaca 62209, Morelos, Mexico

R. A. Conde-Gutiérrez

Centro de Investigación en Ingeniería y Ciencias
Aplicadas (CIICAp),
Universidad Autónoma del Estado de Morelos,
Avenida Universidad No. 1001,
Col Chamilpa, CP,
Cuernavaca 62209, Morelos, Mexico
e-mail: roberto.conde@uaem.mx

J. A. Hernández

Centro de Investigación en Ingeniería y Ciencias
Aplicadas (CIICAp),
Universidad Autónoma del Estado
de Morelos (UAEM),
Avenida Universidad No. 1001,
Col Chamilpa, CP,
Cuernavaca 62209, Morelos, Mexico
e-mail: alfredo@uaem.mx

A. Huicochea

Centro de Investigación en Ingeniería y Ciencias
Aplicadas (CIICAp),
Universidad Autónoma del Estado de Morelos (UAEM),
Avenida Universidad No. 1001,
Col Chamilpa, CP,
Cuernavaca 62209, Morelos, Mexico

J. Siqueiros

Secretaría de Innovación,
Ciencia y Tecnología de Morelos,
Avenida Atlacomulco No. 13,
Colonia Acapatzingo, C.P.,
Cuernavaca 62440, Morelos, Mexico

1Corresponding authors.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 1, 2017; final manuscript received April 11, 2017; published online September 28, 2017. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 140(2), 020901 (Sep 28, 2017) (13 pages) Paper No: JERT-17-1146; doi: 10.1115/1.4036544 History: Received April 01, 2017; Revised April 11, 2017

The most critical component of an absorption heat transformer (AHT) is the absorber, by which the exothermic reaction is carried out, resulting in 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 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 (MAPE) 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 find the optimal values, it was necessary to propose an objective function, where the genetic algorithms (GAs) 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 1.77 kW to 2.44 kW, when a concentrated solution of LiBr–H2O at 59% was used and increased the value of TAB from 104.66 °C to 109.2 °C when a vapor inlet temperature of 73 °C was used.

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References

Figures

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Fig. 1

Experimental absorber: (a) internal graphite disks, (b) outer breastplate, (c) graphite disk

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Fig. 2

Cycle of a heat transformer by absorption

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Fig. 3

Recurrent network architecture for QAB values with the procedure used for neural network learning

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Fig. 4

Recurrent network architecture for TAB values with the procedure used for neural network learning

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Fig. 5

Dispersion of data between the actual values with respect to the simulated data to determine the QAB

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Fig. 6

Dispersion of data between the actual values with respect to the simulated data to determine the TAB

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Fig. 7

Behavior of the internal heat in the absorber, in carrying out the contact between the inlet temperature of the concentrated solution and the inlet temperature of the vapor

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Fig. 8

Behavior of the outlet temperature of the absorber, in carrying out the contact between the inlet temperature of the concentrated solution and the inlet temperature of the vapor

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Fig. 9

Important analysis of the input variables on the determined values of (a) QAB and (b) TAB

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Fig. 10

Development of genetic algorithms to find optimal input values at absorber

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Fig. 11

Minimization of the objective function as close to zero

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Fig. 12

(a) Experimental run and (b) application of model ANNi to improve the value of QAB with respect to the concentrated solution

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Fig. 13

(a) Experimental run and (b) application of model ANNi to improve the value of TAB with respect to the vapor inlet temperature

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Fig. 14

Direct application of model ANNi in different operating conditions to increase the value of QAB

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Fig. 15

Direct application of model ANNi in different operating conditions to increase the value of TAB

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